Environmental impacts of hydropower plants in Brazil: an identification guide
Bibliographic record
Abstract
This paper presents a guide for identifying the environmental impacts of hydroelectric enterprises. The qualitative research used the following methods: case studies, systematic literature review (SLR), content analysis, and consultation with expe ts. Four sources of information were used, including Environmental Impact Statements (EISs), scientific articles, best practice guides, and expert consultation. All EISs of hydroelectric plants submitted to the Brazilian federal Environmental Licensing between 2010 and 2020 (8 EISs) were analysed. RSL identified 68 scientific papers eligible for analysis and collection of impacts. The results were compared with Canadian practice and discussed in a virtual workshop of 15 expe ts. The guide has 90 impacts and can be used by environmental consulting firms and environmental agencies in the preliminary identification of environmental impacts of hydroelectric dams, contributing to the improvement of planning carried out in the EIA scoping stage of future environmental studies of this type.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".