Genetic Gains in Durum Wheat ( <scp> <i>Triticum turgidum</i> ssp. <i>durum</i> </scp> ) Across the Globe: Yield, Quality and Adapting for Variable Weather Patterns
Bibliographic record
Abstract
ABSTRACT Durum wheat ( Triticum turgidum ssp. durum [Desf.] Husnot) is cultivated globally and used to produce pasta, couscous, bulgur and other semolina products. With the growing world population and increasing food demand, it is pertinent to understand past trends in global food production to shape future endeavours. This review briefly gives an overview of more than a century of durum wheat breeding efforts across the globe, focusing on past genetic gains for not only yield but additional traits, such as biotic and abiotic stress tolerance, grain quality, biomass and nutrient uptake. Historical genetic gains have varied across the globe between breeding programmes, with a short history summarised for countries in the Mediterranean Basin, North and South America and Australia and the international programmes led by CIMMYT and ICARDA. As Early Career Researchers of the Expert Working Group on Durum Wheat Genomics and Breeding of the Wheat Initiative, we understand the importance of highlighting past progress in durum wheat yield to further increase genetic gains and give insight into future research and breeding needs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".