Distinguishing benchmarks of biological status from management reference points: A case study on Pacific salmon in Canada
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
SUMMARY For fisheries with multiple, competing objectives, identifying and applying reference points for management can present difficult trade-offs between long-term biological and shorter-term socioeconomic considerations. The term biological benchmarks is proposed to demarcate zones of population status based on conservation and production considerations. These scientifically derived benchmarks contrast with management reference points that generally require additional shorter-term socioeconomic information best obtained through public consultations. This paper illustrates the distinction between biological benchmarks and management reference points with a case study on Pacific salmon ( Oncorhynchus spp.). In Canada, the management and assessment of wild Pacific salmon are guided by a major 2005 conservation policy, which calls for the identification of biological benchmarks to categorize status of demographically isolated populations, and decision-support tools, such as management reference points, to integrate biological information with appropriate social and economic information. In the Fraser River (British Columbia, Canada), the selection of management reference points for sockeye salmon ( O. nerka ) fisheries explicitly considered trade-offs between the probability of meeting long-term biological objectives on component populations and harvest objectives on population aggregates. Decisions about reference points were made in a consultative process that included extensive stakeholder engagement. Other agencies are urged to distinguish biological benchmarks from management reference points to ensure transparency in the relative influence of biological versus socioeconomic information in decision making.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it