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Record W2752047375 · doi:10.18001/trs.3.4.7

Little Cigars vs 3R4F Cigarette: Physical Properties and HPHC Yields

2017· article· en· W2752047375 on OpenAlexaboutno aff
Samera H. Hamad, Nathan M. Johnson, Margaret E. Tefft, Marielle C. Brinkman, Sydney M. Gordon, Pamela I. Clark, Stephanie Buehler

Bibliographic record

VenueTobacco Regulatory Science · 2017
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsSidestream smokeToxicologyChemistryNicotineSmokeless tobaccoCigarette smokeFood scienceMedicineTobacco useEnvironmental healthBiologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Our objective was to characterize physical properties and semivolatile harmful and potentially harmful constituent yields in the mainstream smoke (MSS) of 4 popular little cigars compared to the 3R4F reference cigarette. METHODS: We used the ISO and Canadian Intense Regimen protocols to generate MSS for Cheyenne (Full Flavor and Menthol) and Swisher Sweets (Original and Sweet Cherry) little cigars; and the 3R4F. We examined physical properties such as length, tobacco filler mass, pressure drop, and ventilation for each product. Nicotine, benzo[a]pyrene, and tobacco-specific nitrosamine (TSNA) yields were determined in the MSS. RESULTS: Little cigars were longer (~15mm), contained more tobacco filler (100-200 mg), and had a higher pressure drop (~1.3X) compared to the 3R4F. Ventilation holes were found only on the filter paper of the 3R4F. Nicotine transmitted to the MSS was similar for all products under the intense smoking protocol. The highest yields of TSNAs and benzo(a)pyrene were measured for the little cigars. CONCLUSIONS: Little cigars may deliver similar levels of nicotine but higher levels of carcinogens to the MSS compared to cigarettes. Thus, previous reports on the toxicity of tobacco smoke based on cigarettes might not apply to little cigar products.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.588

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 studies0.0010.002
Scholarly communication0.0000.001
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 head0.050
GPT teacher head0.297
Teacher spread0.247 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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