Effect of Cultivar and Explants Type on Tissue Culture Regeneration of Three Nigerian Cultivars of Tomatoes
Bibliographic record
Abstract
In order to assess the suitable explant(s) for in-vitro regeneration of three local cultivars of Nigerian tomatoes, Ibadan local (IbL), Ife and JM94/46, cotyledon, hypocotyls and radicle explants were cultured in shoot regeneration medium consisting of MS containing 30 g L-1 sucrose and 8 g L-1 agar with no exogenous plant growth hormones. Forty-five of each explant type was cultured on the medium in triplicate experiments and results showed varied percentage survival and shooting for the various explants. Hypocotyl explants had the highest percentage of shooting explants at 13.3% for IbL; 6.67% for Ife and 20% in JM94/46. IbL cotyledon explants had 4.44% of shooting explants with no shoots recorded in Ife and JM94/46 cotyledon explants. IbL radicle explants had 2.22% shooting explants and no shoots recorded in Ife and JM94/46. Student Neuman Keuls (SNK) statistical analysis of cultivar-media interaction showed there was no significant difference (P > 0.05) among the three cultivars in number of calli and shooting calli. There was however significant difference among the cultivars in the number of shoots recorded. SNK values for explants-media interaction showed that cotyledon and radicle explants were significantly different (P < 0.05) from hypocotyl explants in the number of shoots produced.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".