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Record W3165852793 · doi:10.3390/microorganisms9051091

A Deep Look into the Microbiology and Chemistry of Froth Treatment Tailings: A Review

2021· review· en· W3165852793 on OpenAlexafffund
Angeline Van Dongen, Abdul Samad, Nicole E. Heshka, Kara Rathie, Christine Martineau, Guillaume Bruant, Dani Degenhardt

Bibliographic record

VenueMicroorganisms · 2021
Typereview
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsNational Research Council CanadaNatural Resources Canada
FundersOffice of Energy Research and Development
KeywordsTailingsOil sandsEnvironmental scienceWaste managementEnvironmental chemistryBiodegradationAsphaltChemistryEngineering

Abstract

fetched live from OpenAlex

of tailings waste from the bitumen extraction process are stored in tailings ponds. Fugitive emissions associated with residual hydrocarbons in tailings ponds pose an environmental concern and include greenhouse gases (GHGs), reduced sulphur compounds (RSCs), and volatile organic compounds (VOCs). Froth treatment tailings (FTT) are a specific type of tailings waste stream from the bitumen froth treatment process that contains bioavailable diluent: either naphtha or paraffins. Tailings ponds that receive FTT are associated with the highest levels of biogenic gas production, as diverse microbial communities biodegrade the residual diluent. In this review, current literature regarding the composition, chemical analysis, and microbial degradation of FTT and its constituents is presented in order to provide a more complete understanding of the complex chemistry and biological processes related to fugitive emissions from tailings ponds receiving FTT. Characterizing the composition and biodegradation of FTT is important from an environmental perspective to better predict emissions from tailings ponds and guide tailings pond management decisions.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
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)0.0010.000

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 head0.012
GPT teacher head0.269
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2021
Admission routes2
Has abstractyes

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