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Record W4211114266 · doi:10.5194/tcd-7-3899-2013

Parameterization of atmosphere–surface exchange of CO <sub>2</sub> over sea ice

2013· preprint· en· W4211114266 on OpenAlexafffund
Lise Lotte Sørensen, Bente Thoft Jensen, Ronnie N. Glud, Daniel F. McGinnis, Mikael K. Sejr, J. Sievers, D. H. Søgaard, Jean‐Louis Tison, Søren Rysgaard

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Manitoba
FundersForsknings- og InnovationsstyrelsenDanmarks Tekniske UniversitetKommissionen for Videnskabelige Undersøgelser i GrønlandCanada Excellence Research Chairs, Government of CanadaComisión de Investigaciones CientíficasDanish Agency for Science and Higher EducationDeutsches KrebsforschungszentrumNational Research FoundationNordisk MinisterrådDanmarks Grundforskningsfond
KeywordsSea iceBrineAtmospheric sciencesSea ice thicknessFlux (metallurgy)Atmosphere (unit)SnowSea ice growth processesEnvironmental scienceCarbon dioxideArctic ice packClimatologyChemistryGeologyMeteorologyPhysics

Abstract

fetched live from OpenAlex

Abstract. We apply a flux parameterisation commonly used over terrestrial areas for calculation of CO2 fluxes over sea ice surfaces. The parameterisation is based on resistance analogy, and is evaluated and tested on data from seasonal fast sea ice, and the different variables influencing the exchange of CO2 between the atmosphere and ice are investigated. We found the flux to be small during the late winter with fluxes in both directions. Not surprisingly we find that the resistance across the surface controls the fluxes and detailed knowledge of the brine volume and carbon chemistry within the brines as well as knowledge of snow cover and carbon chemistry in the ice are essential to estimate the partial pressure of pCO2 and CO2 flux. Further investigations of surface structure and snow cover and driving parameters like heat flux, radiation, ice temperature and brine processes are required to adequately parameterize the surface resistance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.216
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations1
Published2013
Admission routes2
Has abstractyes

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