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Record W4366823707 · doi:10.1002/fsh.10923

Kings of the North: Bridging Disciplines to Understand the Effects of Changing Climate on Chinook Salmon in the Arctic–Yukon–Kuskokwim Region

2023· article· en· W4366823707 on OpenAlexaffabout
Megan L. Feddern, Erik R. Schoen, Rebecca Shaftel, Curry J. Cunningham, Craig Chythlook, Brendan Connors, Alyssa Murdoch, Vanessa R. von Biela, Brooke Woods

Bibliographic record

VenueFisheries · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsWildlife Conservation Society CanadaCarleton UniversityFisheries and Oceans Canada
FundersAlaska Sustainable Salmon FundInternational Arctic Research Center, University of Alaska, Fairbanks
KeywordsChinook windBridging (networking)ArcticThe arcticGeographyFisheryBiologyEcologyOceanographyOncorhynchusComputer scienceFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

Abstract Understanding how species are responding to environmental change is a central challenge for stewards and managers of fish and wildlife who seek to maintain harvest opportunities for communities and Indigenous peoples. This is a particularly daunting but increasingly important task in remote, high-latitude regions where environmental conditions are changing rapidly and data collection is logistically difficult. The Arctic–Yukon–Kuskokwim (AYK) region encompasses the northern extent of the Chinook Salmon Oncorhynchus tshawytscha range where populations are experiencing rapid rates of environmental change across both freshwater and marine habitats due to global climate change. Climate–salmon interactions in the AYK region are a particularly pressing issue as many local communities have a deep reliance on a subsistence way of life. Here, we synthesize perspectives shared at a recent workshop on Chinook Salmon declines in the AYK region. The objectives were to discuss current understandings of climate–Chinook Salmon interactions, develop a set of outstanding questions, review available data and its limitations in addressing these questions, and describe the perspectives expressed by participants in this workshop from diverse backgrounds. We conclude by suggesting pathways forward to integrate different types of information and build relationships among communities, academic partners, and fishery management agencies.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.550

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.216
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2023
Admission routes2
Has abstractyes

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