Using Behavioral Observations to Develop Escape Devices for Freshwater Turtles Entrapped in Fishing Nets
Bibliographic record
Abstract
Abstract The drowning of freshwater turtles following incidental capture in fishing gear has the potential to cause population declines. Fyke nets can be equipped with bycatch reduction devices that enable the escape of turtles before they drown. We employed quantitative and qualitative behavioral observations (with action cameras deployed underwater) to develop a new, collapsible, escape bycatch-reduction device that mounts internally in the terminal end of a fyke net. We also used behavioral observations to identify areas of the net most used by turtles, thus revealing the most logical placement for an escape bycatch-reduction device. When turtles were introduced into modified nets, escape was rapid (mean of 12.4 min), with 100% escape for map Graptemys geographica and musk turtles Sternotherus odoratus and 94% escape for painted turtles Chrysemys picta. Our preliminary field trials indicated that modified fyke nets decreased the capture rate of turtles relative to unmodified nets. Escape devices can be used as a key component of a bycatch reduction program and be particularly effective when paired with exclusion bycatch-reduction devices. The escape device developed in this study can potentially be used in the local fishery or modified for other fisheries. The use of behavioral observation to guide the development of bycatch reduction devices may provide an extra tool for managers to increase selectivity and maintain sustainable harvests of target fish.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".