DKW basal salts improve micropropagation and callogenesis compared with MS basal salts in multiple commercial cultivars of <i>Cannabis sativa</i>
Bibliographic record
Abstract
Existing Cannabis sativa micropropagation protocols use a limited number of cultivars and the results are often not reproducible. Currently, Murashige and Skoog basal salt mixture (MS) + 0.5 μmol/L thidiazuron (TDZ) has been reported as the optimal medium for nodal micropropagation, yet our preliminary studies with this medium have resulted in abnormal morphology and high mortality rates in multiple cultivars. Following an initial screen of basal salt mixtures [MS, B5, BABI, and Driver–Kuniyaki–Walnut (DKW)], we determined that DKW produced the healthiest plants. In a second experiment, the multiplication rate and canopy area of explants grown on MS + 0.5 μmol/L TDZ and DKW + 0.5 μmol/L TDZ were compared using five drug-type cultivars. The combined multiplication average of explants grown on DKW + 0.5 μmol/L TDZ was 1.5× higher than explants grown on MS + 0.5 μmol/L TDZ. Similarly, the combined average of the canopy area was twice as large on DKW + 0.5 μmol/L TDZ. In the third experiment, callogenesis was compared using a concentration range of 2,4-dichlorophenoxyacetic acid (0–30 μmol/L) on both MS and DKW, and similarly, callus growth was superior on DKW. This study presents the largest comparison of basal salt compositions on the micropropagation of five commercially grown Cannabis cultivars to date.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".