Multi-annual prediction of drought and heat stress to support decision making in the wheat sector
Bibliographic record
Abstract
Abstract Drought and heat stress affect global wheat production and food security. Since these climate hazards are expected to increase in frequency and intensity due to anthropogenic climate change, there is a growing need for effective planning and adaptive actions at all timescales relevant to the stakeholders and users in this sector. This work aims at assessing the forecast quality in predicting the evolution of drought and heat stress by using user-relevant agro-climatic indices such as Standardized Precipitation Evapotranspiration Index (SPEI) and Heat Magnitude Day Index (HMDI) on a multi-annual timescale, as this time horizon coincides with the long-term strategic planning of stakeholders in the wheat sector. We present the probabilistic skill and reliability of initialized decadal forecast to predict these indices for the months preceding the wheat harvest on a global spatial scale. The results reveal the usefulness of the study in a climate services context while showing that decadal climate forecasts are skillful and reliable over several wheat harvesting regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".