Using forensic geochemistry via fish otoliths to investigate an illegal fish introduction
Bibliographic record
Abstract
Illegal fish introductions create some of the most challenging problems for resource managers because of their potential to harm existing recreational fisheries and their impact on species of conservation concern. Determining the origin of a suspected illegal fish introduction can aid managers in preventing the colonization and subsequent ecosystem impacts of introduced species. In this study, we used forensic geochemistry via fish otoliths to investigate an illegal walleye (Sander vitreus) introduction in Swan Lake, Montana, which provides critical habitat for threatened bull trout (Salvelinus confluentus) and native westslope cutthroat trout (Oncorhynchus clarkii). Core to edge geochemical profiles of 87 Sr/ 86 Sr and Sr/Ca ratios in the walleye otoliths revealed that these fish had been introduced to Swan Lake within the past growing season, and their geochemical signature matched that of walleye sampled from Lake Helena, Montana, located 309 road kilometres away. This research highlights application of a tool fisheries managers can use to identify the natal waterbody source of illegally introduced fish.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".