Climatic influences on Markovian transition matrices for Vancouver daily rainfall occurrence
Bibliographic record
Abstract
Two‐state, first‐order, single‐site Markov models for daily precipitation occurrence were developed for each winter rainy season over the historical period of record at five long‐term meteorological stations in the lower Fraser Valley of British Columbia, Canada. Monotonic temporal trends in the independent elements of the transition matrices were then assessed. Although the results remain tentative, there is some evidence for a regionally coherent long‐term negative trend in the probability of wet‐to‐dry state transitions, P10 (or a positive trend in the probability of a wet day being followed by another wet day, P11). In contrast, there is no evidence for a regionally coordinated and consistent trend in the probability of dry‐to‐wet state transitions, P01 (or, therefore, in the probability of a dry day being followed by another dry day, P00). These results appear loosely consistent with previous statistical climate change impact studies in the region, and might be physically interpreted as suggesting a gradual increase in the local typical duration of a Pacific frontal storm during hydrologic winter, with no systematic trend in the average duration of a dry‐day interlude. Additionally, the probability of any day‐to‐day precipitation state transition (from wet to dry, or from dry to wet), PST, has been tentatively interpreted to exhibit an area‐wide negative long‐term trend, suggesting an overall increase in precipitation memory. The findings provide some additional regional context for several issues in hydrometeorological modelling, climatology, and environmental impact 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".