Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.2385 Multiple drought indices for agricultural drought risk assessment on the Canadian prairies
Bibliographic record
Abstract
ABSTRACT: A multi-index drought (MID) model was developed to combine the strengths of various drought indices for agricultural drought risk assessment on the Canadian prairies, as related to spring wheat crop yield. The model automatically selects and combines optimum drought indices derived from the preceding and current months as they become available to better match the conditions (both spatially and temporally) where they work well. The cross-validation results showed that (1) the prediction accuracy of the MID model is better than (or occasionally equal to) using any single drought index for all modelling stages, (2) drought indices derived from the recharge period are useful for early drought risk detection, (3) model prediction accuracy improved as the growing season progressed with the most accurate assessments at the beginning of August, and (4) the model performed best in the more arid locations in the southern prairies, which tend to have a more variable precipitation regime. The model assessment results provide the spatial intensity distribution of possible drought progression and recession before and during the growing season, and can be used with complementary information in agricultural drought risk management and mitigation strategies. Copyright 2011 Royal Meteorological Society KEY WORDS agricultural drought; Canadian prairies; drought index; spring wheat yield; risk assessment
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.261 | 0.151 |
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".