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Record W3155882835

Polycyclic Aromatic Compounds (PACs) in Alberta’s Oil Sands Tailing Ponds: Levels and Reassessment of Emissions

2019· dissertation· en· W3155882835 on OpenAlexaboutno aff
Rachelle Robitaille

Bibliographic record

VenueTSpace · 2019
Typedissertation
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsEnvironmental scienceWaste managementTailingsEnvironmental chemistryMining engineeringEnvironmental engineeringChemistryArchaeologyGeologyEngineeringGeographyAsphalt
DOInot available

Abstract

fetched live from OpenAlex

Polycyclic aromatic compounds (PACs) are semi volatile organic compounds emitted from Alberta’s oil sands tailing ponds (OSTPs). They have been classified as priority compounds for monitoring because of their toxicity and environmental ubiquity. Alberta’s open-air OSTPs have large surface areas from which PACs can volatize. Water analysis from four different OSTPs taken at different times were analyzed for targeted PACs, where magnitude differences in concentrations between ponds showed spatial heterogeneity. An equilibrium chamber study was devised to determine the air-OSTP water partition coefficients for PACs. Air-OSTP water partition coefficients were found to be 10-59% lower than literature values depending on the compound. With these newly determined partition coefficient values, this research also estimated point-in-time fluxes of PACs from four different ponds. Flux estimates can vary as much as 5 orders of magnitudes between ponds.

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 score0.636
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.017
GPT teacher head0.325
Teacher spread0.308 · 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
Published2019
Admission routes1
Has abstractyes

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