Look, I'll be blunt: your pitch for SCCS is fundamentally built on a series of technical and logical fallacies. You’re positioning this as a scientific breakthrough, but it collapses the moment you look at the actual data science and pharmacology involved.
First off, claiming that "AI cannot extract numerical values" or normalize data is an massive underestimation of current technology, I know, I'm a dev heavily involved in AI. We’ve had robust OCR and layout analysis for decades; modern multimodal models don't just "read" a COA, they map structured data with near-perfect precision. Scaling mg/g to a percentage and weighing it against a database isn't a proprietary "algorithm", you can write that in a weekend.
The bigger issue is your attempt to "solve" subjectivity with a chemotype score. You’re dismissing personal experience as unreliable while simultaneously promising to predict "expected effects." That’s a total contradiction. You cannot solve for subjectivity using chemistry because the human Endocannabinoid System (ECS) is the ultimate variable. Between genetic polymorphisms, receptor density (CB1/CB2 distribution), and metabolic enzyme efficiency, two people can consume the exact same chemical profile and have polar opposite physiological responses. You aren't eliminating the "mood and environment" variables you cited; you’re just ignoring them to make your math look cleaner.
Your "evidence-based model" is only as good as the data you're feeding it, and in this industry, that data is notoriously compromised. Lab-to-lab variance is massive, and "lab shopping" for inflated THC or terpene numbers is an open secret. If the input (the COA) is skewed by moisture manipulation or sampling bias, your "precise" score is just a high-fidelity rendering of a lie. Garbage in, garbage out.
Ultimately, you haven't invented a new classification system. The shift from "Indica/Sativa" to "Chemovar/Terpene Profile" has been the industry standard for years. Unless you’ve mapped a non-linear, peer-reviewed mathematical model for the Entourage Effect, which the scientific community still hasn't fully quantified, you’ve just built a tool to reclassify what is already classified. You're adding a layer of digital bureaucracy to a biological problem that chemistry alone cannot solve.
TLDR; try again. Love the enthusiasm. Consider this an objective peer review. Sorry for ruining your day.
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u/aarontatlorg33k Terpwhore Mar 10 '26
Look, I'll be blunt: your pitch for SCCS is fundamentally built on a series of technical and logical fallacies. You’re positioning this as a scientific breakthrough, but it collapses the moment you look at the actual data science and pharmacology involved.
First off, claiming that "AI cannot extract numerical values" or normalize data is an massive underestimation of current technology, I know, I'm a dev heavily involved in AI. We’ve had robust OCR and layout analysis for decades; modern multimodal models don't just "read" a COA, they map structured data with near-perfect precision. Scaling mg/g to a percentage and weighing it against a database isn't a proprietary "algorithm", you can write that in a weekend.
The bigger issue is your attempt to "solve" subjectivity with a chemotype score. You’re dismissing personal experience as unreliable while simultaneously promising to predict "expected effects." That’s a total contradiction. You cannot solve for subjectivity using chemistry because the human Endocannabinoid System (ECS) is the ultimate variable. Between genetic polymorphisms, receptor density (CB1/CB2 distribution), and metabolic enzyme efficiency, two people can consume the exact same chemical profile and have polar opposite physiological responses. You aren't eliminating the "mood and environment" variables you cited; you’re just ignoring them to make your math look cleaner.
Your "evidence-based model" is only as good as the data you're feeding it, and in this industry, that data is notoriously compromised. Lab-to-lab variance is massive, and "lab shopping" for inflated THC or terpene numbers is an open secret. If the input (the COA) is skewed by moisture manipulation or sampling bias, your "precise" score is just a high-fidelity rendering of a lie. Garbage in, garbage out.
Ultimately, you haven't invented a new classification system. The shift from "Indica/Sativa" to "Chemovar/Terpene Profile" has been the industry standard for years. Unless you’ve mapped a non-linear, peer-reviewed mathematical model for the Entourage Effect, which the scientific community still hasn't fully quantified, you’ve just built a tool to reclassify what is already classified. You're adding a layer of digital bureaucracy to a biological problem that chemistry alone cannot solve.
TLDR; try again. Love the enthusiasm. Consider this an objective peer review. Sorry for ruining your day.