Should we adapt nature to climate change? Weighing the risks of selective breeding in Pacific salmon
Bibliographic record
Abstract
This paper uses the case of genomics-assisted selective breeding in Pacific salmon hatcheries to investigate how people weigh the risks of adapting nature to changing climate conditions. Drawing on 105 interviews with people involved in salmon management, this study embeds risk assessments of selective breeding in the context of present interventions into salmon life cycles. While responses to novel technologies are frequently plotted along a support-opposition continuum, the debate over selective breeding Pacific salmon is multivalent, with respondents supporting selective breeding in some contexts while opposing it in others. Nearly half of respondents supported selective breeding to fix the mistakes of past interventions and rewild salmon. Given that past problems have stemmed from technological responses, these findings paradoxically suggest that further interventions may not necessarily be perceived as violating values of naturalness or wildness. Genomic technologies offer new pathways for climate adaptation. In doing so, they expand ethical debates about the role of humans and novel technologies in conserving and managing wildlife.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".