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Record W3047962661 · doi:10.1002/rem.21659

Use of a novel biomarker, botryococcane, to monitor biodegradation of two lacustrine‐sourced crude oils

2020· article· en· W3047962661 on OpenAlexaff
Gregory S. Douglas, Jeffery Hardenstine, Roopa Kamath, Deyuan Kong, Robert E. Hoffmann, S. J. McMillen

Bibliographic record

VenueRemediation Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsChevron (Canada)
Fundersnot available
KeywordsBioremediationBiodegradationHopanoidsEnvironmental remediationEnvironmental chemistryEnvironmental sciencePetroleumCrude oilChemistryHydrocarbonBiomarkerPetroleum engineeringContaminationGeologyEcologySource rockBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Bioremediation is a proven alternative for remediating petroleum‐impacted soils at exploration and production (E&P) sites. Monitoring remediation performance can involve detection and quantification of biodegradation resistant compounds such as C 30 17α(H),21β(H)‐hopane, which requires the use of gas chromatography with mass spectrometry detection (GC/MS). Due to the remoteness of many E&P sites, this technology is not always available, and alternative methods are needed to provide reliable quantitative measurements of petroleum remediation efficiency. This study provides a detailed chemical characterization of lacustrine‐sourced crude oils and a technical basis for measuring the effectiveness of bioremediation efforts for soil impacted by those crudes. We show that the novel isoprenoid hydrocarbon botryococcane is relatively stable in lacustrine‐sourced crude oils compared with C 30 17α(H),21β(H)‐hopane under moderate biodegradation conditions generally observed in field samples. We have also demonstrated that, due to the stability and relatively elevated concentration of botryococcane in lacustrine oils, it can be reliably measured using the more cost‐effective and available GC/FID methodology, and thereby be used to monitor the progress of ongoing soil bioremediation activities at remote sites.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.063
GPT teacher head0.270
Teacher spread0.207 · 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
Published2020
Admission routes1
Has abstractyes

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