An effective protocol for improved regeneration capacity of Kabuli chickpeas
Bibliographic record
Abstract
Yadav, I. S. and Singh, N. P. 2012. An effective protocol for improved regeneration capacity of Kabuli chickpeas. Can. J. Plant Sci. 92: 1057–1064. An efficient protocol for in vitro regeneration is essential for genetic manipulation and micro-propagation of important plant species. A direct shoot regeneration system has been optimized for Desi chickpeas, but an effective regeneration protocol is still needed for Kabuli chickpeas. An efficient regeneration protocol for Kabuli chickpeas was developed, using whole embryonic axes, an embryonic axes slice and cotyledonary node explants from two genotypes L550 and JGK-1. Depending upon chickpea genotype, type of explant and culture medium, percentage of shoot producing explants (frequency) and the number of shoots per explant (efficiency) varied from 10 to 83% and from 1 to 58, respectively. The shoot regeneration capacity (SRC=frequency×efficiency), which is an indicator of the effectiveness of the protocol, varied from 47 to 2508 shoots per 100 explants cultured. On average, SRC of L550 was 1.8 times higher than JGK-1. Murashige and Skoog's (MS) medium+B5 vitamins supplemented with 8.0 µM benzyl amino purine (BAP)+0.5 µM α- naphthalene acetic acid (NAA) and 0.1 M sucrose plus embryonic axes was found to be the most effective culture medium and type of explants, respectively. Half strength MS medium+2% sucrose supplemented with 4 µM NAA, 3µ M IAA or 4µM IAA produced a high rooting percentage in both chickpea genotypes. The regeneration process starting from explant preparation to establishment of a complete plant in soil took 105–110 d. This optimized regeneration method holds promise for facilitating the insertion of interested genes through genetic transformation for improvement of Kabuli chickpeas.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".