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Record W2951306943

Evaluating the biogeochemistry and microbial function in the Athabasca Oil Sands region: Understanding natural baselines for reclamation end-points

2019· article· en· W2951306943 on OpenAlexfundaboutno aff
Thomas Reid

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2019
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsBiogeochemistryOil sandsLand reclamationNatural (archaeology)Function (biology)Environmental scienceGeologyEarth scienceEcologyGeographyOceanographyPaleontologyArchaeologyBiologyAsphalt
DOInot available

Abstract

fetched live from OpenAlex

Understanding the biogeochemical processes governed by the complex metabolic pathways of microbial communities is paramount in understanding overall ecosystem services. Their ability to adapt to the world’s harshest environments allows them to thrive in otherwise hostile environments. The Athabasca Oil Sands of Northern Alberta, Canada, constitutes one of the largest oil sands deposits in the world. This uniquely hydrocarbon-rich environment is a diverse and complex ecosystem governed by strong anthropogenic (i.e. industrial mine sites) and natural environmental gradients (i.e. substantial bitumen outcroppings). The economically significant oil sands deposit produces millions of barrels of bitumen daily, with waste materials (i.e. sands, clays, residual bitumen etc.) pumped into large settling basins called tailings ponds. The Government of Alberta requires oil sands operators to return their mine sites to a reclaimed landscape after mining has completed, thus leaving an enormous task of determining appropriate reclamation procedures, target end-points and water quality targets. However, what remains unknown is an understanding of the baseline biogeochemical fingerprint of the natural McMurray Formation (MF) – the geological strata constituting the mineable bitumen ore. Additionally, there has yet to be any studies focusing on the microbial function in the MF, a vital research gap that would provide insight into how the indigenous microbial communities deal with this ubiquitous, natural hydrocarbon presence. The research comprising the chapters of this dissertation, are the first to reveal the active, in-situ metabolism of the bacterial communities within the MF. Novel metatranscriptomics approaches from in-situ samples are used to characterize the microbial metabolic processes governing these ecosystems, to better understand what may constitute viable ecosystem reclamation end-points. Functional characterizations are compared to hydrocarbon signatures and redox state of the various study sites. Results indicate a unique microbial consortium with both energy and xenobiotic metabolic pathways tailored to the complex hydrocarbon substrate of the MF. Further, the sensitivity of this metatranscriptomics approach was tested and validated as a means of tracking hydrocarbon exposure down a river continuum. Clusters of closely related co-expressing genes revealed patterns of expression indicative of exposure to the hydrocarbons of the MF, providing interesting methodologies to track pollution exposure in a hydrodynamic context. Finally, a field-scale mesocosm study was used to track the biogeochemical evolution of tailings following a novel detoxification treatment and further compare back to the natural reference sites also studied. Treatments caused the reduction of toxic organics and the promotion of microbial taxa adept at metabolizing complex organics. These cumulative insights into the natural and anthropogenically impacted ecosystems of the Athabasca Oil Sands region provides much needed characterizations of reference/baseline environments from which to guide best management practices and gauge reclamation success.

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)0.001
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 score0.540
Threshold uncertainty score0.914

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.048
GPT teacher head0.264
Teacher spread0.216 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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