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Record W2995719732 · doi:10.1029/2019gl085318

Sources and Radiative Absorption of Water‐Soluble Brown Carbon in the High Arctic Atmosphere

2019· article· en· W2995719732 on OpenAlexaff
Siyao Yue, Srinivas Bikkina, Meng Gao, Leonard A. Barrie, Kimitaka Kawamura, Pingqing Fu

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsArcticEnvironmental scienceAtmospheric sciencesAtmosphere (unit)Radiative transferLatitudeSunriseThe arcticCarbon fibersBiomass burningAbsorption (acoustics)ClimatologyEnvironmental chemistryAerosolMeteorologyChemistryOceanographyGeographyGeologyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Brown carbon (BrC) is a source of light‐absorbing aerosols. The Arctic is more sensitive to emissions of light‐absorbing aerosols than lower latitudes. Knowledge of BrC in a historical period is beneficial to understand its role in a changing climate. Here, we present measurement of water‐soluble BrC (WS‐BrC) for the Arctic aerosols during late winter‐late spring in 1991. Mass absorption coefficient (0.07 ± 0.04 M/m) and efficiency (0.41 ± 0.21 m2/g) at 365 nm of WS‐BrC were lower than those in polluted urban and rural regions. WS‐BrC was mainly from biomass burning/combustion (dark winter to mid‐March) and marine sources connected with photochemical gas to particle conversion (after polar sunrise to June). Solar radiative absorption of WS‐BrC relative to elemental carbon was 5% on average in February to April and surged to 34% after mid‐May. This study helps in understanding the role of BrC in the Arctic climate.

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 score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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

Citations30
Published2019
Admission routes1
Has abstractyes

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