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Record W3031015972 · doi:10.1038/s41467-020-16530-z

Controls on surface water carbonate chemistry along North American ocean margins

2020· article· en· W3031015972 on OpenAlexaff
Wei‐Jun Cai, Yuan‐Yuan Xu, Richard A. Feely, Rik Wanninkhof, Bror Jönsson, Simone R. Alin, Leticia Barbero, Jessica Cross, Kumiko Azetsu‐Scott, Andrea J. Fassbender, Brendan R. Carter, Li‐Qing Jiang, Pierre Pepin, Baoshan Chen, Najid Hussain, Janet J. Reimer, Liang Xue, J. Salisbury, José Martín Hernández‐Ayón, Chris Langdon, Qian Li, Adrienne J. Sutton, Chen‐Tung Arthur Chen, D. K. Gledhill

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersNOAA Pacific Marine Environmental LaboratoryClimate Program OfficeNational Oceanic and Atmospheric AdministrationU.S. Department of CommerceNational Science Foundation
KeywordsUpwellingCarbonateOcean acidificationOceanographyAtmosphere (unit)GeologySaturation (graph theory)Carbonate IonZonal and meridionalEnvironmental scienceChemistrySeawaterGeography

Abstract

fetched live from OpenAlex

Abstract Syntheses of carbonate chemistry spatial patterns are important for predicting ocean acidification impacts, but are lacking in coastal oceans. Here, we show that along the North American Atlantic and Gulf coasts the meridional distributions of dissolved inorganic carbon (DIC) and carbonate mineral saturation state (Ω) are controlled by partial equilibrium with the atmosphere resulting in relatively low DIC and high Ω in warm southern waters and the opposite in cold northern waters. However, pH and the partial pressure of CO 2 ( p CO 2 ) do not exhibit a simple spatial pattern and are controlled by local physical and net biological processes which impede equilibrium with the atmosphere. Along the Pacific coast, upwelling brings subsurface waters with low Ω and pH to the surface where net biological production works to raise their values. Different temperature sensitivities of carbonate properties and different timescales of influencing processes lead to contrasting property distributions within and among margins.

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)0.000
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 score0.966
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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

Citations136
Published2020
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicOcean Acidification Effects and ResponsesFrench-language works237,207