Commentary on Borodovsky <i>et al</i>.: Enhancing research on THC quantification—Consumer awareness through accurate labelling
Bibliographic record
Abstract
Recent advances in the quantification of Δ-9-tetrahydrocannabinol (THC) have the potential to increase our understanding of the effects of cannabis use on health.Policies and regulation supporting accurate and accessible THC content labelling will advance scientific progress and give consumers better control over their use.Accurately quantifying use of a drug is vital for assessing its impact on public health.Within the field of cannabis use, quantification has raised major challenges because of the wide range of cannabis products, methods of administration, and distinct policy contexts globally.Recent advances in quantification have focused on THC (Δ-9-tetra-
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.075 | 0.083 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".