MétaCan
Menu
Back to cohort
Record W2424869982 · doi:10.1139/cjm-2016-0137

Enriching acid rock drainage related microbial communities from surface-deposited oil sands tailings

2016· article· en· W2424869982 on OpenAlexaffvenue
Chloe L. Dean, Yeyuan Xiao, Deborah J. Roberts

Bibliographic record

VenueCanadian Journal of Microbiology · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsTailingsOil sandsMicrobial population biologyAcid mine drainageSulfurMicroorganismOxidizing agentEnvironmental chemistryAchromobacterIron bacteriaEnvironmental scienceEcologyChemistryBiologyBacteriaPseudomonasGeography

Abstract

fetched live from OpenAlex

Little is known about the microbial communities native to surface-deposited pyritic oil sands tailings, an environment where acid rock drainage (ARD) could occur. The goal of this study was to enrich sulfur-oxidizing organisms from these tailings and determine whether different populations exist at pH levels 7, 4.5, and 2.5. Using growth-based methods provides model organisms for use in the future to predict potential activities and limitations of these organisms and to develop possible control methods. Thiosulfate-fed enrichment cultures were monitored for approximately 1 year. The results showed that the enrichments at pH 4.5 and 7 were established quicker than at pH 2.5. Different microbial community structures were found among the 3 pH environments. The sulfur-oxidizing microorganisms identified were most closely related to Halothiobacillus neapolitanus, Achromobacter spp., and Curtobacterium spp. While microorganisms related to Chitinophagaceae and Acidocella spp. were identified as the only possible iron-oxidizing and -reducing microbes. These results contribute to the general knowledge of the relatively understudied microbial communities that exist in pyritic oil sands tailings and indicate these communities may have a potential role in ARD generation, which may have implications for future tailings management.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.181
Teacher spread0.173 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of MicrobiologySame topicMetal Extraction and BioleachingFrench-language works237,207