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Record W2801024111 · doi:10.1029/2017gb005738

Rapid Changes in Anthropogenic Carbon Storage and Ocean Acidification in the Intermediate Layers of the Eurasian Arctic Ocean: 1996–2015

2018· article· en· W2801024111 on OpenAlexaboutno aff
Adam Ulfsbo, Elizabeth M. Jones, Núria Casacuberta, Meri Korhonen, Benjamin Rabe, Michael Kärcher, Steven van Heuven

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersSvenska Forskningsrådet FormasRoyal Swedish Academy of Sciences
KeywordsStructural basinOceanographyArcticOcean acidificationCarbonateCarbon fibersGeologyTotal inorganic carbonTRACERBoundary currentCanada BasinEnvironmental scienceOcean currentCarbon dioxideClimate changeGeomorphologyEcologyChemistry

Abstract

fetched live from OpenAlex

The extended multiple linear regression technique is used to determine changes in anthropogenic carbon in the intermediate layers of the Eurasian Basin based on occupations from four cruises between 1996 and 2015. The results show a significant increase in basin‐wide anthropogenic carbon storage in the Nansen Basin (0.44–0.73 ± 0.14 mol C·m −2 ·year −1 ) and the Amundsen Basin (0.63–1.04 ± 0.09 mol C·m −2 ·year −1 ). Over the last two decades, inferred changes in ocean acidification (0.020–0.055 pH units) and calcium carbonate desaturation (0.05–0.18 units) are pronounced and rapid. These results, together with results from carbonate‐dynamic box model simulations and 129 I tracer distribution simulations, suggest that the accumulation of anthropogenic carbon in the intermediate layers of the Eurasian Basin are consistent with increasing concentrations of anthropogenic carbon in source waters of Atlantic origin entering the Arctic Ocean followed by interior transport. The dissimilar distributions of anthropogenic carbon in the interior Nansen and Amundsen Basins are likely due to differences in the lateral ventilation of the intermediate layers by the return flows and ramifications of the boundary current along the topographic boundaries in the Eurasian Basin.

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 score0.005
Threshold uncertainty score0.664

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 head0.011
GPT teacher head0.228
Teacher spread0.218 · 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

Citations28
Published2018
Admission routes1
Has abstractyes

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