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Record W2786986190 · doi:10.1016/j.gca.2018.01.015

Molecular diffusion of stable water isotopes in polar firn as a proxy for past temperatures

2018· article· en· W2786986190 on OpenAlexfundno aff
Christian Holme, Vasileios Gkinis, Bo Vinther

Bibliographic record

VenueGeochimica et Cosmochimica Acta · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersOffice of Polar ProgramsInstitut national des sciences de l'UniversNatural Environment Research CouncilAard- en Levenswetenschappen, Nederlandse Organisatie voor Wetenschappelijk OnderzoekNational Institute of Polar ResearchCalifornia Earthquake AuthorityCommissariat à l'Énergie Atomique et aux Énergies AlternativesVetenskapsrådetNatural Resources CanadaFonds Wetenschappelijk OnderzoekCentre National de la Recherche ScientifiqueInstitut Polaire Français Paul Emile VictorAlfred Wegener Institute Helmholtz Centre for Polar and Marine ResearchChinese Academy of SciencesIcelandic Centre for ResearchFonds De La Recherche Scientifique - FNRSSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDanmarks GrundforskningsfondNederlandse Organisatie voor Wetenschappelijk OnderzoekAgence Nationale de la RechercheEuropean Research CouncilSeventh Framework ProgrammeNational Research FoundationEuropean CommissionKorea Polar Research InstituteSight Research UKNational Science Foundation
KeywordsFirnProxy (statistics)PolarIsotopeStable isotope ratioDiffusionGeologyChemistryChemical physicsEarth scienceThermodynamicsGeomorphologySnowPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Polar precipitation archived in ice caps contains information on past temperature conditions. Such information can be retrieved by measuring the water isotopic signals of δ18O and δD in ice cores. These signals have been attenuated during densification due to molecular diffusion in the firn column, where the magnitude of the diffusion is isotopologue specific and temperature dependent. By utilizing the differential diffusion signal, dual isotope measurements of δ18O and δD enable multiple temperature reconstruction techniques. This study assesses how well six different methods can be used to reconstruct past surface temperatures from the diffusion-based temperature proxies. Two of the methods are based on the single diffusion lengths of δ18O and δD, three of the methods employ the differential diffusion signal, while the last uses the ratio between the single diffusion lengths. All techniques are tested on synthetic data in order to evaluate their accuracy and precision. We perform a benchmark test to thirteen high resolution Holocene data sets from Greenland and Antarctica, which represent a broad range of mean annual surface temperatures and accumulation rates. Based on the benchmark test, we comment on the accuracy and precision of the methods. Both the benchmark test and the synthetic data test demonstrate that the most precise reconstructions are obtained when using the single isotope diffusion lengths, with precisions of approximately 1.0°C. In the benchmark test, the single isotope diffusion lengths are also found to reconstruct consistent temperatures with a root-mean-square-deviation of 0.7°C. The techniques employing the differential diffusion signals are more uncertain, where the most precise method has a precision of 1.9°C. The diffusion length ratio method is the least precise with a precision of 13.7°C. The absolute temperature estimates from this method are also shown to be highly sensitive to the choice of fractionation factor parameterization.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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 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

Citations34
Published2018
Admission routes1
Has abstractyes

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