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Record W4408858150 · doi:10.1029/2024gb008358

Biological Responses to Ocean Acidification Are Changing the Global Ocean Carbon Cycle

2025· article· en· W4408858150 on OpenAlexaff
Reese C. Barrett, Brendan R. Carter, Andrea J. Fassbender, Bronte Tilbrook, Ryan J. Woosley, Kumiko Azetsu‐Scott, Richard A. Feely, Catherine Goyet, Masao Ishii, Akihiko Murata, Fı́z F. Pérez

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersNational Oceanic and Atmospheric Administration
KeywordsOcean acidificationCarbon cycleOceanographyEnvironmental scienceEffects of global warming on oceansBiological pumpGlobal changeCarbon fluxClimate changeGeologyGlobal warmingEcosystemEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Increased oceanic uptake of CO 2 due to rising anthropogenic emissions has caused lowered pH levels (ocean acidification) that are hypothesized to diminish biotic calcification and reduce the export of total alkalinity ( A T ) as carbonate minerals from the surface ocean or their burial in coastal sediments. This “CO 2 ‐biotic calcification feedback” is a negative feedback on atmospheric CO 2 , as elevated levels of surface A T increase the ocean's capacity to uptake CO 2 . We detect signatures of this feedback in the global ocean for the first time using repeat hydrographic measurements and seawater property prediction algorithms. Over the course of the past 30 years, we find an increase in global surface A T of 0.072 ± 0.023 μmol kg −1 yr −1 , which would have caused approximately 20 Tmol of additional A T to accumulate in the surface ocean. This finding suggests that anthropogenic CO 2 emissions are measurably perturbing the cycling of carbon on a planetary scale by disrupting biological patterns. More observations of A T would be required to understand the effects of this feedback on a regional basis and to fully characterize its potential to reduce the efficiency of marine carbon dioxide removal technology.

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)0.001
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 score0.013
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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

Citations10
Published2025
Admission routes1
Has abstractyes

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