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Record W2770808829 · doi:10.5194/amt-11-3829-2018

Airborne lidar measurements of aerosol and ozone above the Canadian oil sands region

2018· article· en· W2770808829 on OpenAlexaffabout
Monika Aggarwal, J. A. Whiteway, J. A. Seabrook, L. H. Gray, K. B. Strawbridge, Peter Liu, Jason O’Brien, Shao‐Meng Li, R. McLaren

Bibliographic record

VenueAtmospheric measurement techniques · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsEnvironment and Climate Change CanadaYork University
Fundersnot available
KeywordsLidarAerosolEnvironmental scienceOil sandsPlanetary boundary layerOzoneAtmospheric sciencesMixing ratioPlumeBoundary layerOzone Monitoring InstrumentBackscatter (email)Air pollutionRemote sensingMeteorologyGeologyChemistryMaterials scienceGeography

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 to 1.6 km above ground. The ozone mixing ratio measured in the polluted boundary layer air directly above the oil sands industry was equal to or less than the background ozone mixing ratio. 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 was substantially greater than the background. 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 polluted 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.214
Teacher spread0.191 · 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

Citations30
Published2018
Admission routes2
Has abstractyes

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