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Record W4405116918 · doi:10.21275/sr21517162650

Effects of Sub-Aerial Weathering on Hydrocarbon Distributions in Oil Sands (Athabasca Oil Sand, Canada and Parana Basin, Brazil)

2021· article· en· W4405116918 on OpenAlexaboutno aff
W E Osung, Edward F. Thompson

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2021
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsWeatheringStructural basinHydrocarbonGeologyEnvironmental scienceGeochemistryGeographyGeomorphologyArchaeologyAsphaltChemistry

Abstract

fetched live from OpenAlex

Sub-aerial weathering plays a significant role in the reduction of low molecular weight hydrocarbons in surface exposed oils. In order to investigate the detailed effects of sub-aerial weathering as opposed to sub-surface biodegradation, a suite of ten (10) oil sand samples were solvent extracted and analyzed in the laboratory using liquid column chromatography separation, gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). The gas chromatograms showed total loss of n-alkanes and isoprenoids while the ion mass chromatograms showed alternation in steranes, little loss of hopanes and no pronounced loss of higher molecular weight of triaromatic steroids; thus, they were at between levels 5-8 on the Peters and Moldowan (PM) scale of biodegradation. GC-MS analysis was performed to analyze biological markers. Among the biological markers, the most important and noticeable effects were a decrease in the 20S/20R diastereomer ratio of the C29 steranes. There were also evident effects on the overall distributions and abundance of pentacyclic terpanes thus, leading to alteration of biomarker parameters which could result in erroneous interpretation of source depositional environments, source correlation and maturity.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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