Multimethod approach to advance provenance determination of fish in stocked systems
Bibliographic record
Abstract
Fish stocking occurs in aquatic systems for conservation purposes, to create or enhance recreational fisheries and to enhance wild-catch commercial fisheries. Identifying and quantifying the contribution of stocking efforts to wild populations is crucial to informing these management objectives. Provenance determination methods trade off accuracy, replicability, and cost-effectiveness at fishery-relevant scales. We present and assess multiple methods for provenance determination using a case study of barramundi ( Lates calcarifer) in the Dry Tropics region of northern Australia. A novel application of near-infrared spectroscopy (NIRS) is compared to two established methods for fish provenance, otolith microchemistry and genetic parentage analysis using microsatellites. The otolith microchemistry method was able to provide extremely high provenance resolution (>99% accuracy). The microsatellite parentage analysis method had a slightly lower overall accuracy (95%), likely as a result of genetic introgression in this region. Provenance determination using otolith NIRS had the lowest overall accuracy (76%). Once limitations regarding spectral noise, image resolution, and sample size are addressed, NIRS may have potential for cost-effectively determining provenance in 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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".