Standardized Precipitation Index Zones for México
Bibliographic record
Abstract
"Precipitation zone systems exist for México based on seasonality, quantity of precipitation, climates andgeographical divisions, but none are convenient for the study of the relation of precipitation with phenomenasuch as El Niño. An empirical set of seven exclusively Mexican and six shared zones was derived from threeseries of Standardized Precipitation Index (SPI) images, from 1940 through 1989: a whole-year series (SPI-12)of 582 monthly images, a six month series (SPI-6) of 50 images for winter months (November through April),and a six-month series (SPI-6) of 50 images for summer months (May through October). By examination ofprincipal component and unsupervised classification images, it was found that all three series had similarzones. A set of basic training fields chosen from the principal component images was used to classify all threeseries. The resulting thirteen zones, presented in this article, were found to be approximately similar, varying principally at zone edges. A set of simple zones defined by just a few vertices can be used for practicaloperations. In general the SPI zones are homogeneous, with almost no mixture of zones and few outliers ofone zone in the area of others. They are compared with a previously published map of climatic regions.Potential applications for SPI zones are discussed."
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".