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Record W2260387185 · doi:10.14644/dust.2014.044

Evaluation of aeolian dust records obtained from Polar Ice Cores

2014· article· en· W2260387185 on OpenAlexfundno aff
Ernesto Kettner, Aslak Grinsted, Anna Wegner, J. R. Petit, Tobias Erhardt, Simon Schüpbach, Paul Vallelonga, Anders Svensson

Bibliographic record

VenueBern Open Repository and Information System (University of Bern) · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersNatural Environment Research CouncilFonds Wetenschappelijk OnderzoekNatural Resources CanadaCentre National de la Recherche ScientifiqueKorea Polar Research InstituteAgence Nationale de la RechercheNederlandse Organisatie voor Wetenschappelijk OnderzoekInstitut Polaire Français Paul Emile VictorNational Institute of Polar ResearchOffice of Polar ProgramsFonds De La Recherche Scientifique - FNRSSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsIce coreAeolian processesCoulter counterEnvironmental sciencePolarRemote sensingExtinction (optical mineralogy)MineralogyGeologyMeteorologyAtmospheric sciencesClimatologyPhysicsGeomorphologyAstronomy

Abstract

fetched live from OpenAlex

When an ice core sample is analysed for its aeolian dust content, it is melted and the particles detected
\nare suspended in water. Consequently, dust measurement techniques employed in the ice core
\ncommunity differ from those used for in-situ studies of airborne dust.
\nMethods commonly used to classify insolubles suspended in a liquid are either based on the particles’
\ninteraction with light or on the detection of resistive pulses by means of Coulter counting. Data sets
\nobtained with Coulter counters are widely accepted as references and other techniques are judged
\nagainst their ability to reproduce these.
\nUnfortunately, optically acquired ice core dust records were found to differ. By analyzing two
\nsections of the NEEM dust record, two different evaluation procedures are discussed before a third
\nprotocol is proposed. It is found that relative changes in the archived dust load can be reproduced,
\nwhile the simultaneous attainment of absolute concentrations or changes in the grain size frequency
\nhistograms in high resolution remains difficult.

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 score0.119
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.014
GPT teacher head0.188
Teacher spread0.174 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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