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Record W4391969930 · doi:10.1038/s43247-024-01251-8

Major changes in fish thermal habitat diversity in Canada’s Arctic lakes due to climate change

2024· article· en· W4391969930 on OpenAlexafffundabout
Daniel P. Gillis, Charles K. Minns, Steven E. Campana, Brian J. Shuter

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of Natural Resources and ForestryUniversity of TorontoFisheries and Oceans Canada
FundersFisheries and Oceans CanadaHáskóli ÍslandsUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaMcGill UniversityNational Science Foundation
KeywordsClimate changeArcticFish <Actinopterygii>HabitatDiversity (politics)The arcticGeographyEnvironmental scienceEcologyFisheryOceanographyPhysical geographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Climate warming is a major disruptor of fish community structure globally. We use large-scale geospatial analyses of 447,077 Canadian Arctic lakes to predict how climate change would impact lake thermal habitat diversity across the Arctic landscape. Increases in maximum surface temperature (+2.4–6.7 °C), ice-free period (+14–38 days), and thermal stratification presence (+4.2–18.9%) occur under all climate scenarios. Lakes, currently fishless due to deep winter ice, open up; many thermally uniform lakes become thermally diverse. Resilient coldwater habitat supply is predicted; however, thermally diverse lakes shift from providing almost exclusively coldwater habitat to providing substantial coolwater habitat and previously absent warmwater habitat. Across terrestrial ecozones, most lakes exhibit major shifts in thermal habitat. The prevalence of thermally diverse lakes more than doubles, providing refuge for coldwater taxa. Ecozone-specific differences in the distribution of thermally diverse and thermally uniform lakes require different management strategies for adapting fish resource use to climate 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 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 score0.774
Threshold uncertainty score0.924

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 science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.217
Teacher spread0.189 · 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

Citations5
Published2024
Admission routes3
Has abstractyes

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