Sound pressure level weighting of the center of activity method to approximate sequential fish positions from acoustic telemetry
Bibliographic record
Abstract
Proximity of acoustically tagged fish to a hydrophone correlates with detection probability, thus allowing fish center of activity (CA) estimation as weighted averages of hydrophones’ positions over fixed listening intervals. Alternately, weighting by the detection sound pressure levels (SPL) would require only a single detection from each hydrophone. We tested SPL-weighted averaging performance relative to an independent and highly accurate positioning method, trilateration, for tagged fish. Positions calculated using SPL were similar to those using CA in the shape and sequence of the paths relative to the trilateration standard. Neither up- nor down-weighting transforms of SPL significantly affected solution error (209 m average, range 192–215 m) and did not differ significantly in error or shape from CA (194 m) over seven fish (1.9 million detections) even at the shortest of five tested averaging intervals (150, 300, 600, 1200, 2400 s). The method increased the stability of position estimates when a single moving receiver was used to create a synthetic array. Potentially, useful solutions can be calculated for tags outside array perimeters by extrapolating regressions to known source SPL.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".