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Record W2483885118 · doi:10.1346/cms-wls-17.1

Microbial Silicification at Hot Springs

2010· book-chapter· en· W2483885118 on OpenAlexaff
Kurt O. Konhauser, Stefan V. Lalonde

Bibliographic record

VenueClay Minerals Society eBooks · 2010
Typebook-chapter
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiology

Abstract

fetched live from OpenAlex

Abstract A wide variety of ancient and modern hot spring systems are characterized by authigenic silica precipitation and sinter formation. In these systems, the chemical disequilibrium of venting hydrothermal fluids leads to the nucleation and growth of amorphous silica masses and simultaneously the mineralization, and potential fossilization, of many different types of microorganisms. Source waters originating from deep, hot reservoirs, at equilibrium with quartz, commonly contain dissolved silica concentrations significantly higher than the solubility of amorphous silica at 100°C (approximately 400 ppm; Figure 1) (Gunnarsson and Arnórsson, 2000). Upon the discharge of these fluids at the surface, decompressional degassing and boiling, rapid cooling to ambient temperatures, evaporation, and changes in solution pH all work together to cause the solution to rapidly exceed amorphous silica solubility (Fournier, 1985). Under these conditions, silicic acid dissolved in the monomer form Si(OH)4 spontaneously polymerizes initially to oligomers (e.g., dimmers, trimers and tetramers), and then to polymeric species with spherical diameters of 1-5 nm, as the silanol groups (-Si-OH-) of each oligomer condense and dehydrate to produce the siloxane (-Si-O-Si-) cores of larger polymers. The polymers quickly grow in size such that a bimodal composition of monomers and particles of colloidal dimensions (>5 nm) are generated (Crerar et al., 1981). Depending on the degree of supersaturation, these either remain in suspension, due to the external silanol groups exhibiting a residual negative surface charge due to a low zero point of charge (around pH 2), they coagulate via cation

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations2
Published2010
Admission routes1
Has abstractyes

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