Consideration of uncertainties in environmental science and management with examples from Pacific salmon
Bibliographic record
Abstract
An informal review of the history of new quantitative methods in environmental science, including environmental risk assessment, shows about a 10- to 20-year lag in wide acceptance of such methods by management agencies. To reduce that lag time as innovative methods continue to emerge, environmental scientists will need to work much more intensively with communications specialists on better ways to explain risk analyses and decision-making strategies to non-technical decision makers and the public. Four key uncertainties make such communication difficult: (1) natural variability in both physical and biological processes, (2) imperfect data arising from observation error (i.e., measurement error), (3) incomplete understanding of an environmental system's structure and dynamics, and (4) outcome uncertainty (deviations between realized outcomes and management targets). These uncertainties create risks -- risks to natural populations as well as to people who use them. Examples of these four sources of uncertainty are presented here for Pacific salmon (Oncorhynchus spp.). One promising framework for explicitly taking such uncertainties into account was initially developed in the early 1990s by scientific advisors to the International Whaling Commission. They built stochastic models, which essentially were comprehensive formal decision analyses, to derive management procedures (i.e., sampling designs for collecting data, methods to analyze those data, and state-dependent harvest-control rules for use by managers) that were robust to all the uncertainties considered. This method of "Management Strategy Evaluation" or "Management Procedure Evaluation" is now considered the "gold standard" for conducting risk assessments and making risk-management decisions in marine fisheries.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".