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Record W2172849911 · doi:10.1139/w08-084

Microbial distribution and diversity in saturated, high pH, uranium mine tailings, Saskatchewan, Canada

2008· article· en· W2172849911 on OpenAlexafffundvenueabout
Gideon Wolfaardt, M. Jim Hendry, Darren R. Korber

Bibliographic record

VenueCanadian Journal of Microbiology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsToronto Metropolitan UniversityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthCameco
KeywordsTailingsUraniumEnvironmental chemistryMicrobial population biologyEnvironmental scienceSurface waterBiofilmUranium oreChemistryEcologyGeologyBiologyEnvironmental engineeringBacteria

Abstract

fetched live from OpenAlex

Microbiological analyses were conducted on core samples collected along a vertical profile (0-66 m below surface) from the tailings management facility (TMF) at the Rabbit Lake uranium mine in northern Saskatchewan, Canada. Bacterial numbers in the core materials were similar to surrounding soils and surface waters, regardless of the seemingly unfavorable pH (mean=9.9) and temperature (approximately 0 degrees C) in the TMF. The greatest number of viable cells (105 CFU/g) was detected at the interface between the tailings and overlying standing water, below which cell counts decreased rapidly with depth. Whole-community metabolic profiles for samples from the different depths grouped into 3 clusters; however, these groups could not be positively correlated with sampling depth, temperature, redox potential, pH, or ore-mill feed. Flow-cell studies demonstrated microbial communities in the tailings surface water could develop biofilms and maintain cell activity at both pH 10 and 7, and altering the pH between these 2 values had little effect on biofilm viability. These results demonstrate the resilience and adaptive nature of naturally occurring microbial communities and signify a potential role of microbial activity in the long-term geochemical evolution of the TMF.

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 score0.321
Threshold uncertainty score0.327

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 integrity0.0000.000
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 head0.006
GPT teacher head0.166
Teacher spread0.160 · 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

Citations21
Published2008
Admission routes4
Has abstractyes

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