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Record W1968697317 · doi:10.1021/es9018817

Hydrous Silica Coatings: Occurrence, Speciation of Metals, and Environmental Significance

2009· article· en· W1968697317 on OpenAlexaffabout
Michael Schindler, Jennifer L. Durocher, Y. A. Abdu, F. C. Hawthorne

Bibliographic record

VenueEnvironmental Science & Technology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsLaurentian UniversityUniversity of Manitoba
Fundersnot available
KeywordsSchwertmanniteHydroxideSilicateTailingsMetalMetalloidSorptionMineralogyNucleationInorganic chemistryMaterials scienceMetallurgyEnvironmental chemistryGoethiteChemistryAdsorption

Abstract

fetched live from OpenAlex

Si-enriched coatings form on the surface of silicate minerals under acidic conditions. Although they are often only a few nanometers thick, their large specific surface area may control the interaction between silicate minerals in acidic soils, aquifers, and mine tailings. Micrometer thick, hydrous-silica coatings occur on the surface of a granite outcrop in contact with acidic pond water at the Coppercliff mine-tailings area in the Greater City of Sudbury, Ontario, and are ideal to study the concentration and speciation of metals and metalloids inside Si-enriched coatings. These coatings have higher average concentrations of Cr, Mn, Co, Ni, Cu, Zn, and Pb than coatings composed of schwertmannite, Fe(8)O(8)(OH)(4.4)(SO(4))(1.8) (H(2)O)(8.4). Microscopic and spectroscopic examination of the hydrous-silica coating indicates the occurrence of Fe- and Cu-Zn-oxy-hydroxide particles, tetrahedrally coordinated Fe(3+) and a high proportion of M-O-Si bonds (M = metal). These observations suggest that metals occur either finely distributed in the hydrous-silica matrix or in oxy-hydroxide particles. The latter particles are products of the diffusion of metals into the hydrous silica and the subsequent nucleation of oxy-hydroxide phases.

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 categoriesScience and technology studies
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 score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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
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

Citations33
Published2009
Admission routes2
Has abstractyes

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