Synthesizing Professional Opinion and Published Science to Build a Conceptual Model of Walleye Recruitment
Bibliographic record
Abstract
Abstract Understanding and predicting recruitment, longstanding goals in fisheries science and ecology, are complicated by variation in the importance of environmental drivers coupled with the dynamic nature of individual ecosystems. Developing an understanding of recruitment from well-monitored stocks offers an opportunity to overcome these complexities. We used a systematic literature review, a survey, and a workshop attended by professionals with expertise in recruitment of Walleye Sander vitreus to identify common environmental drivers of Walleye recruitment and additional sources of variation (i.e., context dependencies) among populations. The importance of individual environmental drivers, as well as the direction of their influence, differed as a function of geographic region, lake surface area, and Walleye life stage. The literature suggested abiotic conditions (e.g., temperature) during the first year of life were influential in determining recruitment. Professional opinion noted the importance of biotic factors, with prey availability and predation risk having the most consistent relationships with recruitment. We synthesized this information to propose a conceptual model that illustrates the suite of characteristics that shape Walleye recruitment over large spatial and temporal scales. Our findings emphasize the importance of first-year growth and system-specific contextual factors, which can alter the relative importance of the environmental drivers of recruitment.
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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 | 0.043 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".