A Simulation-Based Evaluation of In-Season Management Tactics for Anadromous Fisheries: Accounting for Risk in the Yukon River Fall Chum Salmon Fishery
Bibliographic record
Abstract
Abstract Salmon fisheries are managed under uncertainties in abundance, population dynamics, and fishery implementation. These uncertainties create risk, which can be accounted for using probabilistic harvest control rules. Probabilistic control rules ensure management actions are consistent with the level of acceptable risk. Within a fishing season, managers seek to meet escapement objectives by using preseason forecasts and in-season data to set fishery openings and catch targets. We used a stochastic simulation model of the Yukon River fall Chum Salmon Oncorhynchus keta fishery to evaluate the effects on fishery performance of (1) the degree of in-season risk tolerance and (2) two methods for estimating daily abundance projections. We defined risk in terms of the probability of failing to meet escapement objectives, which is consistent with the emphasis on meeting escapement objectives in Alaska's Sustainable Salmon Fisheries Policy. The analysis revealed expected trade-offs between harvest and escapement objectives. However, risk tolerance did not strongly influence fishery performance mainly because outcomes were highly uncertain due to process errors. Average subsistence harvest decreased from 97,000 to 88,000 Chum Salmon and the probability of failing to meet escapement goals in four of the five most recent years decreased from 0.04 to 0.01 when a risk-averse approach was implemented. However, when preseason forecasts were relatively low and close to thresholds where no fishing would be allowed, then a risk-averse approach increased the probability of meeting escapement objectives. A Bayesian approach to estimating the projected run abundance performed similarly to a simpler approach that relied entirely on the preseason forecast until the average first quarter point of the run. Our results suggest that assessment approaches and the degree of in-season risk tolerance are likely less important in determining fishery performance for Yukon River Fall Chum Salmon than are the escapement goals used to manage the fishery. Received November 18, 2013; accepted July 28, 2014
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".