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Record W2804788604 · doi:10.11575/prism/31934

Seasonal Pattern and Sources of Particulate Carbon in Kananaskis and Calgary, Alberta

2018· dissertation· en· W2804788604 on OpenAlexfundaboutno aff
Chengbao Ge

Bibliographic record

VenuePRISM (University of Calgary) · 2018
Typedissertation
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsParticulatesGeographyEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Sources, seasonal pattern of elemental carbon(EC), organic carbon(OC), and total carbon from dry deposition and precipitation in Kananaskis and Calgary were assessed using thermo-optical methods. Vehicle exhaust was inferred to be dominant source of carbon throughout the year with an identical OC/EC of 22±14 in Calgary and 22±5 in Kananaskis in dry deposition. Biogenic OC signal was absent in Kananaskis in precipitation or dry deposition. Biomass burning, with a lower OC/EC both in winter and summer, was potentially associated with recreation and tourism in Kananaskis. Sources from long-range transport impact both locations simultaneously. A lower boundary layer at night concentrates TC and a higher boundary layer in the day lower the concentration in both locations. OC is much more easily removed by precipitation than EC due to is larger surface area and size and OC/EC ratios in precipitation reaching 130 were observed in Calgary while those in Kananaskis reached 46.

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 score0.017
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
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.177
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
Published2018
Admission routes2
Has abstractyes

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