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Degeneration of biogenic superparamagnetic magnetite

2009· article· en· W1986554810 on OpenAlexaff
Yiliang Li, Susan M. Pfiffner, M. D. Dyar, Hojatollah Vali, Kurt O. Konhauser, David R. Cole, Adam J. Rondinone, Tommy J. Phelps

Bibliographic record

VenueGeobiology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of AlbertaMcGill University
Fundersnot available
KeywordsMagnetiteSuperparamagnetismMössbauer spectroscopyChemistryMaterials scienceMineralogyChemical engineeringCrystallographyMetallurgyMagnetizationMagnetic field

Abstract

fetched live from OpenAlex

Magnetite crystals precipitated as a consequence of Fe(III) reduction by Shewanella algae BrY after 265 h incubation and 5-year anaerobic storage were investigated with transmission electron microscopy, Mössbauer spectroscopy and X-ray diffraction. The magnetite crystals were typically superparamagnetic with an approximate size of 13 nm. The lattice constants of the 265 h and 5-year crystals are 8.4164A and 8.3774A, respectively. The Mössbauer spectra indicated that the 265 h magnetite had excess Fe(II) in its crystal-chemistry (Fe(3+) (1.990)Fe(2+) (1.015)O(4)) but the 5-year magnetite was Fe(II)-deficient in stoichiometry (Fe(3+) (2.388)Fe(2+) (0.419)O(4)). Such crystal-chemical changes may be indicative of the degeneration of superparamagnetic magnetite through the aqueous oxidization of Fe(II) anaerobically, and the concomitant oxidation of the organic phases (fatty acid methyl esters) that were present during the initial formation of the magnetite. The observation of a corona structure on the aged magnetite corroborates the anaerobic oxidation of Fe(II) on the outer layers of magnetite crystals. These results suggest that there may be a possible link between the enzymatic activity of the bacteria and the stability of Fe(II)-excess magnetite, which may help explain why stable nano-magnetite grains are seldom preserved in natural environments.

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 score0.619
Threshold uncertainty score0.664

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 head0.218
Teacher spread0.212 · 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

Citations34
Published2009
Admission routes1
Has abstractyes

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