Recalcitrance of <i>Cannabis sativa</i> to <i>de novo</i> regeneration; a multi-genotype replication study
Bibliographic record
Abstract
1 Abstract Cannabis sativa is relatively recalcitrant to regeneration from somatic tissues, but several reports have been published demonstrating a response. Most reports show low levels of regeneration from somatic tissues, but a landmark publication by Lata et al . in 2010 reported regeneration from leaf explants with a 96% response rate, producing an average of 12.3 shoots per explant in a single, high-THC genotype. Despite the importance regeneration plays in plant biotechnology this protocol has not been used in subsequent papers in the decade since it was published, raising the concern that it is not reproducible. Many researchers are looking to build research programmes in this growing field, and it is important that the reproducibility and robustness of single-genotype C. sativa regeneration protocols undergo multi-lab validations to ensure they are reproducible across the species. Replication studies in this burgeoning field will help research groups avoid lost time and resources which arise from pursuing protocols that are not reproducible. Here we test the replicability of this protocol across 10 drug-type C. sativa genotypes. This protocol successfully induced callus in all 10 genotypes. Callus size and appearance substantially differed among cultivars, with the most responsive genotype producing 6-fold more callus than the least responsive genotype. However, the most successful shoot induction medium developed in the 2010 paper failed to induce regeneration in any of the cultivars tested, resulting in the eventual necrosis of the calli. Based on this replication study, it is evident that the existing regeneration protocol is not robust and could not be replicated in any of the 10 genotypes tested.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".