Growth rate and condition of walleye (<i>Sander vitreus</i>), sauger (<i>Sander canadensis</i>), and dwarf walleye in a large Canadian lake
Bibliographic record
Abstract
The second largest inland walleye (Sander vitreus (Mitchill, 1818)) and sauger (Sander canadensis (Griffith and Smith, 1834)) fishery in Canada is found in Lake Winnipeg, Manitoba. To manage the fishery for a sustainable future, the growth and condition of these fish must be understood. Objectives were to (1) examine baseline growth and condition of walleye and sauger in Lake Winnipeg, (2) evaluate variation between the North and South basins, and (3) contribute observational findings on the distribution of dwarf walleye. Gill nets were set to catch walleye, sauger, and dwarf walleye throughout both basins at various locations and in all seasons during 2010 and 2011. North Basin walleye and sauger had higher growth rates and condition relative to the South Basin. This may be due to differential exploitation rates or diets such as the consumption of invasive rainbow smelt (Osmerus mordax (Mitchell, 1814)) in the North Basin and not in the South Basin. Dwarf walleye were observed more frequently in the South Basin than in the North Basin. Overall, this study provides important baseline data on the growth and condition of walleye and sauger populations prior to invasion of the spiny waterflea (Bythotrephes longimanus Leydig, 1860) and zebra mussels (Dreissena polymorpha (Pallas, 1771)).
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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 | 0.001 | 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".