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Record W4251581697 · doi:10.24124/2015/bpgub1029

Measurement of nitrogen dioxide and sulfur dioxide by satellite and passive monitors in Northeastern British Columbia, Canada.

2015· dissertation· en· W4251581697 on OpenAlexfundaboutno aff
Syful Islam

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaGoddard Space Flight CenterMinistry of EnvironmentUniversity of Northern British Columbia
KeywordsNitrogen dioxideEnvironmental scienceSulfur dioxideSatelliteAir quality indexPollutantCarbon dioxideGeographyPhysical geographyHydrology (agriculture)MeteorologyGeologyChemistryEngineering

Abstract

fetched live from OpenAlex

The Peace River district of Northeastern British Columbia (B.C.) Canada is a region of natural gas production that has undergone rapid development since 2005. Both satellite data products and Willems badge passive sampler measurements of nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) were used to assess the air quality implications from gas development activities. Both satellite data products between 2005 and 2013 and Willems badge passive samplers during six two-week exposure periods between August and November, 2013 have been considered in this study. All satellite data products and passive monitoring of these two pollutants in Northeastern B.C. found higher values in Taylor, Fort St. John, and Dawson Creek. This spatial distribution of higher values has resulted from the large gas development activities in these areas. The temporal analysis of satellite NO₂ data revealed higher values near Dawson Creek after 2007 with annual increment of 1.7%. It was also found that Taylor is half as polluted as one of the Canada's largest non-urban SO₂ emission source areas (Canadian oil sands areas in Alberta). --Leaf ii.

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 score0.014
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.004
GPT teacher head0.176
Teacher spread0.172 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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