Lake Winnipeg Commercial Fishery Quota Entitlement System
Bibliographic record
Abstract
"Lake Winnipeg is one of Manitoba's major commercial fisheries, averaging harvest of 5.8 million kg annually of walleye, sauger, whitefish, northern pike, and perch. Because of economic, biological, and market constraints, increases in lake quota or subsidization programs for fishermen are not the solution. Fisheries Branch has worked with Lake Winnipeg commercial fishermen to develop a system of quota entitlement which maintains overall lake quota but allows flexibility in distribution of individual quotas of fishermen. Introduced in 1985, the quota entitlement system on Lake Winnipeg was intended to result in a decrease in total number of fishermen. It was anticipated that gross incomes would increase, utilization of capital equipment would improve, unit harvesting costs would decline, and consequently fishermen's incomes would increase, creating an opportunity for increase in economic sustainability. The lake quota, however, would still be maintained to prevent overharvest. This paper will present results of an evaluation conducted in 1991 to determine whether the program was meeting these objectives."
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".