Expert Judgments Regarding Risks Associated with Salmon Aquaculture Practices in British Columbia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Making sound decisions about managing ecological risks necessarily involves relying on judgments by technical specialists informed by the best available scientific evidence. Yet, organizing those judgments in ways to assess the relative risks of different components of a technology, and considering priorities in managing those risks, is a difficult and under‐explored aspect of environmental management. In this study, we elicited the judgments of scientists associated with the salmon aquaculture industry in British Columbia in order to learn their expert viewpoints of potential risks. This paper presents survey results regarding structured judgments provided by scientists engaged in studies associated with aquaculture or preserving wild stocks of Pacific salmon species. There were statistically significant differences regarding judgments of the risks of various current aquaculture practices on wild salmon stocks. It was possible to rank the means of scientific judgment scores to prioritize these risks. Differences in rankings were location and context specific.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it