Screening of African Yam Bean Accessions for Imbibition and Seed Physiological Quality
Bibliographic record
Abstract
African yam bean is a nutritionally-important but neglected food crop with several health benefits. But its large scale cultivation and consumption are still limited by lack of systematic genetic improvement and breeding programme for the crop. Preliminary studies have established that the inherent field establishment potential of African yam bean is low. Hence the need to screen available accessions to identify those with outstanding seed physiological qualities for selection as parental materials for further improvement. Thirty-four African yam bean accessions were collected from different farmers in five states of Southeastern Nigeria. Seed physiological quality (viability and vigour) was assessed using hundred seed weight, water imbibition, standard germination, accelerated ageing and conductivity tests. The mean squares due to varietal effect were significant (p < 0.05) for germination index and highly significant (p < 0.01) for all other traits. Accessions ENAGag, ENAGmg 1 and, ENAGmg 2 had high standard (> 80%) and accelerated ageing (> 70%) germination percentages. Only 11 of the accessions had an accelerated ageing germination percentage of above 50%. All but one of the 34 accessions had conductivity values less than 5.00 µScm-1 g-1. On the average, African yam bean seeds require up to 76.9% moisture of their initial weight over a period of approximately 53 hours prior for the commencement of germination process. Accessions ENAGag, ENAGmg 1, ENAGmg 2, ENNKob, ENNSog, IMISis, IMOKeo 1 and IMOKeo 3 were outstanding and are therefore potential candidates for genetic improvement of seed quality in African yam bean.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".