MétaCan
Menu
Back to cohort
Record W2053038615 · doi:10.1029/2003jd004331

Long‐term trends of the black carbon concentrations in the Canadian Arctic

2004· article· en· W2053038615 on OpenAlexaffabout
Sangeeta Sharma, D. Lavoué, H. Cachier, L. A. Barrie, S. L. Gong

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsDalhousie University
Fundersnot available
KeywordsArcticEnvironmental scienceHazeCarbon blackRadiative forcingAtmospheric sciencesSnowAtmosphere (unit)AerosolClimatologyGeographyOceanographyMeteorologyChemistryGeology

Abstract

fetched live from OpenAlex

During the winter and spring the North American Arctic is impacted by anthropogenic black carbon (BC) in “Arctic Haze” pollution from sources mainly located in Europe and Russia. This black carbon, while suspended in the atmosphere and in surface snow, has a significant effect on radiative forcing of the Arctic atmosphere. Routine ground‐level observations of aerosol black carbon by optical absorption have been made at a Canadian Arctic location, Alert (82.5°N, 62.5°W), Nunavut since 1989. A 3‐year intensive study was conducted to compare BC obtained by the thermal analysis and optical absorption methods, so that the seasonal variations in the “operational” absorption cross sections of the aerosol could be determined. A time series analysis indicated that black carbon concentrations undergo a strong seasonal variation superimposed upon a long‐term trend. The latter shows a decrease of about 55% in BC concentrations between 1989 and 2002 at Alert. Factors responsible for these trends such as changes in emissions and atmospheric transport support the hypothesis that BC emissions from the former USSR are mostly responsible for the observed decreasing trend. Transport from other sectors such as North America and Europe are not as prevalent at Alert.

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 score0.365
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.288
Teacher spread0.258 · 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

Citations241
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Geophysical Research AtmospheresSame topicAtmospheric chemistry and aerosolsFrench-language works237,207