Identification of QTLs Associated with Silk Emergence Time Under Heat Stress
Bibliographic record
Abstract
The Silk Emergence Time (SET) is a critical period for the formation of corn ( Zea mays L.) grains. Especially under high-temperature stress conditions, its coordination is of great significance for successful pollination and stable yield. High temperatures often lead to delayed filaments and failed pollination, seriously affecting the final yield. This study, with QTL mapping at its core, systematically analyzed the genetic and physiological mechanisms affecting the silk production period under high-temperature stress, providing theoretical support and genetic resources for the molecular breeding of heat-tolerant corn. Analyze the genetic regulatory mechanism of SET and the role of hormone signaling pathways in the heat hypochondrium response; Evaluate the effects of agronomic factors such as plant height and ASI on SET variations; Precise QTL localization is carried out by using the combined method of genomics and transcriptomics. Screen key candidate genes and conduct functional verification; Compare the differences and stability of QTLS in different thermal ecological zones through regional cases; And explore the practical application paths of QTL in heat-resistant breeding. This study reveals the genetic basis for the regulation of the silk production period of corn under high-temperature stress, providing potential targets for marker-assisted selection (MAS) and genomic selection (GS), and is conducive to the breeding of corn varieties with strong high-temperature adaptability and high yield stability.
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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".