A breath of fresh air: avoiding anoxia and mortality of freshwater turtles in fyke nets by the use of floats
Bibliographic record
Abstract
ABSTRACT Freshwater turtles are susceptible to drowning in commercial fishing nets and this is a major conservation concern. Methods to mitigate turtle bycatch mortality typically involve reducing the capture of bycatch using gear modifications. Another method to reduce mortality is to keep bycatch alive following capture. Using physiological measures of anoxia, this study determined whether providing air spaces using floats within fyke nets could prevent turtles from drowning. In a controlled setting, blood lactate and pH of painted turtles (Chrysemys picta) experimentally introduced into submerged nets, nets with floats, and nets that breached the surface were compared. While emulating commercial fishing practices – where turtles and fish voluntarily entered nets – catch rates and compositions as well as blood lactate in turtles captured were compared in submerged nets with and without floats. Painted turtles in submerged nets exhibited elevated blood lactate and pronounced acidosis compared with turtles from nets with floats and surfaced nets. Catch rates and compositions from emulated fishing were statistically similar in nets with and without floats; however, total fish catches were roughly one‐third less in nets with floats. The same pattern of physiological disturbance was observed with turtles captured in submerged nets with and without floats as in the controlled experiment. Overall, blood physiology indicated that anoxia occurred in turtles in submerged nets while nets with floats reduced physiological disturbance. However, variation in blood lactate levels when fishing fyke nets with floats suggests that turtles were experiencing slight anoxia and so the size of air spaces may be important in allowing access to air. Creating air spaces in fyke nets using floats is a simple and cost‐effective method to avoid the drowning of turtles. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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) | 0.001 | 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".