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A re-analysis and review of elemental and polycyclic aromatic compound deposition in snow and lake sediments from Canada's Oil Sands Region integrating industrial performance and climatic variables

2022· article· en· W4206105835 on OpenAlexaffabout

Bibliographic record

VenueThe Science of The Total Environment · 2022
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsAlberta Environment and Protected Areas
Fundersnot available
KeywordsDeposition (geology)Oil sandsSnowPetroleumAsphaltCombustionPetroleum cokeCoke

Abstract

fetched live from OpenAlex

Much of the research from Canada's oil sands region (OSR) shows contaminants of concern (CoCs) throughout the ambient environment surrounding the industrial facilities. While there are some well-established sources of the CoCs, there is also spatial and temporal variability suggesting activity intensity, changes in technology, types and amounts of fuels combusted at the facilities, and climate may affect the results of deposition studies. This study re-analysed published data on the deposition of elements and polycyclic aromatic compounds (PACs) in snow and the sediments of some lakes by incorporating production data from facilities and climate. Using the Elastic Net (EN) regularized regression, variables describing potential associations between facility-specific activity and climate on the deposition of CoCs were identified. Among the selected variables, the combustion of delayed petroleum coke at the Suncor Basemine was associated with the deposition of CoCs, including elements in snow and in some lakes. Similarly, combustion of petroleum coke at Syncrude Mildred Lake was also identified in some models. In both cases, the effects of petroluem coke combustion are likely associated with the emission and deposition of fly ash. The mass of stored petroleum coke was not selected in snow CoC models, but the speed of the wind was a common driver for PACs. However, the mass of stockpiled petcoke was more closely associated with both elements and PACs in lake sediments. While the potential influence of other variables on the occurrence of CoCs in the OSR was also identified, including the production of crude bitumen and synthetic crude, the use of process and natural gases, temperature, and precipitation, these analyses support much of the earlier work and provides additional nuance. While more work is required, these results suggest facility-specific production and climatic data can be coupled with existing approaches to improve the identification of sources of CoCs in Canada's OSR and practices associated with their release.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.137
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.013
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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