Google rolled out continuous scroll in the desktop search results for English queries in the United States on December 5, 2022. Continuous scroll enables users to seamlessly continue to page two and beyond without having to click a “next” button at the bottom of the results. This followed Google rolling out continuous scroll in the mobile results in October of 2021 (again, U.S-only for English queries).

Here is Google’s announcement about the rollout to desktop in early December (with a gif of continuous scroll in action):

Starting today, we’re bringing continuous scrolling to desktop in English in the U.S. so you can continue to see more search results easily. When you reach the bottom of a search results page, you'll now be able to see up to six pages of results. pic.twitter.com/xIuVP24FFm

— Google (@Google) December 5, 2022

After the rollout to desktop, many wondered how continuous scroll would impact the visibility of rankings that were beyond page one. For example, for sites with urls ranking on page two, and maybe even the top of page two, the ability for users to easily scroll to additional pages of search results should lead to more impressions, clicks, and conversions. That’s the idea anyway, and something I set out to analyze.

Before continuous scroll rolled out in the SERPs, ranking on page two and beyond meant your listings probably wouldn’t be seen much. Sure, some people would venture to page two and beyond, but most would stick on page one (and just refine their search if they couldn’t find what they needed after scanning the results). But with continuous scroll, users can easily move to the second page of results without having to click a button. The new results just show up as you approach the bottom of the initial set of results.

Analyzing Continuous Scroll in the U.S. Desktop Search Results:
Right after the rollout of continuous scroll on desktop, I published a post explaining how to analyze the change in impressions, clicks, and click through rate based on users being able to seamlessly view more listings in the search results. My tutorial explains how to use the GSC API and Analytics Edge in Excel to bulk export data from GSC, filter by desktop only from the United States, compare timeframes, and then filter by page two and three results. The resulting worksheets quickly provide the changes across metrics when comparing the timeframe before, and after, continuous scroll rolled out.

After publishing that post, I’ve been eagerly waiting for more data to build in order to dig into the reporting across sites. And that’s exactly what I did for a number of sites across verticals. I knew the sites would have a ton of data to analyze, and across verticals, so it should be easier to see differences based on continuous scroll rolling out in the desktop SERPs in the United States.

Below, I’ll cover the methodology I used, the data I analyzed, some interesting (and scary) findings about GSC data, and the impact, or lack thereof, of continuous scroll rolling out in the desktop search results. Let’s jump in.

Methodology:
First, I selected twelve different sites that have a steady and significant amount of traffic from Google organic. Some of the properties are large-scale sites driving a lot of clicks from Google organic, where others were niche sites driving less traffic (but still a good amount of clicks). I made sure the sites were across verticals, and that those verticals weren’t heavily impacted by the holidays (as much as I could). Also, I made sure to focus on just the desktop results from the United States, since continuous scroll did not roll out internationally yet.

Then I used the process I mapped out in my tutorial for analyzing the change in metrics based on exporting data from GSC in bulk, filtering by desktop only from the U.S., comparing timeframes, and then filtering by page two and three results. You can check my tutorial for how to accomplish this using the GSC API, Excel, and Analytics Edge.

When analyzing the data, I made sure to review queries where the average position was about the same before and after continuous scroll rolled out. For example, I wouldn’t review a query that was ranking 24 before the update and then 16 after. That’s a big difference and could obviously impact the data. I looked for queries where the site ranked at about the same position so I could better analyze if continuous scroll was having an impact on visibility and engagement.

Regarding Google organic search traffic for the twelve sites, I have provided the number of clicks over the past three months for each of the sites I analyzed (just so you have a feel for how much traffic they were driving from Google recently).

The sites ranged from 89M clicks to 1.1M clicks over the past three months and spanned a number of verticals:

• Site 1: 89M clicks
• Site 2: 31M clicks
• Site 3: 25.3M clicks
• Site 4: 8.6M clicks
• Site 5: 7.4M clicks
• Site 6: 4.9m clicks
• Site 7: 3.7M clicks
• Site 8: 3.2M clicks
• Site 9: 3.0M clicks
• Site 10: 2.9M clicks
• Site 11: 2.5M clicks
• Site 12: 1.1M clicks

The GSC Void: Dark, Murky, and Inconclusive
First, the reality of filtered GSC data hit hard after exporting data based on query. Barry Schwartz covered this in July of 2022 after Patrick Stox wrote a post explaining what he was seeing across sites with GSC filtering data. It was eye-opening to see how much data was filtered for some sites…

When I checked at the time across sites, I also saw massive gaps in data when exporting based on query. For example, the total at the top of the Performance report in GSC can be much greater than what you see after exporting the data by query (and then totaling the clicks and impressions). And I mean WAY OFF. For some sites I analyzed at the time, I was only seeing 20% of the total after exporting the data. Yes, that means 80% of the data was filtered.

Yep, the doc was updated on Friday by Google. I've seen some sites with over 80% of queries being filtered. You can check this by using the GSC API. Beware, you might be shocked with what you see… https://t.co/7SQr7hVG6M

— Glenn Gabe (@glenngabe) July 11, 2022

The reason is that Google filters queries based on privacy concerns. In its documentation, which it refined after Patrick’s study, Google explains that it filters “anonymized queries” to protect the privacy of users. And for some sites, it can be a ton of data. It’s worth noting that exporting by page will yield the full results (or close), but exporting by query highlights what I call the GSC void.

Back to continuous scroll data… For the twelve sites I analyzed, some GSC properties only provided 20-30% of the total data reported in the Performance reporting due to filtering. You read that correctly. That means 70-80% was filtered for those sites.

On the flip side, I’ve seen as high as 84% of the data showing (so 16% filtered), but that was the most I could find based on reviewing a number of properties in GSC. Don’t get me wrong, that’s much better than 24%!

When I saw the amount of filtering, I knew I had my work cut out for me with trying to analyze the data. I was hoping there was enough to see changes based on continuous scroll rolling out… One thing was clear, the GSC void was dark and murky.

Dark and Murky: Analyzing the impact to impressions, clicks, and click through rate.
First, “Dark and Murky” isn’t the name of a trendy new drink you can order poolside at a resort. It’s just the first thing I said after going through the data across the sites I analyzed. When I got past the first page of results, the numbers across most of the sites plummet. They drop so much that it’s nearly impossible to draw any conclusions about how continuous scroll is impacting clicks and click through rate from the desktop SERPs.

And from an impressions standpoint, I couldn’t see a consistent trend with the increase in impressions. For some queries, impressions did increase. For others, they dropped. And again, clicks and click through rate were very hard to analyze due to the insanely low numbers beyond page one.

For example, as soon as I checked the spreadsheet for page two results across several of the sites, the number of clicks was inconsequential. That shows you how much data is being filtered, by the way… On page one, some queries are yielding tens of thousands of clicks, or more. Then page two drops to almost nothing? So yes, the GSC void is real and it can severely hamper your analysis.

Here are some screenshots from the spreadsheets for page two of the search results. Get ready to be underwhelmed from a clicks standpoint. :)

But, I mentioned one site that only had 16% of its data filtered (which was the best I came across out the twelve). For that site, you would think I would have enough data to make some conclusions… but not really. Clicks were very low once I analyzed page two and beyond. I could see that impressions increased for a number of queries, but clicks didn’t. And since clicks were so low beyond page one, the difference in click through rate was pointless to review.

Here is a screenshot from the site that was only 14% filtered:

For example, for one query the number of impressions jumped by 3,110, but clicks only increased by 18. Average position went from 21.3 to 19.0, which is close, but that could have meant a jump from page three of the results to two. Clearly this isn’t enough data to draw any conclusions. The impressions increase is one thing, but the clicks were so low that it didn’t mean much. And to be honest, who really cares about an increase in impressions if clicks don’t follow. For most site owners, this isn’t really a branding exercise. They want the clicks and subsequent conversions! :)

Here’s another site where there was a nice increase in impressions for some queries, and definitely an increase in clicks for some of the queries. That said, some of the increases were due to the site ranking much stronger in the latest timeframe. There was an increase in impressions for some queries when the site ranked about the same position, but there’s just not enough click data to draw any serious conclusions…

Key takeaways based on analyzing Continuous Scroll in the desktop SERPs:

• Based on my analysis across sites, there is NOT much data on page two and beyond to analyze… That’s even the case for large-scale sites with a ton of Google organic traffic. That’s based on GSC filtering anonymized queries.
• I could see an increase in impressions for some queries, but I it was hard to draw any conclusions since there were many that dropped when comparing timeframes as well.
• Clicks and click through rate were even harder to analyze. There weren’t many clicks to report overall beyond page one, which made it very tough to draw any conclusions.
• From a GSC data standpoint, I had severely-limited data based on GSC filtering. This has been reported before, and this study underscored how much filtering is going on. For example, some of the exports were only yielding 20-30% of the data reported in GSC in the Performance reporting (when analyzing by query).
• I do recommend going through this process for your own sites using the tutorial I published (if for no other reason than to see the severe filtering going on with GSC data when exporting by query). Note, you should be able to see the full data if exporting by page, but there are many queries that lead to specific pages (which can muddy waters analysis-wise).  

Summary: The GSC Void Limits Analysis of Continuous Scroll in the SERPs
After continuous scroll rolled out in the desktop search results, I was extremely excited to analyze the impact to impressions and clicks based on users scrolling to page two and beyond. Unfortunately, GSC data filtering hampered my efforts big-time. Some sites were only returning 20-30% of the total data based on GSC’s filtering of anonymized queries.

I’ll be sure to update this post if I come across stronger findings based on analyzing continuous scroll across sites. In the meantime, I do recommend going through this process for your own sites. You never know, GSC might not be filtering as much of your data… Good luck.

GG

Updated on 2/1/23: OpenAI’s AI content detection tool (AI Text Classifier) was added to the list. GPTZeroX was also included (an upgrade to GPTZero).
Updated on 1/10/23: GPTZero was added to the list of AI content detection tools (created by a Princeton University senior).
Updated on 12/29/22: Content at Scale’s AI content detection tool was added.
Updated on 12/14/22: Writer’s AI content detection tool has been updated to detect GPT-3, GPT 3.5, and ChatGPT.
Updated on 12/13/22: Originality.ai was added to the list of AI content detection tools.

———-

As I’ve been sharing examples of sites getting pummeled by the Helpful Content Update (HCU) or the October Spam Update, I’ve also been sharing screenshots from tools that detect AI content (since some sites getting hit are using AI to pump out a lot of lower-quality content – among other things they were doing that could get them in trouble). And based on those screenshots, many people have been asking me which tools I’m using.

So, instead of answering that question a million times (seriously, it might be a million), I figured I would write a quick post listing the top tools I have come across. Then I can just quickly point people to this post versus answering the question over and over.

And note, I’m not saying these tools are foolproof. I have just found them to be pretty darn good at detecting lower-quality AI content. And that’s what we should be trying to detect by the way (not all AI content… but just low-quality AI content that could potentially get a site in trouble SEO-wise).

For example, here is high-quality human content run through a tool:

And here is an example of lower-quality AI content run through a tool:

Again, it’s not foolproof, but can give you a quick feel for if AI was used to generate the content. Below, I’ll cover my favorite AI content detectors I’ve come across so far. I’ll also keep adding to this list so feel free to ping me on Twitter if you have a tool that’s great at detecting lower-quality AI content!

Here is a list of tools covered in this post for detecting AI content:

1. Writer’s AI content detector tool.
2. Huggingface GPT-2 Output Detector Demo.
3. Giant Language Model Test Room (GLTR).
4. Originality.ai (AI content and plagiarism detection)
5. Content at Scale’s AI content detection tool.
6. GPTZeroX
7. OpenAI’s AI Text Classifier

1. Writer’s AI content detector tool:
The first tool I’ll cover is from a company that has an AI writing platform (sort of ironic, but does make sense). Also, it seems like the platform is more for assisting writers from what I can see. You can check out their site for more information about the platform. Well, they also have a nifty AI content detector that works very well. You have probably seen my screenshots from the tool several times on Twitter and LinkedIn. :)

Update: 12/14/22 – While I was testing content created via GPT 3.5 and ChatGPT, I noticed that Writer’s detection tool was accurately detecting the content as created by AI. That was a change, since the tool was originally focused on GPT-2, so I quickly reached out to Writer’s CEO for more information. And I was correct! Writer’s AI content detection tool has been updated to detect GPT 3, GPT 3.5, and ChatGPT. So it’s now the second tool on the list that can achieve that.

AI content is progressing, but so are the tools. Below are some examples of using Writer’s AI content detection tool.

Here is Writer’s tool detecting higher-quality human content:

And here is Writer’s tool detecting content created via GPT-3.5 (using davinci-003, which is the latest model as of 12/14/22):

2. Huggingface GPT-2 Output Detector Demo:
If you’re not familiar with Huggingface, it’s one of the top communities and platforms for machine learning. You can check out their site for more information about what they do. Well, they also have a helpful AI content detector tool. Just paste some text and see what it returns. I have found it to be pretty good for detecting lower-quality AI content. 

For example, here is Huggingface’s tool detecting higher quality human content:

And here is Huggingface’s tool detecting lower-quality AI content:

3. Giant Language Model Test Room (GLTR.io)
The third tool I’ll cover was actually down recently, but I had heard good things about it from several people (when it was working). It ends up there was a server issue and the tool was hanging. Well, the GLTR is back online now and I’ve been testing it to see how well it detects AI content.

The tool was developed by Hendrik Strobelt, Sebastian Gerhmann, and Alexander Rush from the MIT-IBM Watson AI Lab and Harvard NLP. It’s definitely not as intuitive as the first tools I covered, but once you get the hang of it, it can definitely be helpful.

How it works:
You can paste text into the tool and view a visual representation of the analysis, along with several histograms providing statistics about the text. I think most people will focus on the visual representation to get a feel for how likely each word would be the predicted word based on the word to its left. And that can help you identify if a text was written by AI or by a human. Again, nothing is foolproof, but it can be helpful (and I’ve found the tool does work well). To learn more about GLTR and how it works, you can read the detailed introduction on the site.

For example, if a word is highlighted in green, it’s in the top 10 of most likely predicted words based on the word to its left. Yellow highlighting indicates it’s in the top 100 predictions, red in the top 1,000, and the rest would be highlighted in purple (even less unlikely to be predicted).

The fraction of red and purple words (unlikely predictions) increases when the text was written by a human. If you see a lot of green and yellow highlighting, then it can indicate the text contains many predicted words based on the language model (signaling the text could have been written by AI).

Here are two examples. The first shows AI content (many words highlighted in green and yellow). This text was generated via GPT-2.

And here is an example from one of my articles about broad core updates. Notice there are many words highlighted in red, and several purple words as well (signaling this is human-written text).

4. Originality.ai (for detecting GPT 3, GPT 3.5, and ChatGPT)
I was able to test Originality.ai recently and I’ve been extremely impressed with their platform. The CEO emailed me and explained they were one of the few tools to be able to detect GPT-3, GPT 3.5 and ChatGPT (as of December 13, 2022). Needless to say, I was excited to jump in and test out its AI content detection tool. Also, it’s worth noting that the tool can detect plagiarism as well (which is an added benefit). They have also released a Chrome extension and they have an API for handling requests in bulk. I’ll cover more about the Chrome extension below.

So, I fired up OpenAI and selected text-davinci-003 (the latest model as of 12/13/22) and started generating essays, short articles, how-tos, and more. I also used ChatGPT to generate a number of examples I could test.

And when testing those examples in Originality.ai’s detection tool, it picked up the work as AI every time. Again, I was extremely impressed with the solution.

For example, here was a short essay based on GPT 3.5:

And here was a how-to containing several paragraphs and then a bulleted list of steps. I also checked for plagiarism:

It’s not a free tool, so you will need to sign up and pay for credits. That said, it’s been a solid solution based on my testing. Note, they are providing a coupon code (BeOriginal) that gets you 50% off your first 2000 credits. One credit scans 100 words according to the site.

Originality.ai Chrome Extension:
I mentioned earlier that Originality.ai has both a Chrome extension and an API. The Chrome extension enables you to highlight text on a page in Chrome and quickly check to see if it was written by AI. You must log in and use the credits you have purchased, so it’s not free. It works very well based on my testing so far.

For example, here is an article created via Automated Insights. By highlighting the article text, right clicking, and selecting Originality.ai in the menu, you can check to see if the content was created by AI.

5. Content at Scale
Next up is an AI content detector tool from Content at Scale. Like Writer, they provide a platform for AI content generation that uses an interesting approach. You can read more about the platform on their site. But, like Writer, they also have an AI content detection tool. You can include up to 2,500 characters and the tool will analyze the text and determine if it’s AI content or human content. And like Originality.ai and Writer, it can detect GPT-3, 3.5, and ChatGPT.

For example, here is the tool detecting AI content generated by ChatGPT (a short essay):

And here is the tool detecting content from one of Barry’s blog posts as human:

6: GPTZeroX
Next up is a new AI content detection tool created by a Princeton University student! And it’s causing quite the buzz. I’ve read a number of articles across major publications about Edward Tian and his tool called GPTZero, which works to detect if content was written by ChatGPT.

{Update: 2/1} GPTZeroX was just released and can highlight which parts of the text being tested is AI-generated. It’s more granular with its detection, which was a top feature that Edward Tian heard from educators.

Beyond that, Edward explains that “GPTZeroX also supports larger text inputs, multiple .txt, word, and pdf file uploading, and lightning-fast processing speeds.” There is also an API now that can handle high-volume requests.

Here is GPTZeroX detecting a part of text as AI-generated:

With GPTZero, Edward’s approach is interesting, since it uses “perplexity” and “burstiness” in writing to detect if a human or AI wrote the content. “Perplexity” aims to measure the complexity of the content being tested, or what Edward explains as the “randomness of text”. And “burstiness” aims to measure the uniformity of the sentences being tested. For example, Edward explains that “human written language exhibits non-common items appearing in random clusters.” Humans tend to write with more burstiness, while AI tends to be more consistent and uniform.

I’ve been testing the tool over the past few days, and it has worked well (and has been pretty accurate). The site has definitely had some growing pains since launching, since I’m sure Edward didn’t think the tool would become so popular that quickly, but site performance has improved greatly recently. Also, the homepage now explains he is creating a “tailored solution for educators”. I’m eager to hear more about that, but for now, you can add GPTZero as yet another tool in your AI detection arsenal. I think you’ll like it.

For example, here is the tool measuring “perplexity” and “burstiness” of content (based on an essay written by ChatGPT):

And here is the final result accurately detecting AI content written by ChatGPT:

7. OpenAI’s AI Text Classifier
Well, this was an interesting development! OpenAI, the creator of ChatGPT, just released its own AI content detection tool. And as you would guess, it can detect when a piece of content was written by ChatGPT (like several other tools in my post). Based on my testing, it works well (when taking direct output from ChatGPT and testing it). Like other AI content detection tools, it’s not foolproof, but does seem to catch a number of examples of AI content that I tested.

Note, it requires a minimum of 1,000 characters of input and provides one of five responses:

• Very unlikely to be AI-generated.
• Unlikely to be AI-generated.
• Unclear if it is AI written.
• Possibly AI-generated.
• Likely AI-generated.

Here is a quick example based on an essay I created via ChatGPT. As you can see, it’s accurately being detected as AI content:

And here is an example of OpenAI’s tool accurately detecting a blog post of mine as human:

Summary: Although not foolproof, tools can be helpful for detecting AI content.
Again, I’ve received a ton of questions about which tools I’ve been using to detect lower-quality AI content, so I decided to write this quick post versus answering that question over and over. I hope you find these tools helpful in your own projects. And again, if you know of other tools that I should try out, feel free to ping me on Twitter!

GG

If you are confused when Google reports redirects as other categories, like “blocked by robots.txt”, “soft 404s”, “noindexed”, “404s”, and others, it could be Google silently following the redirect and reporting the status of the true destination url instead. My post covers the situation in detail, and provides examples of this happening in the wild.

While heavily analyzing websites from an SEO standpoint, you will undoubtedly find yourself deep in Google Search Console (GSC) reporting. GSC contains a boatload of data directly from Google and can help site owners and SEOs surface key insights. That said, it’s important to understand the nuances involved with GSC reporting, and how Google determines the information it provides in those reports. Having a clear understanding of what the data is showing is important when taking action to improve SEO.

And there’s no better example of GSC data confusion than the dreaded true destination url for redirects in GSC’s index coverage reporting (and URL inspection tool). I have received so many questions about this from clients that I decided to write this post so I can just point people here versus explaining it again and again.

So, join me on a GSC adventure where we uncover the secrets of the true destination url. Some of you might already know this, but I know some do not. And for those that don’t, this will all make sense very soon. You might not be happy with how this is working, but at least you’ll understand why urls are categorized in certain ways in GSC (and via the URL inspection tool).

What is the dreaded true destination url situation in GSC for redirects?
When viewing the indexing status in GSC of urls that are being redirected, Google reports on the true destination url (even if that url is outside of your own site). For example, if you redirect a url to another url, and that url is not indexable for some reason, GSC will silently follow the redirect and report on the final destination’s status. And that can be super confusing for site owners and SEOs that don’t know this is happening.

Yes, that means you can see urls showing up as “blocked by robots.txt”, “noindexed”, “soft 404”, “404”, and more (when the url you are inspecting is actually redirecting). As you can imagine, many site owners are left confused when they see “blocked by robots.txt” when they know 100% that a url is redirecting.

Google’s John Mueller has been asked about this many times, and he has replied with what I explained above (and does admit it can be a bit confusing). Also, Barry wrote a post covering how this happens with the URL inspection tool based on John’s comments. Even though this has been documented, I find it’s still a very confusing situation for many site owners and SEOs (which is why I’m writing this post).

Here is a tweet of mine with a link to John explaining how Google silently follows redirects (and how that shows up in GSC):

Right, that's why I said "reminder". :) John has explained this before in webmaster hangout videos. For example, here is one from 2019 where he explains how Google silently follows redirects for the URL inspection tool (and it's what shows up in Coverage): https://t.co/XG0aGNPOSW

— Glenn Gabe (@glenngabe) January 5, 2021

Now that you know this is happening, you might be wondering what this actually looks like in GSC. I’ll cover that next with examples of this happening in the wild.

Examples of Google silently following redirects and reporting the true destination url status in GSC:
Below, I’ll provide examples with screenshots of Google reporting on the true destination urls versus the redirect. Again, this is when the final destination urls are not indexable for some reason.

Blocked by robots.txt:
The url is redirected outside the site to a url that is blocked by robots.txt. Google reports the redirecting url as being “blocked by robots.txt” since the final destination is actually disallowed.

A twist on blocked by robots.txt:
This url redirects first to a tracking url, which is blocked by robots.txt. The final destination is not blocked, but Google can’t follow the first redirect to find the final destination url since it’s disallowed. It just knows that first url in the chain is blocked and reports that in GSC. Below, you can see the second step shows the url is actually blocked by robots.txt (and that’s what is reported in GSC).

Soft 404:
The url redirects to a page that’s a soft 404 (a product is unavailable). Google reports that the redirecting url is a soft 404 (since the true destination url is being seen as a soft 404).

Here is the page the url redirects to (with the product “currently unavailable”). Hence the soft 404:

Noindexed:
Yep, you guessed it. The url redirects to a page that’s noindexed. Google reports the url that is redirecting as noindexed in the coverage reporting:

Crawled, not indexed:
At first glance, you might think the redirect is being reported as “Crawled, not indexed”. Not true! It’s the final destination url that’s not being indexed by Google. Google is reporting “Crawled, not indexed” for the true destination url.

The final destination url is indeed not indexed:

404:
How can Google see a redirect as a 404? It doesn’t. It’s the true destination url that 404s and that’s what is reported in GSC.

404 with domain name change:
This is just a variation on the 404 situation to explain how this works when changing domain names. The url on the old domain redirects to a url on the new domain name, but the url was never migrated (it 404s). So Google reports that the redirecting url is a 404.

Sorry, more confusion with redirects:
When a url redirects to a page that resolves with a 200 header response code, and is indexed, the URL inspection tool reports accurately about the redirect (and says that initial url is a redirect and not indexed), but Google shows the canonical as the true destination url (where the redirect leads to). Talk about confusing, especially based on everything I explained above with the other examples where the redirecting urls are being reported as something different than a redirect…

A possible solution in GSC to clear up the confusion:
So, how can this be more intuitive? I think if GSC actually provided a message that it’s reporting on the true destination url, it could clear up the confusion for site owners and SEOs. Below, I have mocked up what this can look like in GSC. If Daniel Waisberg is reading (and I hope you are), then please add this!

Summary: Clearing up the confusion with redirects and destination url reporting.
I hope this post helped you understand how Google is silently following redirects and reporting on the true destination urls in GSC. I know it’s a confusing topic for many site owners and SEOs and I’m sure it has led to many head-scratching moments. Just keep in mind that as of now, GSC is reporting on the true destination urls when a url redirects. So don’t be surprised when you notice redirects in other categories in GSC’s coverage reporting (or when using the url inspection tool). And who knows, maybe the GSC product team will implement that message I mocked up above…

GG