Bayesian change‐point analysis of heat spell occurrences in Montreal, Canada
Bibliographic record
Abstract
Abstract Positive/upward shifts in the rate of occurrence of heat spells can considerably impact socioeconomic sectors. Particularly, populous urban areas and centers of regional socioeconomic activities are more vulnerable to the enhanced activity of heat spells. In this study, 24 time series of annual counts of summer‐season (June–August) heat spells are derived from homogenized records of daily minimum and maximum temperatures (i.e. Tmin and Tmax) observed at McTavish station, located in the center of Montreal (Canada), over the period 1896–1991. Twelve of these time series, which fulfill the assumption of the Poisson process for heat spell occurrences, are examined for abrupt changes in the rate of occurrences using hierarchical Bayesian change‐point approach. In these analyses, a heat spell is defined as an extreme climate event with Tmin and Tmax simultaneously above selected thresholds and a duration ≥ 1‐day. The results of the Bayesian change‐point analyses suggest structural inhomogeneities within the heat spell observations, i.e. the results do not support abrupt changes for all time series of annual counts of heat spells; this may not have been possible to detect by studying heat spells defined on the basis of just a single combination of Tmin and Tmax thresholds. Furthermore, the overall results of the Bayesian change‐point analyses and those of commonly employed nonparametric trend detection and estimation techniques, when applied to change‐point free smaller samples, suggest that there is inadequate evidence in favor of increased activity of heat spells in Montreal during the third last and second last decades (i.e. 1970s and 1980s) of the 20th century, which are the most recent decades of the observation period analyzed. Copyright © 2006 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".