Application of forensic techniques to enhance fish conservation and management: injury detection using presumptive tests for blood
Bibliographic record
Abstract
The detection of injury on the skin of fish has generally been limited to gross macroscopic examination, which has numerous limitations, including researcher subjectivity and a lack of quantitative analytical capacity.The use of chemical enhancers, such as those used by forensic analysts, can aid in the detection and quantification of skin injuries in fish, which can arise from fish interactions with humans and anthropogenic infrastructure (e.g.recreational and commercial fishing, research sampling, fishway passage or guidance, turbines).In this review, we examine several presumptive tests for blood and evaluate their potential usefulness for detecting and quantifying injury in fish.Our evaluation was based on sensitivity, specificity, cost, carcinogenicity and ease of use.Fluorescein and Bluestar © offer the ability to perform whole body detection, but require low-light conditions and a digital camera to capture the emitted light.Several tests (i.e.Hemastix ® , Hemi-dent™, phenolphthalein) yield rapid results and do not require large or expensive pieces of equipment, which makes them ideal for field use, although further research is needed to validate these tools for use on different fish species and in different contexts.Collectively, these tools show promise for a variety of fish research, conservation and management applications, including hydropower assessment, commercial fisheries bycatch evaluation, and analysis of the practices and gear regulations associated with recreational angling.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".