A importância do estudo de impacto ambiental e do relatório de impacto ambiental nos licenciamentos do Estado de São Paulo
Bibliographic record
Abstract
A importância do estudo de impacto ambiental e do relatório de impacto ambiental nos licenciamentos do Estado de São Paulo / Vivian Galvão Milani.--versão revisada de acordo com a resolução CoPGr 6018 de 2011.--Piracicaba, 2017.208 p. Dissertação (Mestrado) --USP / Escola Superior de Agricultura "Luiz de Queiroz".Centro de Energia Nuclear na Agricultura.1. Estudo de impacto ambiental 2. Licenciamento ambiental 3. Direito ambiental 4. Desregulamentação ambiental 5. Relatório ambiental preliminar I. Título AGRADECIMENTOS Primeiramente agradeço a Deus por me permitir mais uma rica e grandiosa experiência.Ao Professor e orientador Giancarlo Conde Xavier Oliveira.Não tenho palavras para descrever o quanto sou grata.Agradeço pela confiança, amizade e pelos valiosos ensinamentos, os quais levarei para vida toda.Aos mestres com carinho!Professor Sergius Gandolfi, por suas aulas brilhantes e por me mostrar uma visão mais profunda das questões ambientais e Professor Eduardo Luís Martins Catharino, pela amizade, pelas contribuições ao projeto de pesquisa, participação no comitê de orientação e pelo tempo e paciência despendidos em nossas longas reuniões; Aos meus professores do mestrado pela competência nos ensinamentos durante o cumprimento dos créditos das disciplinas;
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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".