Larval dispersal underlies demographically important intersystem connectivity in a Great Lakes yellow perch (<i>Perca flavescens</i>) population
Bibliographic record
Abstract
Ability to quantify connectivity among spawning subpopulations and their relative contribution of recruits to the broader population is a critical fisheries management need. By combining microsatellite and age information from larval yellow perch (Perca flavescens) collected in the Lake St. Clair – Detroit River system (SC-DRS) and western Lake Erie with a hydrodynamic backtracking approach, we quantified subpopulation structure, connectivity, and contributions of recruits to the juvenile stage in western Lake Erie during 2006–2007. After finding weak (yet stable) genetic structure between the SC-DRS and two western Lake Erie subpopulations, microsatellites also revealed measurable recruitment of SC-DRS larvae to the juvenile stage in western Lake Erie (17%–21% during 2006–2007). Consideration of precollection larval dispersal trajectories, using hydrodynamic backtracking, increased estimated contributions to 65% in 2006 and 57% in 2007. Our findings highlight the value of complementing subpopulation discrimination methods with hydrodynamic predictions of larval dispersal by revealing the SC-DRS as a source of recruits to western Lake Erie and also showing that connectivity through larval dispersal can affect the structure and dynamics of large lake fish populations.
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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 | 0.000 |
| 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".