High temperatures disturb ovule development in field pea (<i>Pisum sativum</i>)
Bibliographic record
Abstract
High temperatures affect reproductive growth and lead to yield loss in many crops. Field pea is heat sensitive, but little is known about the effect of high temperatures on field pea ovules. We investigated the impact of heat on ovules of flowers at various reproductive nodes of field pea using growth chambers. We exposed 6 cultivars exhibiting diverse heat tolerance to 4 days of heat (35 °C day/18 °C night) during early flowering. Post-treatment ovules and embryo sacs were assessed using clearing by light microscopy and fluorescence microscopy. Results indicated that greater ovule and embryo sac development occurred on some nodes, but poor ovule and embryo sac expansion resulted on other nodes of the same heat-treated plants. Whereas advanced ovule and embryo sac development were identified on heat-tolerant cultivars, a combination of advanced and less advanced ovule and embryo sac development occurred in intermediate and heat-sensitive cultivars. More than 90% of the affected ovules displayed embryos at various stages of development, which indicated disruption around fertilization or shortly thereafter. Callose accumulation around the vascular bundle within ovules suggested disruption of assimilate transport to the embryo sac. The contrasting pattern of ovule development at different nodes implied a conflict between early aging and maternal supply of heat-treated plants.
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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 | 0.000 | 0.000 |
| 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".