Stochastic Drought Quantification for the South Canadian Prairie
Bibliographic record
Abstract
For formulating robust adaptation policies under nonstationary climate, drought processes need to be characterised and modelled adequately. As a case study, the major drought episode in the early 2000s in the South Canadian Prairie is investigated using a stochastic approach driven by Global Circulation Model (GCMs) outputs, large scale climate oscillation indices and reanalysis data. The meteorological drought conditions are characterised by the Drought Severity Index (DSI). Results show that interannual drought variability cannot be modelled simply by autocorrelated structures or seasonal cycles of the precipitation series. The US National Centers for Environmental Prediction (NCEP) reanalysis data and Pacific Decadal Oscillation (PDO) Index provide interannual signals which are useful for the proposed stochastic approach to simulate realistic severe drought events. Although GCM outputs such as the Canadian Centre for Climate Modelling and Analysis (CCCma) can in principle also be used in the proposed framework to generate drought series, the simulated and historical series are less well matched. These results imply that current GCM outputs have limited information with respect to interannual signals. Finally, the possibility to extend the proposed stochastic approach to support risk-based water management is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".