On the critical values of the standard normal homogeneity test (SNHT)
Bibliographic record
Abstract
Abstract The use of the standard normal homogeneity test (SNHT) for homogenization of climatological records and studying changes in their patterns has increased in recent years. The critical values of this test were originally developed for sample sizes ranging from 10 to 250 using relatively short Monte Carlo simulations (MCS). The objective of this paper is to improve the critical values of the SNHT and extend them to large sample sizes. The critical values, along with their standard errors, are developed for 108 sample sizes ranging from 10 to 50 000 using 30 replicates of one million samples for each sample size. These critical values mimic the tails of the SNHT statistic better and therefore are more accurate, and would be useful for making correct statistical inference for climate data homogenization and assessment of climate variability in future studies. 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 | 0.068 | 0.450 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".