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Record W2994808686 · doi:10.1117/12.2538888

Variations of the carbon isotope composition and of organic and elemental carbon concentrations of the North Atlantic aerosols

2019· article· en· W2994808686 on OpenAlexaboutno aff
Svetlana A. Popova, Г. В. Симонова, В.И. Макаров, D. A. Kalashnikova, Polina N. Zenkova, A. P. Lisitzin, А. N. Novigatsky

Bibliographic record

Venue25th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon fibersEnvironmental chemistryEnvironmental scienceIsotopes of carbonComposition (language)IsotopeTotal organic carbonEarth scienceOceanographyChemistryGeologyMaterials sciencePhysicsNuclear physics

Abstract

fetched live from OpenAlex

The work reports the results of the measurements of particulate matter (PM), organic (OC) and elemental (EC) carbon mass concentrations, and carbon isotope composition (δ13С) in atmospheric aerosol sampled along the route of the research vessel “Academician Mstislav Keldysh” (71 voyage). The measurements were carried out over the Baltic Sea – the North Sea – the North Atlantic – the Norwegian Sea – the Barents Sea from June to August 2018. The increased OC, EC concentrations and the high δ13С value in the samples collected off the coast of Greenland are shown to be not due to marine origin, but due to the transfer of atmospheric aerosol formed in Canada during biomass burning (forest fires).

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)0.000
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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