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Record W2137945591 · doi:10.1029/2008gl033906

Light absorbing carbon emissions from commercial shipping

2008· article· en· W2137945591 on OpenAlexaff
D. A. Lack, B. M. Lerner, Claire Granier, T. Baynard, Edward R. Lovejoy, P. Massoli, A. R. Ravishankara, E. J. Williams

Bibliographic record

VenueGeophysical Research Letters · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsLockheed Martin (Canada)
FundersNational Oceanic and Atmospheric Administration
KeywordsEnvironmental scienceAerosolArcticAir quality indexAtmospheric sciencesThe arcticClimatologyMeteorologyOceanographyGeographyGeology

Abstract

fetched live from OpenAlex

Extensive measurements of the emission of light absorbing carbon aerosol (LAC) from commercial shipping are presented. Vessel emissions were sampled using a photoacoustic spectrometer in the Gulf of Mexico region. The highest emitters (per unit fuel burnt) are tug boats, thus making significant contributions to local air quality in ports. Emission of LAC from cargo and non cargo vessels in this study appears to be independent of engine load. Shipping fuel consumption data (2001) was used to calculate a global LAC contribution of 133(±27) Ggyr −1 , or ∼1.7% of global LAC. This small fraction could have disproportionate effects on both air quality near port areas and climate in the Arctic if direct emissions of LAC occur in that region due to opening Arctic sea routes. The global contribution of this LAC burden was investigated using the MOZART model. Increases of 20–50 ng m −3 LAC (relative increases up to 40%) due to shipping occur in the tropical Atlantic, Indonesia, central America and the southern regions of South America and Africa.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.282
Teacher spread0.229 · 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.

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

Citations104
Published2008
Admission routes1
Has abstractyes

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