Additional file 1 of The biosynthesis of the cannabinoids
Bibliographic record
Abstract
Additional file 1: Table S1. Classification of major cannabinoids based on their chemical nature discovered from C. sativa L. and change in their numbers from 2005 to 2015. Figure S1. The three types of glandular trichomes found on female cannabis flowers. (a) magnified trichomes from the female plant, (b) schematic illustration of stalked trichome showing different regions and storage of cannabinoids, (c-e) cryo-SEM images of the three types of cannabis glandular trichomes, classified as stalked (c), sessile (d), and bulbous (e); scale bars 20 um. Panels c-e are reproduced from Livingston et al. 2020.1 Panel A is reproduced under a creative commons licence. Figure S2. HPLC chromatograms recorded at 220 nm for the conversion of Δ9-THCA to Δ9-THC at different temperatures by Dussy and coworkers. At higher temperatures (160 °C, 180 °C, Δ9-THC is oxidised to form cannabinol, Reproduced from Dussy et al. 2005.6 Figure S3. (a) UHPSFC/PDA (220 nm) chromatogram of a mixture of cannabinoid standards. Peak assignment: (1) CBD, (2) Δ8-THC, (3) THCV, (4) Δ9-THC, (5) CBN, (6) CBG, (7) THCA, (8) CBDA, (9) CBGA (see list of abbreviations at the end of the article for explanation, (b) concentration (mM) of THCA, (c) Δ9-THC (d) CBDA and (e) CBD as function of time and temperature. Figures reproduced from Wang et al. 2016.8 Figure S4. Decarboxylation of 2-hydroxybenzoic acid via a β-keto acid intermediate as proposed by Perrotin-Brunel.9
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.858 | 0.248 |
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".