REDUCING THE RISK OF FISHERY RESOURCE DISASTERS: A BIOECONOMIC APPROACH TO SUSTAINABLE RESOURCE MANAGEMENT<sup>1</sup>
Bibliographic record
Abstract
Abstract: Recognition is growing that fisheries must be both ecologically and commercially sustainable. The bioeconomic models proposed herein constitute an analytic framework capable of integrating the ethics and Societal values associated with fisheries preservation. Specifically, we focus on the normalized optimal (equilibrium) fish population, z*, a dimensionless variable representing biomass as a proportion of environmental capacity. We model z* as a function of (a) the dimensionless “bionomic growth ratio”, γ, which is the ratio of the discount rate to the intrinsic population growth rate, and (b) the preservation coefficient, Ω, which is the ratio of the preservation value (a measure of Society's value for the stock) to price, assuming that the population growth rate and intrinsic growth rate are fixed. It is shown that increasing Ω significantly impacts z*, particularly for moderate values of γ (2 γ 4). Finally, stochastic population models are used to analyze the risk of a fish stock collapse due to harvesting pressures. The bioeconomic models and simulations herein described improve the accuracy and reliability of maximum sustainable yield management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".