Do Carbonated Beverages Reduce Bleeding from Gill Injuries in Angled Northern Pike?
Bibliographic record
Abstract
Abstract The premise of catch-and-release recreational angling is that postrelease survival is high. Therefore, it is common for anglers, management agencies, and conservation organizations to share information on handling practices and other strategies that are believed to improve the welfare and survival of fish that are released. A recent surge in popularity has sensationalized the use of carbonated beverages to treat bleeding fish—an intervention that is purported to stop bleeding but has yet to be validated scientifically. We captured Northern Pike Esox lucius via hook and line and experimentally injured their gills in a standardized manner. Gill injuries were treated with Mountain Dew, Coca-Cola, or carbonated lake water. The duration and intensity of bleeding as well as overall blood loss (using gill color as a proxy) were observed while the fish were held in a lake water bath. As a control, we used a group of experimentally injured fish that did not have liquid poured over their gills before the observation period. All treatments and the control were conducted at two different water temperatures (11–18°C and 24–27°C) to determine whether the effects of pouring carbonated beverages over injured gills are seasonally dependent. When compared to the control, we found that the duration and intensity of bleeding increased regardless of the type of carbonated beverage used in this study, and there was no effect of season. Use of chilled versus ambient-temperature beverages similarly had no influence on outcomes. As such, there is no scientific evidence to support the use of carbonated beverages for reducing or stopping blood loss in fish that receive gill injuries during recreational angling based on the context studied here. Our study reinforces the need to scientifically test angler anecdotes and theories regarding best practices for catch-and-release fishing.
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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) | 0.002 | 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".