Phenology and related traits for wheat adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wheat is a major food crop, with around 765 million tonnes produced globally. The largest wheat producers include the European Union, China, India, Russia, United States, Canada, Pakistan, Australia, Ukraine and Argentina. Cultivation of wheat across such diverse global environments with variation in climate, biotic and abiotic stresses, requires cultivars adapted to a range of growing conditions. One intrinsic way that wheat achieves adaptation is through variation in phenology (seasonal timing of the lifecycle) and related traits (e.g., those affecting plant architecture). It is important to understand the genes that underlie this variation, and how they interact with each other, other traits and the growing environment. This review summarises the current understanding of phenology and developmental traits that adapt wheat to different environments. Examples are provided to illustrate how different combinations of alleles can facilitate breeding of wheat varieties with optimal crop performance for different growing regions or farming systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it