Changes in North American extremes derived from daily weather data
Bibliographic record
Abstract
Detailed homogeneity assessments of daily weather observing station data from Canada, the United States, and Mexico enabled analysis of changes in North American extremes starting in 1950. The approach used a number of indices derived from the daily data, primarily based on the number of days per year that temperature or precipitation observations were above or below percentile thresholds. Station level indices were gridded to produce North American area‐averaged time series. The results indicated that the increase in the number of days exceeding the 90th percentile is about the same magnitude as the decrease in the number of days below the 10th percentile. Analysis of extremes farther out on the tails of the distribution (e.g., 95th and 97.5th percentiles) reveals changes very similar to the 90th and 10th percentiles. Annual extreme lowest temperatures are warming faster than annual extreme highest temperatures when the index assessed is the actual temperature, but cold and hot extremes are changing about the same when examined on a normalized basis. On the basis of several measures, heavy precipitation has been increasing over the last half century, and the average amount of precipitation falling on days with precipitation has also been increasing. These observed changes since the late 1960s, decrease in cold extremes, increases in warm extremes, and increases in heavy precipitation, are consistent with a warming planet.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".