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Record W3008710125 · doi:10.1002/lno.11421

Freshening of the western Arctic negates anthropogenic carbon uptake potential

2020· article· en· W3008710125 on OpenAlexaboutno aff
Ryan J. Woosley, Frank J. Millero

Bibliographic record

VenueLimnology and Oceanography · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsEnvironmental scienceDissolved organic carbonSink (geography)ArcticAlkalinityCarbon dioxideTotal inorganic carbonOceanographyCarbon cycleCarbon sinkThe arcticCarbon fibersDilutionEnvironmental chemistryCarbon dioxide in Earth's atmosphereEcologyClimate changeEcosystemChemistryGeologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract As human activities increase the atmospheric concentration of carbon dioxide (CO 2 ), the oceans are known to absorb a significant portion. The Arctic Ocean has long been considered to have enormous potential to sequester anthropogenic CO 2 , and mitigate emissions. The frigid waters make CO 2 more soluble, and as sea ice melts, greater surface area is exposed to absorb CO 2 . However, sparse data have made quantifying the amount of anthropogenic CO 2 in the Arctic difficult, stimulating much debate over the basin's contribution to CO 2 sequestration from the atmosphere. Using three separate cruises in 1994, 2005, and 2015 in the Canada and Makarov basins, we analyze the decadal variability in anthropogenic CO 2 uptake in the central western Arctic. Here we show, from direct carbon system measurements spanning two decades, that despite increased atmospheric CO 2 , total dissolved inorganic carbon has actually decreased, with minimal anthropogenic CO 2 uptake. The reduction in dissolved CO 2 results from a dilution of total alkalinity by increased freshwater supply, particularly river water. Changes in the freshwater budget of the western Arctic override its uptake potential, resulting in a weak sink, or possibly source of CO 2 .

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)0.000
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 score0.004
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.184
Teacher spread0.176 · 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

Citations49
Published2020
Admission routes1
Has abstractyes

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