Distinct patterns of movement in monthly space use across Lake Winnipeg by a population of south basin walleye
Bibliographic record
Abstract
Lake Winnipeg hosts North America’s second largest commercial fishery for walleye ( Sander vitreus (Mitchill, 1818)); however, little is currently known regarding walleye distribution throughout the lake. Here we identify two movement strategies for adult female walleye (migrant and resident) and describe patterns in monthly space use over 2 years. We used permissible home range estimators to determine monthly home range (95%), core range (50%), and associated mean locations. Mean locations showed that migratory walleye occupied more northern regions of the lake during late summer into fall (August and September) and were more southern during winter (November to March) and spring (April to June), overlapping residents. Migrants exhibited larger ranges during June, July, and October and shared similar ranges to residents when found at similar latitudes. Putative repeat spawning within the Red River was marginally more frequent among migrants compared to residents. This study describes two movement strategies of walleye within the south basin of Lake Winnipeg, possibly arising from multiple factors including water clarity, prey density, and temperature gradients. Results presented here provide information on the timing of movement and the spatial distribution of fish, which may be incorporated into a spatiotemporal based approach for fisheries management and stock assessment.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".