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Record W4245029582 · doi:10.5194/amt-2017-391

Airborne Lidar Measurements of Aerosol and Ozone Above the Canadian Oil Sands Region

2017· preprint· en· W4245029582 on OpenAlexafffundabout
Monika Aggarwal, J. A. Whiteway, J. A. Seabrook, Lawrence Gray, K. B. Strawbridge, Peter Liu, Jason M. O’Brien, Shao‐Meng Li, R. McLaren

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsEnvironment and Climate Change CanadaYork University
FundersNatural Sciences and Engineering Research Council of CanadaFedDev OntarioEnvironment and Climate Change CanadaUniversity of Wyoming
KeywordsLidarAerosolEnvironmental sciencePlanetary boundary layerOzoneAtmospheric sciencesMixing ratioPlumeBoundary layerBackscatter (email)Ozone Monitoring InstrumentOil sandsRemote sensingMeteorologyMaterials scienceGeologyGeography

Abstract

fetched live from OpenAlex

Abstract. Aircraft based lidar measurements of atmospheric aerosol and ozone were conducted to study air pollution from the oil sands extraction industry in northern Alberta. Significant amounts of aerosol were observed in the polluted air within the surface boundary layer, up to heights of 1 km to 1.5 km above ground. The ozone mixing ratio measured in the polluted boundary layer air was equal to or less than the background ozone mixing ratio, in the range of 10 ppbv to 35 ppbv. On one of the flights, the lidar measurements detected a layer of forest fire smoke above the surface boundary layer in which the ozone mixing ratio had a maximum value of 70 ppbv. Measurements of the linear depolarization ratio in the aerosol backscatter were obtained with a ground based lidar and this aided in the discrimination between the separate emission sources from industry and forest fires. The retrieval of ozone abundance from the lidar measurements required the development of a method to account for the interference from the substantial aerosol content within the surface boundary layer.

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 score0.036
Threshold uncertainty score0.072

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.242
Teacher spread0.206 · 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

Citations3
Published2017
Admission routes3
Has abstractyes

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