Restoring Wild Salmon to the Pacific Northwest: Framing the Risk Question
Bibliographic record
Abstract
In the Pacific Northwest of the United States, it is urgent to assess accurately the various options proposed to restore wild salmon. For the past 150 years, a variety of analytic approaches have been employed to assess the ecological consequences of salmon management options. Each approach provided useful information to decision makers, but each also suffered from limitations, some relatively minor, others sufficient to undermine any potential utility. Risk assessment has become the most widely used analytic approach to evaluate environmental policy options. To date its use in ecological policy has been largely constrained to evaluating relatively simple technical questions (e.g., regulatory actions associated with specific chemicals or hazardous waste sites). Recently, however, there has been interest in applying risk assessment to more complex ecological policy problems (e.g., the decline of wild salmon in the Pacific Northwest). Although its use has become commonplace and widely accepted, especially among regulatory and land management agencies, risk assessment remains contentious. The most heated debates revolve around delineating the specific meaning of risk; that is, framing the risk “question” to be answered.
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 | 0.034 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.014 | 0.013 |
| 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".