Assessing effects of temperature driven by sowing dates on the grain filling and kernel weight of corn in the North China Plain
Bibliographic record
Abstract
Introduction: The grain filling process is strongly influenced by weather during the reproductive growth stage, but grain filling and yield of corn under various weather conditions resulting from different sowing dates remain uncertain. Methods: A two-year study was conducted at Henan Agricultural Research Farm in 2015 and 2016 to investigate the grain filling dynamics of corn under different temperatures that were modified through four distinct sowing dates: spring sowing in late April, early summer sowing in late May, summer sowing in mid-June, and autumn sowing in early July. Results: The findings indicated that the elevated temperatures in 2016 led to an 8.7% reduction in yield and a 7.4% decrease in 1000-kernel weight compared to the normal temperatures in 2015, attributed to a shortened grain filling period resulting from the higher temperature (31.6°C). Significant differences in yield were observed across the four sowing dates. Findings from this study also highlighted that growing degree-days had a weak direct, but a strong indirect positive effect on thousand kernel weight through the grain filling duration. The average daily temperature had a weak direct negative, but a strong indirect positive effect on kernel weight through mean grain filling rate. Discussion: With the global climate changing, the intensity and frequency of high temperatures are expected to increase. To reduce corn yield loss due to high temperature in North China Plain advancing sowing dates from mid-June to late May is recommended. It is concluded that early sowing could effectively prolong the active grain filling duration and offset the decline in grain filling rate caused by high temperatures. The findings demonstrated advancing sowing dates improved the grain filling and yield of summer corn, thereby mitigating high temperature stress under global warming.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".