The opportunity cost of information: an economic framework for understanding the balance between assessment and control in sea lamprey (Petromyzon marinus) management
Bibliographic record
Abstract
Fisheries managers must make trade-offs between competing management actions; however, the inherent trade-offs associated with information gathering are seldom explicitly considered. Incorporating economics into management decisions at the outset can aid managers in explicitly considering the trade-off between collecting more information to guide management and taking management actions. We use control of the invasive sea lamprey ( Petromyzon marinus ) in the Laurentian Great Lakes to illustrate how budget constraints shape this trade-off. Economic theory is used to frame previous empirical work showing that reducing the allocation of resources to conducting assessment, and thereby freeing resources for treatment, would result in a greater reduction of sea lamprey populations — the overarching management objective. The optimal allocation of resources between assessment and control depends on the total budget, the relative cost of each management activity, the marginal reduction in uncertainty associated with increased assessment, and the marginal effectiveness of increased treatment. Formal incorporation of prior information can change the optimal allocation of resources. The approach presented here is generally applicable to a wide range of fishery management and research questions.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".