Measuring Detection Efficiency of High-Residency Acoustic Signals for Estimating Probability of Fish–Turbine Encounter in a Fast-Flowing Tidal Passage
Bibliographic record
Abstract
Semidiurnal tidal currents can exceed 5 ms−1 in Minas Passage, Bay of Fundy, where a tidal energy demonstration area has been designated to generate electricity using marine hydrokinetic turbines. The risk of harmful fish–turbine interaction cannot be dismissed for either migratory or local fish populations. Individuals belonging to several fish populations were acoustically tagged and monitored by using acoustic receivers moored within the Minas Passage. Detection efficiency ρ is required as the first step to estimate the probability of fish–turbine encounter. Moored Innovasea HR2 receivers and high-residency (HR) tags were used to obtain detection efficiency ρ as a function of range and current speed, for near-seafloor signal paths within the tidal energy development area. Strong tidal currents moved moorings, so HR tag signals and their reflections from the sea surface were used to measure ranges from tags to receivers. HR2 self-signals that reflected off the sea surface showed which moorings were displaced to lower and higher levels on the seafloor. Some of the range testing paths had anomalously low ρ, which might be attributed to variable bathymetry blocking the line-of-sight signal path. Clear and blocked signal paths accord with mooring levels. The application of ρ is demonstrated for the calculation of abundance, effective detection range, and detection-positive intervals. High-residency signals were better detected than pulse position modulation (PPM) signals. Providing that the presently obtained ρ applies to tagged fish that swim higher in the water column, there is a reasonable prospect that probability of fish–turbine encounter can be estimated by monitoring fish that carry HR tags.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".