Bibliographic record
Abstract
Finding the factors that determine recruitment for various fishes has been the primary objective in fisheries science for over a century. Another goal of fisheries science has been to further the understanding of how individual stocks interact both among themselves and with the environment. This thesis attempts to explore both recruitment variability and the stock structure of walleye (Sander vitreus Mitchell 1818) in western Lake Erie. Year-class strength has been associated with variables that describe the environment of a fish population. Walleye are a percid species whose populations are influenced by environmental factors and thus resource managers on Lake Erie have a long tradition of investigating environment-recruitment relationships. This thesis reviews historic relationships between environmental variables and walleye recruitment in Lake Erie and then re-evaluates the influence of environmental factor using a data set that covers the period of 1987--2001. None of the historic relationships between walleye recruitment and environmental variables held during the 15-year period of study. Environmental variables overall explained very little of the interannual variation in walleye recruitment in Lake Erie. (Abstract shortened by UMI.)Dept. of Biological Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .B37. Source: Masters Abstracts International, Volume: 44-03, page: 1275. Thesis (M.Sc.)--University of Windsor (Canada), 2005.
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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 | 0.001 |
| 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".