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Record W4413948532 · doi:10.1139/facets-2024-0348

Barriers and opportunities for the effective management of cumulative effects in salmon ecosystems in British Columbia, Canada

2025· article· en· W4413948532 on OpenAlexaffvenueabout
Marta E. Ulaski, Jonathan W. Moore, Deborah Carlson, Kai Fig Taddei, Kevin Kriese, Julian Griggs, Cathryn Clarke Murray, Megan S. Adams, Kyle L. Wilson, Andrea J. Reid, Nigel C. Sainsbury, Sara E. Cannon, Emma Griggs, Tara G. Martin

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans CanadaInstitute on GovernanceSimon Fraser University
Fundersnot available
KeywordsEcosystemCumulative effectsEcosystem-based managementEnvironmental resource managementFisheryEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

The cumulative effects of climate change and human activities pose major challenges for environmental management, a problem exemplified by Pacific salmon ecosystems. We offer an integrative treatment of both the science and policy levers of cumulative effects and reveal the sheer complexity of effective governance of salmon ecosystems in British Columbia, Canada. We then present and examine a hypothetical conceptualization of cumulative effects and their governance in salmon ecosystems to highlight several barriers and opportunities. We find that the progressive degradation of many salmon habitats appears to be enabled by the current policy approach through scarce monitoring, ineffective assessment, lack of legal limits, and isolated decision-making. At the same time, climate change magnifies the urgency of effective management as human activities act cumulatively with climate change impacts. However, our synthesis also highlights opportunities with existing but underused policy levers within Crown and Indigenous governance, as well as local co-governance arrangements, that could improve salmon ecosystem management. Although positive steps have been made toward managing several stressors, the current challenges facing Pacific salmon underscore the need for a fundamental shift in the treatment of cumulative effects.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.207
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2025
Admission routes3
Has abstractyes

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