Telemetry reveals limited exchange of walleye between Lake Erie and Lake Huron: Movement of two populations through the Huron-Erie corridor
Bibliographic record
Abstract
Immigration and emigration of individuals among populations influence population dynamics and are important considerations for managing exploited populations. Lake Huron and Lake Erie walleye (Sander vitreus) populations are managed separately although the interconnecting Huron-Erie Corridor provides an unimpeded passageway. Acoustic telemetry was used to estimate inter-lake exchange and movement within St. Clair River and Detroit River. Of 492 adult walleyes tagged and released during 2011 and 2012, one fish from Tittabawassee River (Lake Huron; 1 of 259, 0.39%) and one individual from Maumee River (Lake Erie; 1 of 233, 0.43%) exchanged lakes during 2011–2014. However, both fish returned to the lake where tagged prior to the next spawning season. The one walleye from Maumee River that moved to Lake Huron made repeated round-trips between Lake Erie and Lake Huron during three consecutive years. Of twelve fish tagged in the Tittabawassee River detected in the Huron-Erie Corridor, few (n = 3) moved south of Lake St. Clair to the Detroit River. Ten walleye tagged in the Maumee River entered the Huron-Erie Corridor, and five were detected in the St. Clair River. Our hypothesis that walleye spawning in Maumee River, Lake Erie, served as a source population to Lake Huron (“sink population”) was not supported by our results. Emigration of walleye to Lake Huron from other populations than the Maumee River, such as those that spawn on in-lake reefs, or from Lake St. Clair may contribute to Lake Huron walleye 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".