Maximum lethal temperature for flowering and seed set in maize with contrasting male and female flower sensitivities
Bibliographic record
Abstract
Abstract With a warming climate, heat events occur more frequently especially during flowering of many crops which increasingly threatens food security. The maximum temperature thresholds (TTmax) for different flowering processes and kernel formation, however, are not explicit in maize (Zea mays L.). For this, a temperature‐controlled experiment was conducted including two maize hybrids ZD958 and XY335 that were widely grown in China and six temperature levels (maximum/minimum temperature; 30/20, 32/22, 34/24, 36/26, 38/28 and 40/30℃ for 14 consecutive days bracketing the silking stage). Both tasseling and pollen shedding time were advanced with elevated temperature, but silking time was advanced from 30/20 to 36/26℃ and then delayed with further temperature increase, thus extending anthesis–silking interval (ASI). Silking rate was significantly reduced to 63% at 40/30℃ for ZD958 but maintained at 88%–95% for XY335 compared to that at 30/20℃. Pollen shed weight and pollen viability decreased with elevated temperature with larger reductions in XY335 than in ZD958. Hence, ZD958 and XY335 are female and male flower sensitive hybrids, respectively. Silking rate, pollen shed number and ASI were the most important constraints to kernel formation under HT stress. TTmax for seed set of both hybrids were estimated to be ~38℃, and different flowering processes have respective TTmax. These detailed information are important to uncover heat impacts on maize and increase simulation accuracy when modelling heat effects on maize yield. Besides, more attentions need to be directed at female flower sensitivity when breeding and/or selecting heat‐tolerant maize hybrids.
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 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".