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Record W1976554320 · doi:10.1080/15459620601115743

Factors Affecting the Accuracy of Airborne Quartz Determination

2006· article· en· W1976554320 on OpenAlexaff
Stepan Reut, R.A. Stadnichenko, Derek G. Hillis, Peter Pityn

Bibliographic record

VenueJournal of Occupational and Environmental Hygiene · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsOptech (Canada)
FundersNational Institute for Occupational Safety and Health
KeywordsCoefficient of variationSampling (signal processing)Fourier transform infrared spectroscopyEnvironmental scienceAnalytical Chemistry (journal)Detection limitParticle sizeCorrelation coefficientMaterials scienceStatisticsChemistryMathematicsEnvironmental chemistryEngineeringTelecommunicationsChemical engineering

Abstract

fetched live from OpenAlex

Samples collected in a foundry were used to analyze sources of variation and factors influencing the overall accuracy of sampling results. Air samples were analyzed by Fourier Transform Infrared Spectroscopy (FTIR) using NIOSH Method 7602 to study particle size effects, analytical precision, sampling equipment performance, and production factors. The FTIR technique provides accuracy when silica particle size is taken into consideration. In this case, the variability due to analytical factors is small compared with other sources of error. The typical coefficient of variation of the analytical procedure is 0.08; variation associated with sampling reaches 0.21; and interday coefficient of variation can be as high as 0.48. The IR method has advantages over XRD analysis, including cost effectiveness, sensitivity, and a lower detection limit.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.089
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
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 head0.015
GPT teacher head0.239
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2006
Admission routes1
Has abstractyes

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