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Record W2101474062 · doi:10.5194/bg-11-6525-2014

CO <sub>2</sub> and CH <sub>4</sub> in sea ice from a subarctic fjord under influence of riverine input

2014· article· en· W2101474062 on OpenAlexafffund
Odile Crabeck, Bruno Delille, David N. Thomas, Nicolas‐Xavier Geilfus, Søren Rysgaard, Jean‐Louis Tison

Bibliographic record

VenueBiogeosciences · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of CanadaFonds De La Recherche Scientifique - FNRSRoyal SocietyArcticNetUniversity of Manitoba
KeywordsAMP-activated protein kinaseSubarctic climateChemistryEndocrinologyInternal medicineAnimal scienceBiologyOceanographyAMPKBiochemistryGeologyProtein kinase AMedicineEnzyme

Abstract

fetched live from OpenAlex

Abstract. We present the CH4 concentration [CH4], the partial pressure of CO2 (pCO2) and the total gas content in bulk sea ice from subarctic, land-fast sea ice in the Kapisillit fjord, Greenland. Fjord systems are characterized by freshwater runoff and riverine input and based on δ18O data, we show that &gt; 30% of the surface water originated from periodic river input during ice growth. This resulted in fresher sea-ice layers with higher gas content than is typical from marine sea ice. The bulk ice [CH4] ranged from 1.8 to 12.1 nmol L−1, which corresponds to a partial pressure ranging from 3 to 28 ppmv. This is markedly higher than the average atmospheric methane content of 1.9 ppmv. Evidently most of the trapped methane within the ice was contained inside bubbles, and only a minor portion was dissolved in the brines. The bulk ice pCO2 ranged from 60 to 330 ppmv indicating that sea ice at temperatures above −4 °C is undersaturated compared to the atmosphere (390 ppmv). This study adds to the few existing studies of CH4 and CO2 in sea ice, and we conclude that subarctic seawater can be a sink for atmospheric CO2, while being a net source of CH4.

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)0.001
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 score0.021
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.203
Teacher spread0.194 · 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

Citations24
Published2014
Admission routes2
Has abstractyes

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