Challenges for Diadromous Fishes in a Dynamic Global Environment
Bibliographic record
Abstract
Abstract.-An evaluation of the history of efforts to reverse the long-term decline of Pacific salmon in western North America provides instructive policy lessons for the potential recovery of diadromous fishes throughout the world. From California to southern British Columbia, wild runs of Pacific salmon have universally declined and many have disappeared. Billions have been spent in so-far failed attempts to reverse the decline in response to the requirements of the U.S. Endangered Species Act, the Canadian Species at Risk Act, or other laws or policies. The annual expenditure of hundreds of millions of dollars continues, but a sustainable future for wild salmon in this region of North America remains elusive. Despite documented public support for restoring wild salmon, the long-term prognosis for a sustainable future appears problematic. After considering various policy options to increase the numbers of wild salmon and other diadromous species, the major lessons learned were (1) the rules of commerce, especially trends in international commerce and trade, tend to put relentless downward pressure on their numbers; (2) competition for critical natural resources, especially for high quality water, will continue to be great and will work to constrain the numbers of most diadromous species; (3) the aggregate demands of humans will continue to swell, thus tending to limit the available habitat for diadromous species; (4) individual and collective preferences directly determine the future of diadromous species, and there appears to be little indication of a massive and widespread change in those individual and collective preferences; (5) there is an almost insurmountable tendency on the part of elected and appointed officials to avoid explicitly confronting difficult, divisive ecological policy issues; and (6) there is a systematic, often subtle, pressure on fisheries scientists, managers, and analysts to avoid explicitly conveying unpleasant facts to the public, senior bureaucrats, and elected or appointed officials. Fisheries biologists and others continue to craft restoration plans, but an easy, effective approach has yet to emerge that will actually restore and sustain most runs of wild salmon in the region. For most diadromous fishes, restoration options exist that offer both ecological viability and appreciably reduced social disruption, but these options also tend to have more modest restoration objectives.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".