Finding a PATH toward Scientific Collaboration: Insights from the Columbia River Basin
Bibliographic record
Abstract
Observed declines in the Snake River basin salmon stocks, listed under the U.S. Endangered Species Act (ESA), have been attributed to multiple causes: the hydrosystem, hatcheries, habitat, harvest, and ocean climate. Conflicting and competing analyses by different agencies led the National Marine Fisheries Service (NMFS) in 1995 to create the Plan for Analyzing and Testing Hypotheses (PATH), a collaborative interagency analytical process. PATH included about 30 fisheries scientists from a dozen agencies, as well as independent participating scientists and a technical facilitation team. PATH had some successes and some failures in meeting its objectives. Some key lessons learned from these successes and failures were to: (1) build trust through independent technical facilitation and multiple levels of peer review (agency scientists, independent participating scientists and an external Scientific Review Panel); (2) clarify critical uncertainties by developing common data sets, detailed sensitivity analyses, and thorough retrospective analyses of the weight of evidence for key alternative hypotheses; (3) clarify advice to decision makers by using an integrated life cycle model and decision analysis framework to evaluate the robustness of potential recovery actions under alternative states of nature; (4) involve key senior scientists with access to decision makers; (5) work closely with policy makers to clearly communicate analyses in nontechnical terms and provide input into the creation of management alternatives; and (6) recognize the trade-off between collaboration and timely completion of assignments.
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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".