Examining trends in multiple parameters of seasonally‐relative extreme temperature and dew point events across North America
Bibliographic record
Abstract
Abstract Concurrent with the background rise in global mean temperatures, changes in extreme events are also becoming evident, and are arguably more impactful on society. This research examines trends in three components of seasonally‐relative extreme temperature and humidity events in North America that directly influence human thermal comfort: event frequency, duration, and areal extent. Results indicate that for the majority of the study domain, changes in these events are in the expected direction with changes in means. Extreme heat events are generally increasing throughout the domain, with the largest changes in summer and autumn in the eastern portion of Canada and the United States. Cold events are largely decreasing in these same locations and seasons, with additional widespread decreases in winter. Interestingly, significant increases in cold events are also evident in autumn in parts of the western United States. Extreme humidity events are showing an even greater change than temperature events – nearly all of Canada and most of the United States is seeing significant increases in extreme humid events and decreases in dry events, while the southwestern deserts show widespread significant increases in dry events, especially in winter and spring. Changes in event duration and spatial extent mimic these results. Importantly, this research demonstrates that there are regions that show changes to extreme events that differ from the overall changes in means, highlighting the importance of looking beyond climate averages to examine not only extreme events, but changes in higher‐order statistical moments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".