A daily simulation model of catch, mortality and escapement for Fraser-Thompson steelhead stocks
Bibliographic record
Abstract
A computer model was constructed to simulate the effects of alternative fishing patterns on catch, mortality and escapement of interior steelhead stocks from the FraserThompson system, British Columbia. The model simulates the catch and mortality associated with various marine and in-river fisheries on a daily time step as fish migrate from the northern tip of Vancouver Island, through marine fishing areas, and up the Fraser River towards overwintering areas and spawning tributaries. Key inputs include the fishing schedule, migration speed, catch rates, and mortality rates of caught fish. As with similar models built for Fraser River sockeye stocks, the primary use of the model will be in the pre-season, in understanding the relative changes in catch, mortality and escapement expedcted with alternative fishing regimes. Testing of the model during the 1999 season led to some refinements and modifications as discussed in this paper, and in particular revealed limitations resulting from poor data for many parameters. The model is useful as a tool for exploring relative changes in catch rates expected for various fishing patterns, and for identifying critical data constraints. However, its value as a reliable predictive model is limited by the paucity of data available for steelhead and the need to use data for other species.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".