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Record W3196527892 · doi:10.5670/oceanog.2021.308

Ocean Regime Shift is Driving Collapse of the North Atlantic Right Whale Population

2021· article· en· W3196527892 on OpenAlexfundno aff
Erin Meyer‐Gutbrod, Charles H. Greene, Kimberley T. A. Davies, David G. Johns

Bibliographic record

VenueOceanography · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UKCanadian Wildlife FederationUniversity of WashingtonPew Charitable Trusts
KeywordsRight whaleOceanographyWhaleFisheryEndangered speciesPopulationForagingClimate changeGeographyGulf StreamHabitatEnvironmental scienceEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Ocean warming linked to anthropogenic climate change is impacting the ecology of marine species around the world. In 2010, the Gulf of Maine and Scotian Shelf regions of the Northwest Atlantic underwent an unprecedented regime shift. Forced by climate-driven changes in the Gulf Stream, warm slope waters entered the region and created a less favorable foraging environment for the endangered North Atlantic right whale population. By mid-decade, right whales had shifted their late spring/summer foraging grounds from the Gulf of Maine and the western Scotian Shelf to the Gulf of St. Lawrence. The population also began exhibiting unusually high mortality in 2017. Here, we report that climate-driven changes in ocean circulation have altered the foraging environment and habitat use of right whales, reducing the population’s calving rate and exposing it to greater mortality risks from ship strikes and fishing gear entanglement. The case of the North Atlantic right whale provides a cautionary tale for the management of protected species in a changing ocean.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.202
Teacher spread0.195 · 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

Citations139
Published2021
Admission routes1
Has abstractyes

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