A simplified methodology for the analysis of the establishment of hydrokinetic parks downstream from hydroelectric plants
Bibliographic record
Abstract
The exploitation of surplus energy through the setup of hydrokinetic parks in reservoirs downstream from hydroelectric plants is on the increase worldwide. Costly measurements in loco are required in order to estimate the amount of energy that may be extracted from a river. However, river modeling provides river velocities and depths whereby the power of hydrokinetic parks may be estimated. Velocities and depths were simulated with a Saint-Venant 2-D model applied to a downstream reservoir of the Tucuruí hydroelectric dam. Velocities were extrapolated in the vertical direction by means of a logarithmic function to determine the vertical velocity profile, which transfers energy to the turbines. The turbine diameters were defined according to the depths of the studied section and information available in the literature. In the analyzed section, 73 turbines with approximately 3 MW may be installed. Power may be greater if other sections are evaluated. However, studies on environmental impacts and production reduction due to decrease of water level downstream the hydroelectric plant should be taken into account prior to the installation of hydrokinetic plants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".