On the critical values of the standard normal homogeneity test (SNHT)
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- Insufficient payload (model declined to judge)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: Observational
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.256
- Threshold uncertainty score
- 0.999
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.261 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- International Journal of Climatology
- Topic
- Hydrology and Drought Analysis
- Field
- Environmental Science
- Canadian institutions
- Hydro-QuébecInstitut National de la Recherche ScientifiqueNatural Sciences and Engineering Research Council of CanadaOuranos
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Keywords
- Homogeneity (statistics)StatisticsMonte Carlo methodHomogenization (climate)StatisticSample size determinationRangingTest statisticStatistical hypothesis testingEconometricsMathematicsClimatologyEnvironmental scienceGeographyGeologyGeodesy
- Has abstract in OpenAlex
- yes