Mixed stock analysis of genetic compositions of lake sturgeon (<i>Acipenser fulvescens</i>) mixtures in Lake Michigan: hierarchical spatial heterogeneity and evidence of improving recruitment in Wisconsin spawning populations
Bibliographic record
Abstract
Information regarding site occupancy of fish that migrate long distances during nonspawning periods together with estimates of recruitment trends for individual populations can be informative for management, especially when individuals from different spawning populations intermix and are sampled or harvested together. Tendencies for individuals from different populations to preferentially occupy specific regions increases vulnerability to anthropogenic and natural disturbances. Using mixed stock analysis (MSA), we estimated population-specific occupancy of lake sturgeon (Acipenser fulvescens) in open-water and nearshore regions of Lake Michigan across a hierarchy of spatial scales. Open-water mixture composition differed between Lake Michigan’s eastern and western basins. Significant heterogeneity in habitat occupancy was also observed at microgeographic scales throughout open-water regions of Green Bay, indicating nonrandom occupancy to regions proximal to natal streams. Estimates of relative recruitment levels determined from MSA extensions indicated increasing recruitment trends for spawning populations associated with Wisconsin tributaries (Oconto–Peshtigo, Fox, and Menominee rivers). Our lake sturgeon results demonstrate the utility of genetic data for informing management efforts for spatially structured, highly migratory species. Similar analyses could prove beneficial for species with similar characteristics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".