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Record W2302198625 · doi:10.2478/cttr-2013-0775

Measurement of the “Tar” and Nicotine Yield of Fine-Cut Smoking Articles - The ISO/TC 126 Collaborative Study 2001

2004· article· en· W2302198625 on OpenAlexfundno aff
Perrie M. Adams

Bibliographic record

VenueBeiträge zur Tabakforschung international · 2004
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersFondation Pour La Conservation Du Saumon Atlantique
Keywordstar (computing)StandardizationFactory (object-oriented programming)StatisticsRepeatabilityYield (engineering)MathematicsOutlierReproducibilityComputer scienceOperations managementToxicologyEngineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract The Technical Committee ISO/TC 126 ‘Tobacco and tobacco products’ of the International Organization for Standardization set up Working Group 7 to develop and test a method for the determination of ‘tar’ and nicotine yield when fine-cut smoking articles (FCSAs) are smoked by routine-analytical smoking machines. FCSAs are individually made by consumers using tobaccos manufactured for enclosure in paper wrappers (i.e. leaflets and tubes - with or without filters). This paper records the history of method development, defines terms and explains the differences between measurements made on FCSAs and commercially factory made cigarettes. Individual consumer making practices are represented by the specification of four hand-made article types using two wrappers of different physical properties each with either 400 mg or 750 mg tobacco. ISO standards require estimates of the repeatability ( r ) and reproducibility ( R ) of analytical methods, so the working group has conducted a collaborative study. The values of r and R were determined using the definitions given in ISO 5725-2 (18). Twenty laboratories took part and tested three different commercial tobacco blends according to the standard method. The detailed protocol for the study is given in Annex A. Annex B is an illustrated manual used as a training aid for laboratory personnel with little experience of making FCSAs. After the smoking was completed, the laboratory data were examined for outlying points by the methods of Grubbs and Cochran. It was found necessary to exclude the data from one laboratory prior to statistical analysis otherwise the incidence of outliers was low (1.41%). Values of r and R were calculated from the results of one-way analyses of variance (within and between laboratories) for smoke nicotine and nicotine-free dry particulate matter (NFDPM). The data for the 19 laboratories are given in Annex C. The quality of the data is high as is shown by the comparisons made between the r and R values for the articles and those for the monitor test pieces which were used to confirm that smoking conditions run-to-run were satisfactory. The range of NFDPM and smoke nicotine yields resulting from the four designs of articles is large for each of the three blends. The measured NFDPM yields of the four designs are ranked in the same order by all of the laboratories and all pairs are statistically different for every laboratory. For smoke nicotine the ranking order is the same for all laboratories. All pairs are statistically different in all but three laboratories for which only the 400 mg articles were not statistically different. The r and R data are given in tables and in graphical representation. The graphs show that r and R increase with the mean values of NFDPM and smoke nicotine yields. Such a trend has also been found in studies using factory-made cigarettes. A comparison of results shows that r values for articles are approximately 40% greater and R values about 100% greater. The variability of individual hand-made articles in this study is likely to be much greater than the variability of machine-made matched cigarette samples. The method detailed in ISO 15592-3, therefore, is satisfactory for general adoption even in laboratories where there has been little previous experience.

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 score0.069
Threshold uncertainty score0.339

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 studies0.0000.000
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 head0.047
GPT teacher head0.301
Teacher spread0.255 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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