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Record W4388527258 · doi:10.1139/facets-2021-0202

Recent and projected climate change–induced expansion of Atlantic halibut in the Northwest Atlantic

2023· article· en· W4388527258 on OpenAlexaffvenueabout
Andrew N. Czich, Ryan R. E. Stanley, Trevor S. Avery, Cornelia E. den Heyer, Nancy L. Shackell

Bibliographic record

VenueFACETS · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of OceanographyAcadia University
Fundersnot available
KeywordsHalibutHippoglossus hippoglossusClimate changeGlobal warmingHabitatAbundance (ecology)FisheryEnvironmental scienceEffects of global warmingPopulationRange (aeronautics)OceanographyGeographyEcologyBiologyFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

With the influence of climate change on marine systems expanding, climate adaptation will be fundamental for the future of fisheries management. An exponential increase in Atlantic halibut Hippoglossus hippoglossus landings over the past decade has coincided with warming ocean temperatures. Here, we explore how historical changes in abundance have been linked to changing thermal habitat conditions and project trends with a warming climate under different emissions scenarios. From 1990 to 2018, available thermal habitat increased by 11.6 ± 7.35% and growing degree days have increased by 13.5 ± 7.86 °C·days across the region. With warming, the probability of occurrence is projected to increase up to 20.5% in Canada by 2085 under RCP 8.5 for Atlantic halibut. Our results suggest that shifting patterns of halibut distribution and abundance are linked to thermal conditions and that continued warming will likely continue to enhance habitat conditions, leading to increased abundance in the Canadian range. Collectively, these results illustrate the influence of shifting environmental conditions on population dynamics and emphasize the importance of adaptive management practices in a dynamic future climate.

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 score0.030
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.287
Teacher spread0.231 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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