Parentage‐based tagging using mothers balances accuracy and cost for discriminating between natural and stocked recruitment for inland fisheries
Bibliographic record
Abstract
Abstract Hatchery programmes are frequently used to supplement inland fisheries, yet achieving successful management outcomes often requires information on stocked versus naturally reproduced fish abundance. Parentage‐based tagging – genetically assigning offspring to their parents – has potential to be an effective approach for distinguishing stocked and naturally reproduced fish. However, several challenges may limit its application to inland fisheries, including genetic relatedness among populations that can affect identification accuracy and high costs of genotyping large broodstocks. Here, we demonstrate the efficacy of parentage‐based tagging based on broodmothers in the Lake Ontario Chinook Salmon fishery, which uses thousands of broodparents and has potential for substantial relatedness between stocked and naturally reproduced fish. Restricting parent sampling to broodmothers reduced costs by two‐thirds, was logistically pragmatic, and achieved >95% accuracy in distinguishing stocked from naturally reproduced salmon. Combined, these results highlight the potential wide applicability of parentage‐based tagging for assessing stocking programmes in inland waters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".