Learning to balance wheat G × E × M interactions in response to a changing climate—The case for ultra‐early planting systems
Bibliographic record
Abstract
Abstract Reported global reductions in cereal grain yields due to increased global average temperature combined with increasing global populations peaking near 2050 create an immediate need to increase cereal grain yield potential and reduce the yield gap between realized on‐farm grain yield and potential yield. The development of an ultra‐early planting system for spring wheat (Triticum aestivum L.) on the northern Great Plains can increase the resiliency of current growing systems to a changing climate. This was achieved through the development of a unique set of practices designed to successfully shift current wheat production systems to ultra‐early growing systems. Ultra‐early‐planted wheat‐growing systems on the northern Great Plains will provide immediate benefits to the adopting producer in the form of increased grain yield and increased grain yield stability relative to current practices. As global average temperatures warm, and atmospheric CO2 concentrations increase, the northern Great Plains region is in a unique position to potentially realize grain yield increases rather than temperature‐driven grain yield decreases. Shifting planting earlier and taking advantage of increased growing degree day accumulation and water use efficiencies while avoiding higher temperatures during sensitive physiological periods are tactics implemented in ultra‐early growing systems that will increase in importance and relevance in the next three decades as average daily temperatures increase.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".