A Spatially Explicit Model to Predict Walleye Spawning in an Eastern Lake Ontario Tributary
Bibliographic record
Abstract
Abstract Field egg collections coupled with physical habitat measurements were used to develop, apply, and validate a spatially explicit model to predict the spawning sites of walleyes Sander vitreus in an eastern Lake Ontario tributary. We collected 22,246 walleye eggs using passive traps and measured the attendant habitat variables, including substrate composition and heterogeneity, water depth and velocity, and cumulative spring water temperature. The squared term that we used for water depth in our regressions and selected over linear and polynomial fits better represented the relationship between spawning probability and depth. To predict walleye spawning likelihood, we developed 10 candidate models based on the available spawning data; following an information-theoretic approach, we compared the models by means of Akaike's information criterion. The best model had an area under the receiver operating curve of 0.89 and a weight of evidence of 0.32, where the probability of spawning (egg presence) was associated with predominance of course substrate and shallow depth and timing was associated with the accumulated spring water temperature. The model was applied to the study stream and validated by additional egg collections. The model correctly classified egg presence/absence in 66.7% (10 of 15) of egg traps in the initial validation sample. After a high-flow event, classification success decreased to only 20% (3 of 15 traps), probably because of the redistribution of eggs to less suitable habitats in depositional areas. Prediction of walleye spawning distribution allows for reach-scale assessment of critical habitat to help guide spawning habitat restoration efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".