Mapas da especialização trimestral de chuva e hietrograma trimestral da região metropolitana de Teresina / PI
Bibliographic record
Abstract
Mapas da distribuição espacial de chuva trimestral subsidiam o gerenciamento e planejamento dos recursos hídricos. O objetivo deste trabalho é apresentar o mapeamento da distribuição espacial trimestral e anual da precipitação pluviométrica dos municípios da região metropolitana de Teresina/PI. Na espacialização dos dados pontuais de chuva (1977 a 2006), utilizou-se a função Topo to Raster como interpolador dos dados das estações pluviométricas. Os trimestres mais úmidos, em ordem decrescente, são: 1° trimestre (janeiro a março), com 747mm, 2° trimestre (abril a junho), com 372mm, 4° trimestre (outubro a dezembro), com 160mm 3° trimestre (julho a setembro), com 24mm. O somatório da precipitação média nos quatro trimestres, ou seja, foi de 1.303mm.ano-1. ABSTRACT: Quarterly rainfall spatial distribution maps subsidize water resource management and planning. The objective of this work is to present the mapping of the quarterly and annual spatial distribution of pluviometric precipitation of the municipalities of the metropolitan region of Teresina / PI. In the spatialization of the rainfall data (1977 to 2006), the Topo to Raster function was used as the interpolator of the rainfall data. The wettest quarters, in descending order, are: 1st quarter (January to March), with 747mm, 2nd quarter (April to June), with 372mm, 4th quarter (October to December), with 160mm 3rd quarter July to September), with 24mm. The sum of the average precipitation in the four quarters, that is, was 1.303 mm.year-1.
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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 | 0.002 | 0.005 |
| 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) | 0.009 | 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".