Bibliographic record
Abstract
Available climatological information of Central Brazil does not satisfy the requirements for detailed climate diagnosis, as they do not provide the necessary spatial resolution for water resources management purposes. Annual and seasonal climatology (1971-2000) of precipitation from 6 meteorological stations and 54 rain gauge stations from Central Brazil were used to test 6 different spatial interpolation methods. The accuracy of estimations was assessed through visual analysis as well as independent validation based on basic statistics, such as mean square error, correlation coefficient and Nash-Sutcliffe efficiency criterion. Between most commonly used methods, such as Inverse Distance Weighting (IDW) and Ordinary Kriging, the multivariate regression (using altitude, latitude and longitude as explanatory variables) with interpolation of residuals by IDW (MRegIDW) have performed the lowest errors, the highest correlation and Nash-Sutcliffe efficiency criterion. Comparative visual analysis of the maps complements the independent validation results showing that MRegIDW corresponds to finer details than other interpolation methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.938 | 0.924 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".