Evaluating a heat-tolerant wheat germplasm in a heat stress environment
Bibliographic record
Abstract
Abstract Heat stress, a regular risk to wheat in the subtropics, is a growing threat in other wheat producing regions as the global temperature rises. This paper reports on three experiments evaluating 49 entries of the 13th High Temperature Wheat Yield Trial (13HTWYT) from the International Centre for Maize and Wheat Improvement (distributed in 2014), with Fang 60 as the local check, at two locations at Chiang Mai, Thailand, a designated representative of the wheat mega-environment 5, in which temperature for the coolest month averages >17.5 °C and the crop is subjected to high temperature for the entire growing season. The wheat was grown in the lowland (elevation 330 m) at Chiang Mai University in (i) sand culture to simulate the condition of non-limiting nutrient and water supply and (ii) in the field and (iii) as an on-farm trial in the highlands (elevation 800 m) at Mae Wang district of Chiang Mai province. Heat tolerance in the wheat germplasm, recently developed for adaptation to high temperature, was indicated by longer pre-heading duration, and the positive correlation between days to heading and grain yield all three experiments. The longer time before heading enabled development of larger spikes that produced more seeds from more and larger spikelets and more competent florets. However, with the number of spikes that was either lower than or similar to Fang 60, none of the recently developed 13HTWYT entries out-yielded the local check from the 1970s.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".