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Record W2942813007 · doi:10.1128/aem.00086-19

Potential for Microbially Mediated Natural Attenuation of Diluted Bitumen on the Coast of British Columbia (Canada)

2019· article· en· W2942813007 on OpenAlexafffundabout
Lars Schreiber, Nathalie Fortin, Julien Tremblay, Jessica Wasserscheid, Miria Elias, Jennifer L. Mason, Sylvie Sanschagrin, Susan E. Cobanli, Thomas King, Kenneth Lee, Charles W. Greer

Bibliographic record

VenueApplied and Environmental Microbiology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsFisheries and Oceans CanadaNational Research Council Canada
FundersUniversity of Waterloo
KeywordsDispersantMicrocosmPetroleumEnvironmental scienceEnvironmental chemistryChemistryDispersion (optics)

Abstract

fetched live from OpenAlex

Western Canada hosts large petroleum deposits, which ultimately enter the market in the form of dilbit. Tanker-based shipping represents the primary means to transport dilbit to international markets. With anticipated increases in production to meet global energy needs, the risk of a dilbit spill is expected to increase. This study investigated the potential of microbial communities naturally present in the waters of a potential dilbit shipping lane to mitigate the effects of a spill. Here we show that microbial degradation of dilbit was mostly limited to n -alkanes, while the overall concentration of polycyclic aromatic hydrocarbons, which represent the most toxic fraction of dilbit, decreased only slightly within the time frame of our experiments. We further investigated the effect of the oil dispersant Corexit 9500A on microbial dilbit degradation. Our results highlight the fact that dispersant-associated growth stimulation, and not only increased bioavailability of hydrocarbons and inhibition of specific genera, contributes to the overall effect of dispersant addition.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.900

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.002
GPT teacher head0.142
Teacher spread0.140 · 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 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

Citations36
Published2019
Admission routes3
Has abstractyes

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