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Record W6884335549 · doi:10.1021/acs.est.9b05522.s001

Top-Down Determination\nof Black Carbon Emissions from\nOil Sand Facilities in Alberta, Canada Using Aircraft Measurements

2019· article· en· W6884335549 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsDiesel fuelEmission inventoryStack (abstract data type)Carbon fibersAir pollutionGreenhouse gasCarbon black

Abstract

fetched live from OpenAlex

Black carbon (BC) emissions from the Canadian oil sand\n(OS) surface\nmining facilities in Alberta were investigated using aircraft measurements.\nBC emission rates were derived with a top-down mass balance approach\nand were found to be linearly related to the volume of oil sand ore\nmined at each facility. Two emission factors were determined from\nthe measurements; production-based BC emission factors were in the\nrange of 0.6–1.7 g/tonne mined OS ore, whereas fuel-based BC\nemission factors were between 95 and 190 mg/kg-fuel, depending upon\nthe facility. The annual BC emission, at 707 ± 117 tonnes/year\nfor the facilities, was determined using the production-based emission\nfactors and annual production data. Although this annual emission\nis in reasonable agreement with the BC annual emissions reported in\nthe latest version of the Canadian national BC inventory (within 16%),\nthe relative split between off-road diesel and stack sources is significantly\ndifferent between the measurements and the inventory. This measurement\nevidence highlights the fact that the stack sources of BC may be overestimated\nand the off-road diesel sources may be underestimated in the inventory\nand points to the need for improved BC emission data from diesel sources\nwithin facilities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.4400.000

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 head0.024
GPT teacher head0.219
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

Citations0
Published2019
Admission routes1
Has abstractyes

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