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Nitrilotriacetate Stimulation of Anaerobic Fe(III) Respiration by Mobilization of Humic Materials in Soil

2003· article· en· W2129740903 on OpenAlexafffund
Y.-S. Luu, Bruce A. Ramsay, J. A. Ramsay

Bibliographic record

VenueApplied and Environmental Microbiology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryHumic acidNitrilotriacetic acidEnvironmental chemistryChelationBiostimulationSoil waterMineralization (soil science)Extraction (chemistry)Nuclear chemistryInorganic chemistryBioremediationChromatographyOrganic chemistryBacteria

Abstract

fetched live from OpenAlex

An enrichment culture capable of naphthalene mineralization reduced Fe(III) oxides without direct contact in anaerobic soil microcosms when the Fe(III) was placed in dialysis membranes or entrapped within alginate beads. Both techniques demonstrated that a component in soil, possibly humic materials, facilitated Fe(III) reduction when direct contact between cells and Fe(III) was not possible. The addition of the synthetic Fe(III) chelator, nitrilotriacetic acid (NTA), to soil enhanced Fe(III) reduction across the dialysis membrane and alginate beads, with the medium changing from clear to a dark brown color. An NTA-soil extract was more effective in Fe(III) reduction than the extracted soil itself. Characteristics of the NTA extract were consistent with that of humic substances. The results indicate that NTA improved Fe(III) reduction not by Fe(III) solubilization but by extraction of humic substances from soil into the aqueous medium. This is the first study in which stimulation of Fe(III) reduction through the addition of chemical chelators is shown to be due to the extraction of electron-shuttling compounds from the soil and not to solubilization of the Fe(III) and indicates that mobilization of humic materials could be an important component of anaerobic biostimulation.

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 score0.001
Threshold uncertainty score0.697

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)0.0010.000

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 head0.005
GPT teacher head0.178
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

Citations18
Published2003
Admission routes2
Has abstractyes

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