North American Journal of Fisheries Management 22:770–784, 2002 q Copyright by the American Fisheries Society 2002 Effectiveness of a High-Frequency-Sound Fish Diversion System at the Annapolis Tidal Hydroelectric Generating
Bibliographic record
Abstract
Abstract.—We describe an experiment to assess the effectiveness of a fish diversion system that utilizes high-frequency sound at the Annapolis Tidal Generating Station, Nova Scotia, Canada, during the fall of 1999. A band-limited, random-noise signal, with most of the energy focused between 122 and 128 kHz, was projected into the turbine forebay during randomly selected gen-erating cycles. The effectiveness of the diversion system was assessed by monitoring fish passage through the turbine and two adjacent fishways. During the study, fish representing 27 taxa were captured. For the 11 species with sufficient data, we modeled the rate of passage as a function of the sampling site and the on/off status of the diversion system and compared models with and without a set of environmental variables. The environmental component of the model was highly significant for all 11 species. When the environmental variables were removed from the models, the standard errors of the diversion coefficients increased, and between-site comparisons showed that factors other than the on/off status of the diversion system were affecting the effectiveness estimates. Model coefficients were estimated using maximum likelihood, assuming Poisson or extra-Poisson error distributions. The catches of all 11 species were overdispersed, and the sta-tistical significance of the effectiveness estimates was overestimated when a Poisson error distri-
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.065 | 0.015 |
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".