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Record W2058428797 · doi:10.1080/10934529.2014.928558

The effects of naturally occurring acids on the surface properties of chrysotile asbestos

2014· article· en· W2058428797 on OpenAlexaff
Emma P. Holmes, L. M. Lavkulich

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
KeywordsChrysotileAsbestosBruciteWeatheringMineralOxalic acidHydrochloric acidEnvironmental chemistryMineral dustChemistryMineralogyMaterials scienceEnvironmental scienceInorganic chemistryGeologyMetallurgyGeochemistryMagnesiumOrganic chemistryAerosol

Abstract

fetched live from OpenAlex

Chrysotile asbestos is considered an environmental health hazard. It is postulated that the surface of chrysotile, with its inherent positive charge and chemical content of trace transition metals within the mineral is a causative factor of the concern. Weathering may reduce the negative health effects of chrysotile asbestos, by alteration of the outer brucite layer of the chrysotile. To assess the changes in the surface properties of chrysotile asbestos by simulated weathering, chrysotile was treated with oxalic, hydrochloric, and carbonic acids. Naturally occurring chrysotile, from a mine site and serpentinitic stream sediments from the Sumas River were analyzed and compared. Oxalic acid, a chelating acid, was the most effective at extracting the majority of the trace elements present in the chrysotile, reducing their positive surface charge and producing visible changes at the surface of the fibers as shown by Field Emission Scanning Electron Microsopy (FESEM). Carbonic acid had little effect on the surface properties. Stream environments had minor detectable effects on the surface properties on the chrysotile stream sediments.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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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