Echolocation behavior of harbor porpoises Phocoena phocoena around chemically enhanced gill nets
Bibliographic record
Abstract
The echolocation behavior of harbor porpoises Phocoena phocoena around gillnets was monitored to test their response to chemically (BaSO 4 ) enhanced gill nets, designed to be more acoustically reflective than commercial nets.Field trials were conducted between 22 July and 31 August 2000 in the Bay of Fundy, Canada.Echolocation clicks were continuously monitored with Porpoise Echolocation Detectors (PODs).Commercial and experimental (chemically enhanced) gill nets were set for groundfish in water depths from 100 to 130 m.Echolocation occurrence (the proportion of 10 s intervals during which clicks were detected) and echolocation rate (the number of clicks h -1 ) were measured.The PODs varied in their detection ability, so comparisons between commercial and enhanced nets were made with individual PODs.Neither echolocation rate nor occurrence differed with net type for any POD.Significantly more echolocation was detected during the day than at night.Echolocation rate and echolocation occurrence varied with depth and location, possibly reflecting concomitant variation in the relative abundance of porpoises and/or their prey.We conclude that porpoises do not respond to the acoustic reflectivity of the modified nets.Rather, the effectiveness of these nets is apparently due to other factors, such as their physical properties, particularly their stiffness.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".