DKW basal salts improve micropropagation and callogenesis compared to MS basal salts in multiple commercial cultivars of <i>Cannabis sativa</i>
Bibliographic record
Abstract
Abstract Micropropagation of Cannabis sativa is an emerging area for germplasm storage and large-scale production of clean plants. Existing protocols use a limited number of genotypes and are often not reproducible. Previous studies reported MS + 0.5 μM TDZ to be optimal for Cannabis nodal micropropagation, yet our preliminary studies using nodal explants suggested this media may not be optimal. It resulted in excessive callus formation, hyperhydricity, low multiplication rates, and high mortality rates. Following an initial screen of four commonly used basal salt mixtures (MS, B5, BABI, and 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 μM TDZ and DKW + 0.5 μM TDZ were compared using five drug-type cultivars to determine if the preference for DKW was genotype-dependent. Four cultivars had significantly higher multiplication rates on DKW + 0.5 μM TDZ with the combined average being 1.5x higher than explants grown on MS + 0.5 μM TDZ. The canopy area was also significantly larger on DKW + 0.5 μM TDZ for four cultivars with the combined average being twice as large as the explants grown on MS + 0.5 μM TDZ. In the third experiment, callogenesis was compared using a range of 2,4-D concentrations (0-30 μM) 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".