Genome-wide analyses reveal clustering in Cannabis cultivars: the ancient domestication trilogy of a panacea
Bibliographic record
Abstract
In the present research, I used an open access data set (Medicinal Genomics) consisting of nearly 200'000 genome-wide single nucleotide polymorphisms (SNPs) typed in 28 cannabis accessions to shed light on the plant's underlying genetic structure. Genome-wide loadings were used to sequentially cull less informative markers. The process involved reducing the number of SNPs to 100K, 10K, 1K, 100 until I identified a set of 42 highly informative SNPs that I present here. The two first principal components, encompass over 3/4 of the genetic variation present in the dataset (PCA1 = 48.6%, PCA2= 26.3%). This set of diagnostic SNPs is then used to identify clusters into which cannabis accession segregate. I identified three clear and consistent clusters; reflective of the ancient domestication trilogy of the genus Cannabis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".