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Record W4406417288 · doi:10.1038/s41598-024-84537-3

Delayed onset of ocean acidification in the Gulf of Maine

2025· article· en· W4406417288 on OpenAlexafffund
Joseph Stewart, B. Williams, M. LaVigne, Alan D. Wanamaker, Aaron Strong, Brittany Jellison, Nina M. Whitney, Diana L. Thatcher, Laura F. Robinson, Jochen Halfar, Walter H. Adey

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of Toronto
FundersNOAA ResearchNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsOceanographyOcean acidificationEnvironmental scienceGeologyClimate change

Abstract

fetched live from OpenAlex

Abstract The Gulf of Maine holds significant ecological and economic value for fisheries and communities in north-eastern North America. However, there is apprehension regarding its vulnerability to the effects of increasing atmospheric CO 2 . Substantial recent warming and the inflow of low alkalinity waters into the Gulf of Maine have raised concerns about the impact of ocean acidification on resident marine calcifiers (e.g. oysters, clams, mussels). With limited seawater pH records, the natural variability and drivers of pH in this region remain unclear. To address this, we present coastal water pH proxy records using boron isotope (δ 11 B) measurements in long-lived, annually banded, crustose coralline algae (1920–2018 CE). These records indicate seawater pH was low (~ 7.9) for much of the last century. Contrary to expectation, we also find that pH has increased (+ 0.2 pH units) over the past 40 years, despite concurrent rising atmospheric CO 2 . This increase is attributed to an increased input of high alkalinity waters derived from the Gulf Stream. This delayed onset of ocean acidification is cause for concern. Once ocean circulation-driven buffering effects reach their limit, seawater pH decline may occur swiftly. This would profoundly harm shellfisheries and the broader Gulf of Maine ecosystem.

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)0.003
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 score0.046
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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