A biophysical model of Lake Erie walleye (Sander vitreus) explains interannual variations in recruitment
Bibliographic record
Abstract
We used a three-dimensional coupled hydrodynamic–ecological model to investigate how lake currents can affect walleye ( Sander vitreus ) recruitment in western Lake Erie. Four years were selected based on a fall recruitment index: two high recruitment years (i.e., 1996 and 1999) and two low recruitment years (i.e., 1995 and 1998). During the low recruitment years, the model predicted that (i) walleye spawning grounds experienced destructive bottom currents capable of dislodging eggs from suitable habitats (reefs) to unsuitable habitats (i.e., muddy bottom), and (ii) the majority of newly hatched larvae were transported away from the known suitable nursery grounds at the start of their first feeding. Conversely, during two high recruitment years, predicted bottom currents at the spawning grounds were relatively weak, and the predicted movement of newly hatched larvae was toward suitable nursery grounds. Thus, low disturbance-based egg mortality and a temporal and spatial match between walleye first feeding larvae and their food resources were predicted for the two high recruitment years, and high egg mortality plus a mismatch of larvae with their food resources was predicted for the two low recruitment years. In general, mild westerly or southwesterly winds during the spawning–nursery period should favour walleye recruitment in the lake.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".