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Record W2285197898 · doi:10.1144/geochem2014-275

<i>Marinobacter</i> bacteria associated with a massive sulphide ore deposit affect metal mobility in the deep subsurface

2015· article· en· W2285197898 on OpenAlexaffabout
Karla Leslie, Arne Sturm, Randy L. Stotler, Christopher J. Oates, Kurt Kyser, David A. Fowle

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeologyGeochemistryMetallurgyMaterials science

Abstract

fetched live from OpenAlex

A microorganism of the Marinobacter genus capable of Fe-oxidation at near-neutral pH, both in the presence and absence of oxygen, was found at a depth of 1.4 km in proximity to a Cu-Zn Volcanogenic Massive Sulphide (VMS) deposit, within the Triple 7 mine, Flin Flon, Manitoba, Canada. The microorganism was isolated from saline groundwater emanating from boreholes at that depth, which contained a small microbial community consisting of only two organisms. To examine biogeochemical trace metal cycling in this deep subsurface setting, incubation experiments were carried out with the Marinobacter isolate and mineralized (metal-containing ore) material in batch and column flow-through settings. The activity of the Marinobacter isolate resulted in an increase in the mobilization of major elements (Fe, S) and trace metals (Cu, Zn) from the solid ore material. These results indicate that Fe-oxidation may be an important biogeochemical process in the deep subsurface, which affects the mobilization of Fe and trace elements from buried mineralization.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

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 studies0.0000.000
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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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

Citations5
Published2015
Admission routes2
Has abstractyes

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