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Record W2190757338 · doi:10.1021/acs.est.5b04985

A Decade of Change in NO<sub>2</sub> and SO<sub>2</sub> over the Canadian Oil Sands As Seen from Space

2015· article· en· W2190757338 on OpenAlexaffabout
C. A. McLinden, Vitali Fioletov, N. A. Krotkov, Can Li, K. F. Boersma, C. Adams

Bibliographic record

VenueEnvironmental Science & Technology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsEnvironment and Climate Change Canada
FundersEarth Sciences Division
KeywordsOil sandsEnvironmental scienceSpace (punctuation)GeologyMineralogyPetroleum engineeringPhysical geographyArchaeologyGeographyAsphaltComputer science

Abstract

fetched live from OpenAlex

A decade (2005-2014) of observations from the Ozone Monitoring Instrument (OMI) were used to examine trends in nitrogen dioxide (NO2) and sulfur dioxide (SO2) over a large region of western Canada and the northern United States, with a focus on the Canadian oil sands. In the oil sands, primarily over an area of intensive surface mining, NO2 tropospheric vertical column densities (VCDs) are seen to be increasing by as much as 10%/year, with the location of the largest trends in a newly developing NO2 "lobe" well removed from surface monitoring stations. SO2 VCDs in the oil sands have remained approximately constant. The only other significant increase in the region was seen in NO2 over Bakken gas fields in North Dakota which showed increases of up to 5%/yr. By contrast, other locations in the region show substantial declines in both pollutants, providing strong evidence to the efficacy of environmental pollution control measures implemented by both nations. The OMI-derived trends were found to be consistent with those from the Canadian surface monitoring network, although in the case of SO2, it was necessary to apply a correction in order to remove the residual signal from volcanic eruptions present in the OMI data.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score1.000

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 studies0.0000.007
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations70
Published2015
Admission routes2
Has abstractyes

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