Chemical Profiling of Medical Cannabis Extracts
Bibliographic record
Abstract
Medical cannabis has been legally available for patients in a number of countries. Licensed producers produce a variety of cannabis strains with different concentrations of phytocannabinoids. Phytocannabinoids in medical cannabis are decarboxylated when subjected to heating for consumption by the patients or when extracted for preparing cannabis derivative products. There is little understanding of the true chemical composition of cannabis extracts, changes occurring during heating of the extracts, and their relevance to pharmacological effects. We investigated the extract from a popular commercial strain of medical cannabis, prior to and after decarboxylation, to understand the chemical profiles. A total of up to 62 compounds could be identified simultaneously in the extract derived from commercial cannabis, including up to 23 phytocannabinoids. Upon heating, several chemical changes take place, including the loss of carboxylic group from the acidic phytocannabinoids. This investigation attempts to reveal the chemical complexity of commercial medical cannabis extracts and the differences in the chemical composition of the native extract and the one subjected to heat. Comprehensive chemical analyses of medical cannabis extracts are needed for standardization, consistency, and, more importantly, an informed employment of this substance for therapeutic purposes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".