Spatial and temporal impact of pingers on porpoises
Bibliographic record
Abstract
Bycatches are considered the most serious threat to harbour porpoises ( Phocoena phocoena ) and other small cetaceans worldwide. Pingers are used to reduce bycatch levels, but may also deter porpoises from critical habitats. We investigated the spatial and temporal responses of porpoises to simulated bottom-set nets equipped with periodically operating Dukane NetMark 1000 pingers. Echolocation rates were monitored by porpoise click train detectors (PODs) placed at and around the nets, and a shore-based observation team recorded surfacing positions and movements. Pinger sound significantly reduced the median echolocation encounter rate by 50%–100% at PODs placed up to 500 m and reduced the sighting rate up to 375 m from the simulated net. The average distance of approach increased by 300 m. When pingers were silent after being active for 24 h 50 min, the return time of porpoises was 6 h, in comparison with 2.5 h after pingers had been silent. During the study period of approximately 50 days, habituation was detectable at two of nine PODs. The results indicate that pingers affect porpoises at greater distances than previously observed. This confirms that pingers are an effective bycatch mitigation measure, but alternative solutions should be applied in ecologically important habitats and migration routes. An example is given from the Baltic region.
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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".