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Record W1509824193 · doi:10.1002/2014jg002745

Formation of iron‐rich shelled structures by microbial communities

2015· article· en· W1509824193 on OpenAlexaff
David C. Fernández‐Remolar, Joan Santamaría, Ricardo Amils, Vı́ctor Parro, David Gómez-Ortíz, M. R. M. Izawa, Neil R. Banerjee, Raúl Pérez Rodríguez, Núria Rodríguez, Nieves López‐Martínez

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of WinnipegWestern University
FundersDepartment for the Economy
KeywordsMineralization (soil science)GoethiteMetalZincBiomineralizationSulfideStackingIron oxideOxideChemical engineeringChemistryFerrousMineralogyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Abstract In this paper, we describe the discovery and characterization of shelled structures that occur inside galleries of Pyrenees mines. The structures are formed by the mineralization of iron and zinc oxides, dominantly franklinite (ZnFe2O4) and poorly ordered goethite (α‐FeO(OH)). Subsurface oxidation and hydration of polymetallic sulfide orebodies produce solutions rich in dissolved metal cations including Fe2+/3+ and Zn2+. The microbially precipitated shell‐like structure grows by lateral or vertical stacking of thin laminae of iron oxide particles which are accreted mostly by fungal filaments. The resulting structures are composed of randomly oriented aggregates of needle‐like, uniform‐sized crystals, suggesting some biological control in the structure formation. Such structures are formed by the integration of two separated shells, following a complex process driven likely by different strategies of fungal microorganisms that produced the complex macrostructure.

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 score0.001
Threshold uncertainty score0.002

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 communication0.0010.000
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 head0.069
GPT teacher head0.358
Teacher spread0.289 · 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 routes1
Has abstractyes

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