A field method for screening maize cold to lerance
Bibliographic record
Abstract
Screening of cold-tolerant genotypes is hampered by the lack of a reliable field-evaluation method. A study was conducted to develop a field screening method for cold tolerance by recreating seedbed conditions limiting seed growth and quantifying the effect of those limiting parameters on the germination and emergence of two maize hybrids. By comparing maize germination and emergence in uncovered control plots with those covered with reflective insulation, differences were monitored in soil temperature, soil water content, oxygen and air content. Maize germination and emergence were monitored, and rate of radicle and coleoptile growth was measured. Soil temperature was significantly reduced by covering with reflective insulation. The average soil temperature at 5 cm depth was 5.5 and 7.4°C in late April, 11.0 and 16.0°C in May and 14.0 and 16.6°C in early June for the covered and uncovered treatments, respectively. In addition, day-to-day fluctuation in maximum and minimum temperatures was much smaller under covered than uncovered conditions. Although air content was maintained (about 34% on average), seedbed oxygen content dropped tremendously three times during the growing period. Reflective insulation cover caused a 5-d delay in germination and emergence for both hybrids. On average, across hybrids, the daily growth rate of radicle and coleoptile was 50% and 22% higher in uncovered than in covered treatments. Reflective insulation cover created adverse growth conditions and reduced seed germination, which suggests that it could be used to create uniform cold seedbed conditions for screening maize cold tolerant lines under field conditions. Key words: Soil temperature, soil water content, germination, emergence, Zea mays L.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".