Multi-annual prediction of drought and heat stress to support decision making in the wheat sector
Bibliographic record
Abstract
Unfavourable and extreme climate events such as drought and heat stress heavily impact the agriculture sector and food security globally, and the impact of these climate hazards is expected to increase over the upcoming years due to anthropogenic climate change. Decadal climate predictions have been made available to stakeholders in the agriculture sector as a potential source of near-term climate information that provides forecasts for the following 10 years, thus providing an important source of information for increasing preparedness and for adaptation. In this study, the ability of such forecasts to predict climate extremes on a multi-annual timescale is explored. In particular, the skill and reliability of decadal probability forecasts to estimate user-relevant agro-climatic indices, such as the Standardized Precipitation Evapotranspiration Index (SPEI), for the months preceding the wheat harvest on a global spatial scale, will be presented. Following this, the added value of such climate information with respect to using past observed climatology or standard (uninitialized) climate projections will be shown. The applicability of decadal forecasts to enhance the adaptation and mitigation activities in the agricultural sector will be illustrated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".