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Record W4408040107 · doi:10.5194/bg-22-1115-2025

Long-term impacts of global temperature stabilization and overshoot on exploited marine species

2025· article· en· W4408040107 on OpenAlexaff
Anne L. Morée, Fabrice Lacroix, William W. L. Cheung, Thomas L. Frölicher

Bibliographic record

VenueBiogeosciences · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
Fundersnot available
KeywordsTerm (time)Overshoot (microwave communication)Environmental scienceOceanographyEcologyFisheryClimatologyGeologyBiologyComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

Global warming alters ocean conditions, which can have dramatic consequences for marine species. Yet, the centennial-scale effects and reversibility of habitat viability for marine species, particularly those that are important to fisheries, remain uncertain. Using the Aerobic Growth Index, we quantify the impacts of warming and deoxygenation on the contemporary habitat volume of 46 exploited marine species in novel temperature stabilization and overshoot simulations until 2500. We demonstrate that only around half of the simulated loss of contemporary (1995–2014) habitat volume is realized when warming levels are first reached. Moreover, in an overshoot scenario peaking at 2 °C global warming before stabilizing at 1.5 °C, the maximum decrease in contemporary habitat volume occurs more than 150 years post-peak warming. Species' adaptation may strongly mitigate impacts depending on adaptation rate and pressure. According to our study, marine species will be affected for centuries after temperature stabilization and overshoot, with impacts surpassing those during the transient warming phase.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.011
GPT teacher head0.239
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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