TÉCNICAS DE GEOPROCESSAMENTO E SENSORIAMENTO REMOTO APLICADAS NA AVALIAÇÃO DO POTENCIAL HIDROGEOLÓGICO DA FOLHA IRAUÇUBA
Bibliographic record
Abstract
A CPRM – Servico Geologico do Brasil esta executando, atraves de um programa de cooperacao tecnica entre o Brasil e o Canada, o Projeto “Otimizacao de Metodologias para Prospeccao de Aguas Subterrâneas em Rochas Cristalinas”, no contexto climatico semiarido, com a finalidade de estabelecer modelos de ocorrencia de agua em aquiferos fissurados, estudar os mecanismos de salinizacao da agua em terrenos cristalinos, bem como desenvolver metodologias de prospeccao que aumentem o indice de acertos na locacao de pocos produtivos. Este projeto esta sendo realizado em tres areas-piloto, localizadas nos estados do Ceara, Rio Grande do Norte e Pernambuco. No Ceara, a area-piloto escolhida foi a Folha Iraucuba (SA.24-Y-D-V). Uma das abordagens do Projeto, apresentada neste trabalho, e a utilizacao de geotecnologias, envolvendo tecnicas de tratamento digital de imagens de satelites e sistemas de informacoes geograficas (SIGs), a fim de analisar e integrar as variaveis do meio fisico-biotico que influenciam na capacidade de percolacao e acumulacao de agua subterrânea. O modelamento dos dados permitiu a elaboracao do mapa de potencial hidrogeologico da area, que devera servir de suporte para estudos mais detalhados, direcionando as pesquisas para locacao de pocos.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".