Small Hydropower Plants in Generation in the Rural Area of the West and Southwest of Paraná State-Brazil
Bibliographic record
Abstract
Hydropower generation is the main source of electric energy in Brazil and the West and Southwest of Paraná. Year after year the energy matrix has been diversifying, including new sources, such as wind power. However, due to the power accumulation characteristic of hydropower, this type of source is important for the stability of the electric system. Currently, hydropower corresponds to just over 60% of all power installed in Brazil. Particularly in the West and Southwest region of Paraná, this number increases, and Paraná’s share in the Brazilian matrix is approximately 16% of the installed potential. This compromises the productive areas of these regions by the flood generated by the plants. In order to verify the participation of these regions in the hydropower matrix and its production, a survey was made of the installed potential and the constructive profile of the hydroenergy projects. In addition, the flow and production trend of the Salto Santiago hydropower in the Iguaçu River watershed and the Melissa hydropower in the Piquiri River watershed were verified during the period from 2003 to 2017. Finally, a conceptual map of the western and southwestern regions of Paraná was elaborated with the hydropower generation profile. It was concluded by the survey that the regions of the study have great participation in the hydropower matrix of Brazil and that its production and the flow of the rivers in the entrance of these remains practically unchanged in the studied period.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".