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Record W4297937001 · doi:10.1126/science.abo0383

Climate change drives rapid decadal acidification in the Arctic Ocean from 1994 to 2020

2022· article· en· W4297937001 on OpenAlexaboutno aff
Di Qi, Zhangxian Ouyang, Liqi Chen, Yingxu Wu, Ruibo Lei, Baoshan Chen, Richard A. Feely, Leif G. Anderson, Wenli Zhong, A. A. Polukhin, Yixing Zhang, Yongli Zhang, Haibo Bi, Xinyu Lin, Yiming Luo, Yanpei Zhuang, Jianfeng He, Jianfang Chen, Wei‐Jun Cai

Bibliographic record

VenueScience · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNOAA Pacific Marine Environmental LaboratoryJST-Mirai ProgramNational Key Research and Development Program of ChinaOcean Acidification ProgramLamont-Doherty Earth Observatory, Columbia UniversityNational Oceanic and Atmospheric AdministrationNational Natural Science Foundation of ChinaWoods Hole Oceanographic InstitutionJapan Agency for Marine-Earth Science and TechnologyNatural Science Foundation of Fujian ProvinceChinese Academy of SciencesNational Science Foundation
KeywordsOcean acidificationAragoniteOceanographySea iceEnvironmental scienceArcticAlkalinityClimate changeArctic sea ice declineArctic ice packSeawaterArctic geoengineeringEffects of global warming on oceansCarbon dioxideGlobal warmingClimatologyCanada BasinGeologyAntarctic sea iceEcologyChemistryCalciteBiology

Abstract

fetched live from OpenAlex

The Arctic Ocean has experienced rapid warming and sea ice loss in recent decades, becoming the first open-ocean basin to experience widespread aragonite undersaturation [saturation state of aragonite (Ω arag ) < 1]. However, its trend toward long-term ocean acidification and the underlying mechanisms remain undocumented. Here, we report rapid acidification there, with rates three to four times higher than in other ocean basins, and attribute it to changing sea ice coverage on a decadal time scale. Sea ice melt exposes seawater to the atmosphere and promotes rapid uptake of atmospheric carbon dioxide, lowering its alkalinity and buffer capacity and thus leading to sharp declines in pH and Ω arag . We predict a further decrease in pH, particularly at higher latitudes where sea ice retreat is active, whereas Arctic warming may counteract decreases in Ω arag in the future.

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 score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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

Citations129
Published2022
Admission routes1
Has abstractyes

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