Experimental and field evaluation of otolith strontium as a marker to discriminate between river-spawning populations of walleye in Lake Erie
Bibliographic record
Abstract
Otolith microchemistry is a commonly used tool for stock discrimination in fisheries management. Two key questions remain with respect to its effectiveness in discriminating among river-spawning populations. First, do larvae remain in their natal river long enough for their otoliths to pick up that system’s characteristic chemical signature? Second, are larval otolith microchemical differences between natal rivers sufficiently large to overcome spatiotemporal variation in water chemistry? We quantified how larval age, the ratio of ambient strontium to calcium concentrations (Sr:Ca), and water temperature influence otolith Sr in both lab-reared and wild-collected Lake Erie walleye (Sander vitreus). Otolith microchemistry shows promise as a spawning stock discrimination tool, given that otolith Sr in larval walleye (i) is more strongly influenced by ambient Sr:Ca than by temperature; (ii) reflects Sr:Ca levels in the natal environment, even in larvae as young as 2 days old; and (iii) can effectively discriminate between larvae captured in two key Lake Erie spawning tributaries, even in the face of short larval river residence times and within-year and across-year variation in ambient Sr:Ca.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".