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Record W2063122632 · doi:10.1080/10934529.2014.865401

Effects of natural acids on surface properties of asbestos minerals and kaolinite

2014· article· en· W2063122632 on OpenAlexaff
L. M. Lavkulich, Hanspeter E. Schreier, Julie E. Wilson

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2014
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChrysotileKaoliniteAmphiboleChemistryMineralAsbestosClay mineralsAdsorptionActinoliteInorganic chemistryTremoliteMineralogyEnvironmental chemistryMetallurgyEpidoteMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Serpentine, and other asbestos minerals, are considered potential hazards to human respiratory health. It has been postulated that the surface characteristics of these substances, such as surface charge and adsorbed metals, notably Fe and other transition metals, may be the major agents responsible for their toxicity. There is a general consensus that the amphibole group of minerals possesses a greater health risk than serpentines dominated by chrysotile. There have been suggestions that natural processes can alter the surfaces of these minerals and reduce their potency. This study examined the effects of carbonic acid, oxalic acid and hydrochloric acid on the surface characteristics of two trioctahedral minerals, actinolite (amphibole) and chrysotile (serpentine), and compared the results to a non-asbestiform, dioctahedral mineral, kaolinite. Results confirm that the treatments alter the mineral surfaces by changing the zeta potential of the asbestiform minerals from positive to negative and by removing considerable amounts on non-crystalline Fe and other metals. X-ray analyses indicated that mineral structure was little affected by the treatments, and TOF-SIMS revealed that treatments did remove surface adsorbed metals and cations in octahedral coordination within the samples.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.211

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 studies0.0000.001
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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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 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

Citations12
Published2014
Admission routes1
Has abstractyes

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