MétaCan
Menu
Back to cohort
Record W4321447242 · doi:10.1038/s43247-023-00708-6

Arctic deep-water anoxia and its potential role for ocean carbon sink during glacial periods

2023· article· en· W4321447242 on OpenAlexaboutno aff
Kwangchul Jang, Kyung Sik Woo, Jin‐Kyoung Kim, Seung‐Il Nam

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
FundersNational Research Foundation of KoreaMinistry of Science and ICT, South KoreaMinistry of Oceans and FisheriesKangwon National University
KeywordsOceanographyGeologyGlacial periodArcticMeltwaterDeep ocean waterArctic dipole anomalySea iceArctic sea ice declineCircumpolar deep waterNorth Atlantic Deep WaterDeep seaYounger DryasThermohaline circulationCanada BasinArctic ice packClimate changePaleontologyAntarctic sea ice

Abstract

fetched live from OpenAlex

Abstract Deep water freshening beneath pan-Arctic ice shelves has recently been proposed based on the absence of excess thorium in glacial Arctic sediments. This profound proposal requires scrutiny of Arctic paleohydrology during past glacial periods. Here, we present structural and geochemical results of inorganic authigenic carbonates in deep-sea glacimarine sediments from the Mendeleev Ridge, western Arctic Ocean over the last 76 kyr. Our results suggest that Polar Deep Water in the western Arctic became brackish and anoxic during stadial periods. We argue that sediment-laden hyperpycnal meltwater discharged from paleo-ice sheets filled much of the water column depending upon the density, substantially reducing the salinity and oxygen content of the Polar Deep Water. Our findings suggest that this phenomenon was more extreme in the western Arctic Ocean and may point to the potential role of the western Arctic Ocean as an additional carbon reservoir in the global carbon cycle across glacial-interglacial cycles.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.578

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 studies0.0010.000
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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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 designBench or experimental
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

Citations11
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCommunications Earth & EnvironmentSame topicMethane Hydrates and Related PhenomenaFrench-language works237,207