Recalcitrance of Cannabis sativa to de novo regeneration; a multi-genotype replication study
Bibliographic record
Abstract
Cannabis sativa is relatively recalcitrant to de novo regeneration, but several studies have reported shoot organogenesis or somatic embryogenesis from non-meristematic tissues. Most report infrequent regeneration rates from these tissues, but a landmark publication from 2010 achieved regeneration from leaf explants with a 96% response rate, producing an average of 12.3 shoots per explant in a single drug-type accession. Despite the importance regeneration plays in plant biotechnology and the renewed interest in this crop the aforementioned protocol has not been used in subsequent papers in the decade since it was published, raising concerns over its reproducibility. Here we attempted to replicate this important Cannabis regeneration study and expand the original scope of the study by testing it across 10 drug-type C. sativa genotypes to assess genotypic variation. In our study, callus was induced in all 10 genotypes but callus growth and appearance substantially differed among cultivars, with the most responsive genotype producing 6-fold more callus than the least responsive. The shoot induction medium failed to induce shoot organogenesis in any of the 10 cultivars tested, instead resulting in necrosis of the calli. The findings of this replication study raise concerns about the replicability of existing methods. However, some details of the protocol could not be replicated due to missing details in the original paper and regulatory issues, which could have impacted the outcome. These results highlight the importance of using multiple genotypes in such studies and providing detailed methods to facilitate replication.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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, 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".