Bibliographic record
Abstract
Abstract Cannabis can elicit various reactions in different consumers. In order to shed light on the mechanisms underlying the human-cannabis relationship, we begin to investigate the genetic basis of this differential response. The web-based platform OpenSNP was used to collect selfreported genetic and phenotypic data. Participants either reported a positively or negative affinity to cannabis. A total of 26 individuals were retained, 10 of which indicated several negative responses and the remaining 16 indicating strong affinity for Cannabis. A total of 325’895 single nucleotide polymorphisms (SNPs) were retained. The software TASSEL 5 was used to run a genome-wide association study (GWAS), with a generalized liner model (GLM) and1000 permutations. The analysis yielded a set of 45 SNPs that were significantly associated with the reported affinity to cannabis, including one strong outlier found in the MYO16 gene. A diagnostic process is proposed by which individuals can be assessed for their affinity to cannabis. We believe this type of tool may be helpful in alleviating some of the stigma associated with cannabis use in individuals sensitive to THC and other cannabis constituents such as myrcene, which may potentiate negative responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".