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Record W2147942812 · doi:10.1139/w05-128

Electron transport pathways for the oxidation of endogenous substrate(s) in<i>Acidithiobacillus ferrooxidans</i>

2006· article· en· W2147942812 on OpenAlexaffvenue
Yongqiang Chen, Isamu Suzuki

Bibliographic record

VenueCanadian Journal of Microbiology · 2006
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAcidithiobacillus ferrooxidansEndogenySubstrate (aquarium)ChemistryBiophysicsRedoxBiochemistryBioleachingMicrobiologyBiologyInorganic chemistryCopperEcology

Abstract

fetched live from OpenAlex

Oxidation of endogenous substrate(s) of Acidithiobacillus ferrooxidans with O2 or Fe3+ as electron acceptor was studied in the presence of uncouplers and electron transport inhibitors. Endogenous substrate was oxidized with a respiratory quotient (CO2 produced/O2 consumed) of 1.0, indicating its carbohydrate nature. The oxidation was inhibited by complex I inhibitors (rotenone, amytal, and piericidin A) only partially, but piericidin A inhibited the oxidation with Fe3+ nearly completely. The oxidation was stimulated by uncouplers, and the stimulated activity was more sensitive to inhibition by complex I inhibitors. HQNO (2-heptyl-4-hydroxyquinoline N-oxide) also stimulated the oxidation, and the stimulated respiration was more sensitive to KCN inhibition than uncoupler stimulated respiration. Fructose, among 20 sugars and sugar alcohols including glucose and mannose, was oxidized with a CO2/O2 ratio of 1.0 by the organism. Iron chelators in general stimulated endogenous respiration, but some of them reduced Fe3+ chemically, introducing complications. The results are discussed in view of a branched electron transport system of the organism and its possible control.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 head0.017
GPT teacher head0.182
Teacher spread0.165 · 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 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

Citations5
Published2006
Admission routes2
Has abstractyes

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