MétaCan
Menu
Back to cohort
Record W2498595760 · doi:10.1111/fog.12171

Changes in albacore tuna habitat in the northeast Pacific Ocean under anthropogenic warming

2016· article· en· W2498595760 on OpenAlexaffabout
James R. Christian, John Bradley Holmes

Bibliographic record

VenueFisheries Oceanography · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsAlbacoreHabitatEnvironmental scienceRange (aeronautics)Climate changeTunaOceanographySea surface temperatureFisheryGlobal warmingEffects of global warming on oceansNicheOceanic climateClimatologyGeographyFish <Actinopterygii>EcologyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract Albacore tuna are widespread in the North Pacific Ocean and the basis of an important commercial fishery. These fish live mainly within a fairly narrow thermal niche range defined by sea surface temperature ( SST ) isotherms between 14 and 19°C. Because the fish's thermal range coincides with strong latitudinal temperature gradients off the northwest coast of North America, there is a great deal of seasonal and interannual variability in the distribution of these fish, and a significant potential for a new habitat in this region with anthropogenic climate change. We use historical catch and effort data from the Canadian troll fleet to define the fish's thermal niche, and document observed shifts in distribution associated with interannual climate variability. We then use an ensemble of climate model simulations from the Coupled Model Intercomparison Project to estimate northward extension of the potential habitat under anthropogenic warming scenarios. A potential new habitat is about half a million square kilometres even under a moderate mitigation scenario. Estimates are smaller for some months of the year in which the fishery is conducted, but as well as opening up new regions, the length of season in which the fishery is active may be extended in the northern part of the range. However, much of the potential new habitat will be in oceanic waters with relatively low productivity. Our estimated area of potential habitat is based on the fish's thermal niche and assumes that other biologically important factors such as food will not be limiting.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.997

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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.

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

Citations20
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueFisheries OceanographySame topicMarine and fisheries researchFrench-language works237,207