Overwinter Low‐Temperature Responses of Cereals: Analyses and Simulation
Bibliographic record
Abstract
ABSTRACT Winter survival of cereals is dependent on complex, environmentally induced responses that affect just about every measurable morphological, physiological, and biochemical characteristic of the plant. Simulation models offer a valuable means for the integration of knowledge accumulated from detailed physiological, agronomic, genetic, and genomics studies, thereby improving our understanding of complicated plant responses. A well‐designed model also provides an effective extension and teaching tool and the opportunity for systematic investigation of production risks, cause‐and‐effect processes, genetic theories, and adjustments needed to mitigate the possible effects of climate change. Earlier, we developed and deployed a Winter Cereal Survival Model based on a series of equations that described acclimation, vernalization, dehardening, and damage due to low temperature (LT) stress. A modular design has permitted modification and allowed for interfacing with other simulation models. Recent advances in our understanding of this agronomically important character have provided us with the opportunity to develop a more robust winter survival simulation model with a wider geographic application that now also considers cultivar acclimation threshold induction temperature (Ti), respiration stress, photoperiod, and other developmental factors. The model has been field validated and provides the opportunity for the simulation of a wide range of species and overwinter environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".