An extended cumulative logit model for detecting a shift in frequencies of sky‐cloudiness conditions
Bibliographic record
Abstract
In Canada, sky‐cloudiness (or cloud cover) condition is reported in terms of tenths of the sky dome covered by clouds and hence has 11 categories (0/10 for clear sky, 1/10 for one tenth of the sky dome covered by clouds, …, and 10/10 for overcast). The cloud cover data often contain temporal discontinuities (changepoints) and present a large amount of observational uncertainty. Detecting changepoints in a sequence of continuous random variables has been extensively explored in both statistics and climatology literature. However, changepoint analyses of a multinomial sequence data with extra variabilities are relatively sparse. This study develops a likelihood ratio test for detecting a sudden change in parameters of the cumulative logit model for a multinomial sequence. The extra‐multinomial variation is accounted for by allowing an overdispersion parameter in the model fitting. Moreover, the empirical distribution of the estimated changepoint is approximated by a bootstrap method. An application of this new technique to real sky cloudiness data in Canada is presented.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".