Growth and physiological trait variation among corn hybrids for cold tolerance
Bibliographic record
Abstract
Global food demand has risen continuously because of increasing population with greater food and energy needs. Corn production, however, is constrained by current and possible increased future variability in the weather. Earlier planting is a strategy for U.S. Mid-South corn producers to avoid typical summer droughts. However, planting early will increase the likelihood of seedlings exposure to cold temperatures. The objectives of this study were to evaluate corn hybrids planted when the conditions are desirable followed by low and moderately low temperatures to assess the variability among the vegetative and physiological parameters and to classify hybrids into different cold tolerant groups. Twenty one commercially-grown hybrids were subjected to three day/night temperature treatments; 29/21 °C (optimum), 25/17 °C (moderately low), and 21/13 °C (low) from 15 d after planting (DAP) for plants grown at optimum temperature. Shoot, root, and physiological parameters were measured, 32–34 DAP. Significant differences and interactions were observed among the temperature treatments and hybrids for most of the traits measured. Based on relative scores, developed in this study, AR1262 and P1636YHR were classified as cold tolerant and H68B and ST11504VT3 as cold sensitive. Cold tolerant hybrids and their associated morpho-physiological characteristics may be useful for breeders to develop new hybrids that could withstand low and variable temperatures during seedling growth and developmental period.
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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 | 0.001 | 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".