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Record W3012363998 · doi:10.1093/biosci/biaa015

Glacier Retreat and Pacific Salmon

2020· article· en· W3012363998 on OpenAlexafffund
Kara J. Pitman, Jonathan W. Moore, Matthew R. Sloat, Anne H. Beaudreau, Allison Bidlack, Eran Hood, George R. Pess, Nathan J. Mantua, Alexander M. Milner, Valentina Radić, Gordon H. Reeves, Daniel E. Schindler, Diane C. Whited

Bibliographic record

VenueBioScience · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersAssociation of Canadian Universities for Northern StudiesGordon and Betty Moore Foundation
KeywordsOncorhynchusGlacierHabitatFish migrationFisheryEcosystemGlacial periodProductivityGeographyRange (aeronautics)OceanographyEnvironmental scienceEcologyPhysical geographyFish <Actinopterygii>GeologyBiology

Abstract

fetched live from OpenAlex

Glaciers have shaped past and present habitats for Pacific salmon (Oncorhynchus spp.) in North America. During the last glacial maximum, approximately 45% of the current North American range of Pacific salmon was covered in ice. Currently, most salmon habitat occurs in watersheds in which glacier ice is present and retreating. This synthesis examines the multiple ways that glacier retreat can influence aquatic ecosystems through the lens of Pacific salmon life cycles. We predict that the coming decades will result in areas in which salmon populations will be challenged by diminished water flows and elevated water temperatures, areas in which salmon productivity will be enhanced as downstream habitat suitability increases, and areas in which new river and lake habitat will be formed that can be colonized by anadromous salmon. Effective conservation and management of salmon habitat and populations should consider the impacts of glacier retreat and other sources of ecosystem change.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.190
Teacher spread0.179 · 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

Citations75
Published2020
Admission routes2
Has abstractyes

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