Lions mane 8:1 extract on amazon has an F on fakespot. Why?

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You guys start by claiming that the grade is for the authenticity of the reviews, but then later ask me to contact you to provide evidence of our competitor's misdeeds and manipulation. There is an inherent flaw in this, since by merely grading things you have positioned yourselves as an arbiter of the truth using objective data. However, you can't possibly have enough data to even get close to being that by looking at the metrics you are. What even is the truth in this situation? Does me giving you evidence that our competitors give free product for reviews change the statistical analysis of the review content itself? No, certainly not. Someone wrote those reviews. Real humans wrote them. It's the incentive that is the issue. On top of that, does me giving you evidence that our competitors are running review manipulation programs change anything about the ratings of our products? Also no. Your own claims about the authenticity of reviews, and how you rate them, makes any evidence I give you irrelevant. This is because it doesn't inherently change what the review says, and thus does not statistically change how your model assesses them. Also, there is NO EVIDENCE that there is a statistical difference between an incentivized review and an organic one. I know you would like people to believe there is, as that's the entire basis for your business. However, it doesn't exist. You can prove me wrong if you like. Show me some scientifically objective and verified data proving there is a statistical difference in the content between compensated/incentivized reviews and organic reviews that an AI or algorithm can detect. The problem is that you are working with an imperfect dataset. Any scientist can tell you that your ultimate conclusions are only as good as the data used to form them. Bad data in, faulty conclusions out. There is no way for you to prove which reviews are fake/compensated and which ones are organic using statistical analysis. You could use strategies like I said, and look for evidence of participation in review manipulation programs, but not using statistical analysis of the review content itself. On top of that, you allude to the fact that other reviews are used to judge the legitimacy of a given review. That's an inappropriate use of statistics. It's a very common misuse, but a mistake nonetheless. If 60 out of 100 reviews are fake, that is NOT evidence that one randomly chosen review is fake. The statistics of the review population as a whole only apply to the population as a whole, not to individual reviews that make up that population. You cannot bias the likelihood of a review being fake based on the properties of the population from which it comes, unless it is known that the population is a Dirac function. That doesn't apply in the non-mathematical-physics world, as for the population to be a Dirac function all the values in the population have to be exactly the same. Obviously, the individual reviews are not exactly the same, so you cannot bias the likelihood of an individual review as being fake based on the population as a whole. You can infer things about populations based on smaller samples of data, but you can't infer things about individual pieces of data based solely on the population as a whole. This is an inherent flaw in your system. You are trying to use math and statistical analysis to assess real-world things outside of the appropriate functions of those methodologies. To put it simply, you are trying to assess the validity of review data by looking at flawed metrics. Essentially you are trying to use statistical inference like it is descriptive statistics, but without any of the underlying validation data to prove its conclusions. You have no idea which reviews are real, fake, or compensated. You can't validate any of your model's data, because you don't have access to that data. How can a model be built on closed-off systems without validation data? Furthermore, you are fighting against a lot of money. It's appropriate to say that the people running review manipulation programs are your adversaries, as that's who you are purporting your systems to catch. However, your adversaries are always two steps ahead of you, and actually have the backend data to validate their strategies. So they will always be two steps ahead of you by nature. They have the data and the monetary incentive to beat your systems. You are fighting a losing fight by trying to use statistical inference on a closed-off and flawed dataset. You will never beat your adversaries using this strategy. You have to shift to a more active and less statistical strategy if you ever hope to have your grades be valid. Look at how companies are beating the system, and then grade on that. If companies are found to be participating in review programs, give them all Fs. Is the goal not to try and help consumers weed through BS reviews online? If a company is participating in a review manipulation program, how can you trust any of their reviews? Then you are not rating the reviews. You are rating the company and its actions. I think that's the only valid way to do it.

So in the end, I know this rant won't change anything. You are too far down the rabbit hole to reverse course now, just like Amazon is too far down the rabbit hole to do what they need to do to fix the ranking and review system on there. The moral of the story is that you are rating completely legitimate products F, and completely illegitimate ones A, with a bunch of randomness thrown in to boot. A system that allows that to happen is a flawed system. You don't have the data to prove it one way or another, though. It is easier for you to assume your statistical model is valid than to look under all the rocks that show it is not. Maybe I am wrong. Maybe you do want to change for the better. If that is the case, I implore you to really take a hard look at things. There are ways to show if reviews are being manipulated, but the end result of that will completely upend the entire system. That likely means less affiliate commissions for you, less revenue for Amazon, more bad press, and less short-term consumer confidence in the systems they currently trust. I am not an idiot. I know the end result of all this. Things will keep going like they are, because not rocking the boat makes more money than actually searching for the truth.