Multi-annual prediction of drought and heat stress to support decision making in the wheat sector
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
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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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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