Bibliographic record
Abstract
Abstract The southwestern United States has experienced some of the most important increases in nighttime minimum temperatures over the last 60 yr, and climate models are projecting more of the same to the end of the century. As climate, geography, and population density vary considerably over the area, very diverse extreme temperature levels and dynamics are observed. It is shown how nighttime minimum temperatures over the 1950–2009 period exhibit more complex dynamics than daytime maximum temperatures. The author studies nighttime minimum temperature series from 12 locations and presents one model capable of capturing all the features of the data at each location. The time series preprocessing model normalizes seasonal shocks by daily and yearly volatility components before modeling the residual volatility as an exponential generalized autoregressive conditional heteroskedasticity [EGARCH(1, 1)] process with seasonal autoregressive structure to account for the presence of nonlinear and seasonal linear dependence, respectively, in the residual series. An exceedance over high thresholds approach is then used to model the tail of the distribution of scaled residuals from the preprocessing model. The resulting marginal distribution of nighttime minimum temperature at each location is then examined to see how it has changed in mean, scale, and shape, respectively, over the 60-yr period. Changes at the 12 locations vary considerably: many locations have seen considerable change in some or all of the three parameters, while two locations have experienced little or no change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".