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Record W2079432996 · doi:10.1080/08958370600835252

Mineral–Fluid Interaction in the Lungs: Insights From Reaction-Path Modeling

2006· article· en· W2079432996 on OpenAlexaff
Scott A. Wood, A. E. Taunton, Charles Normand, Mickey E. Gunter

Bibliographic record

VenueInhalation Toxicology · 2006
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsUniversité du Québec à Montréal
FundersNational Institute of Environmental Health Sciences
KeywordsChrysotileTremoliteTalcChemistrySaturation (graph theory)MineralogyMineralPhase diagramThermodynamicsPhase (matter)GeologyAsbestosMaterials scienceMetallurgyOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Thermodynamic modeling, in conjunction with available kinetic information, has been employed to investigate the fate of chrysotile and tremolite in the human lung. In particular, we focus on mineral-fluid reactions using techniques borrowed from geochemistry, including calculation of saturation indices, activity-ratio phase diagrams, and reaction-path modeling. Saturation index calculations show that fresh lung fluid is undersaturated with respect to both tremolite and chrysotile and these minerals should dissolve, in accordance with conclusions from previous work described in the literature. Modeling of reaction paths in both closed and open systems confirms previous suggestions that chrysotile dissolves faster than tremolite in lung fluid, which offers an explanation for the apparent increase in tremolite/chrysotile ratios in lungs of miners and millers over time. However, examination of activity-ratio phase diagrams and reaction-path model calculations raises the possibility not only that minerals dissolve congruently in lung fluid, but that secondary minerals such as talc or various Ca-Mg carbonates might potentially form in lung fluid as asbestiform minerals dissolve.

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 score0.569
Threshold uncertainty score0.255

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 studies0.0000.000
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 head0.016
GPT teacher head0.267
Teacher spread0.251 · 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

Citations13
Published2006
Admission routes1
Has abstractyes

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