Variability and assessment of interrelationships among yield and yield‐related characters of pea accessions under the influence of high temperature
Bibliographic record
Abstract
ABSTRACT Understanding the response and variability of pea accessions traits to high temperatures is an excellent strategy for breeding pea for heat tolerance. This research aimed to determine character variability and interrelationships among yield and yield‐related traits as responses to pea grown under daily high temperature. Ninety‐four pea accessions were grown under greenhouse conditions in Malaysia and Indonesia in the 2020–2021 season. During flowering up to the physiological maturity stage, the average daily maximum temperature was 31.6°C–32.4°C in Malaysia and 26.5°C–27.6°C in Indonesia. Heat stress reduced grain yield in Malaysia by 40% when the daily maximum temperature was above 32°C compared to yield in Indonesia. The number of seeds per pod, number of seeds per plant, filled pods per plant and number of ovules per pod were positively correlated with grain yield in Malaysia and Indonesia, respectively. A negative correlation was observed for percent aborted flower and aborted seed with grain yield. The genotype by trait biplot identified accessions B32, A19, G76, A11 and D44 as potentially promising under high‐temperature conditions with high yield supported by plant height.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".