Examining the potential of otolith chemistry to determine natal origins of wild Lake Michigan Chinook salmon
Bibliographic record
Abstract
Previous research has demonstrated a large movement of hatchery-reared Chinook salmon (Oncorhynchus tshawytscha) from Lake Huron to Lake Michigan, suggesting the potential for wild fish to exhibit similar movement patterns. We assessed the feasibility of using otolith microchemistry to estimate the natal source composition of wild Chinook salmon in Lake Michigan and evaluate interbasin movement. Otolith pairs were extracted from juvenile and adult fish collected in 2015 and 2016 from Great Lakes tributaries. Otoliths were analyzed using laser ablation inductively coupled plasma mass spectrometry to determine trace element concentrations, and four multivariate classification algorithms were evaluated for classification accuracy. Juvenile data reclassified to their natal regions with up to 89% success on a basin level, with a random forest approach performing the best among all models. Assigning adults to their natal origins resulted in more success on a basin-wide scale (74% to 88%) compared with a regional scale (32% to 51%), but success was still below juvenile reclassification accuracy. Our findings suggest that otolith microchemistry can be used to estimate wild Chinook salmon interbasin movement and that classification accuracy can be improved by matching juvenile and adult year classes in our assessment samples. Ultimately, we intend to use these models to assess the effects of wild Chinook salmon interbasin movement on Lake Michigan predatory demand and evaluate the risks of various stocking alternatives.
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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 | 0.002 |
| 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) | 0.001 | 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".