A Growing Degree‐Day Approach to Estimate Larval Hatching Sites Using Backtracking Simulations Without Larval Age
Bibliographic record
Abstract
Abstract Walleye ( Sander vitreus ) are a valuable commercial and sport fishery in the North American Great Lakes, yet the relative contributions of known spawning sites to the Lake Erie population remain poorly understood. Backward‐in‐time Lagrangian particle tracking (backtracking) models have been widely applied to estimate larval origins, but the required simulation duration (i.e., larval age), typically derived from otoliths, often remains unknown. We developed a novel proof‐of‐concept approach to estimate larval age without otolith data, using an observed exponential relationship between the total length of tank‐reared Walleye larvae and growing degree‐days (GDD °C days): , where = 8.34 mm is length at hatch and = 0.0027 (°C days) −1 . GDD for wild larvae was estimated from measured length. Using temperatures experienced by larvae along their paths from backtracking models, we estimated larval age and the origins of observed Walleye larvae in western Lake Erie. Larval age predicted by the GDD‐age model were 72% of those estimated using an age‐length curve from the tank‐reared Walleye and 53% of those estimated using a common GDD‐approach based on mean‐basin temperatures. In all three approaches, larvae were backtracked to the southern shore of the western basin, with 43%–60% originating from hard substrates. We neglected differences in environmental conditions between the tank‐reared and wild‐hatched larvae, which affected the values of and . Sensitivity analyses showed <12% variation in had negligible effects on predicted hatching sites. Our novel GDD approach is an additional tool for estimating larval age and simulating larval origins when otolith data are unavailable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".