Characterizing the spatial and temporal variability of June–July moisture conditions in the Canadian prairies
Bibliographic record
Abstract
Abstract Palmer's moisture anomaly index (Z‐index) was used to characterize the frequency, severity, and spatial extent of June–July moisture anomalies for 43 crop districts across the Canadian prairies during 1920–99. In addition, the main modes of spatial and temporal variability in moisture conditions were identified and used to elucidate the physical mechanisms responsible for causing these moisture anomalies to occur. The crop districts were divided into five relatively homogeneous moisture regions using cluster analysis, and moisture anomaly statistics were analysed for each region. The single most severe June–July drought on the Canadian prairies occurred in 1961. This drought covered more than 86% of the study region and had a mean severity (Z‐index) of −5.67. Other severe June–July droughts occurred in the Canadian prairies (in order of severity) in 1988, 1936, 1929, and 1937. The results demonstrated that the severity and spatial extent of moisture anomalies on the Canadian prairies are strongly correlated, indicating that the more severe events tend to affect larger areas. The most drought‐prone regions experienced moisture conditions detrimental to crop production in approximately one year out of every six years. The spatial analysis revealed the existence of three preferred spatial patterns of moisture variability on the Canadian prairies and each pattern can be attributed to a unique set of atmospheric and oceanic forcings. The temporal analysis verified the presence of coherent periodicities (in particular quasi‐2, ‐4, and ‐10–15 year oscillations) in the moisture time series. Copyright © 2005 Royal Meteorological Society
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".