Environmental flows and recruitment of walleye (<i>Sander vitreus</i>) in the Peace–Athabasca Delta
Bibliographic record
Abstract
Age composition data from a commercial walleye (Sander vitreus) fishery in the Peace–Athabasca Delta were used to test the hypotheses that recruitment varied interannually and that recruitment was related to local hydrological conditions. Variable interannual recruitment was strongly supported over a null model of constant annual recruitment. Assuming recruitment strength was established in a walleye's first year of life, several a priori hypotheses relating recruitment to river discharge or lake levels were tested using an information–theoretic approach. The data best supported the hypothesis of a positive relationship between walleye recruitment and mean discharge in the Athabasca River during the fry rearing period (weeks 18–43). Approximately 25% of observed variability in annual recruitment could be explained by mean discharge during the fry period. However, the data could not fully rule out the alternate hypotheses that recruitment was related to mean discharge in the Athabasca River over the entire year or during winter. Several mechanisms are hypothesized to explain the positive relationship, including increased space in preferred open-water habitat or greater food production from nutrient inputs or wetted area. The observed correlation between river discharge and walleye recruitment can be used to help us understand water management planning on the Athabasca River.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".