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Record W2807896397 · doi:10.1098/rsos.172003

Analysis of crotonaldehyde puff-by-puff release in mainstream cigarette smoke under various smoking regimens by high-performance liquid chromatography with the modified QuEChERS method

2018· article· en· W2807896397 on OpenAlexaboutno aff
Chao Li, E'xian Li, Jian Zhang, Ying Tao, Chengming Zhang, Xiaofeng Shen, Ya Liu, Qin Yunhua

Bibliographic record

VenueRoyal Society Open Science · 2018
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsnot available
Fundersnot available
KeywordsCrotonaldehydeSidestream smokeCigarette smokeSmokeQuechersChromatographyChemistryHigh-performance liquid chromatographyEnvironmental scienceMedicineOrganic chemistryEnvironmental healthBiology

Abstract

fetched live from OpenAlex

To study puff-by-puff release characteristics of crotonaldehyde in mainstream cigarette smoke under diverse intensive smoking regimens, we designed an RM20H smoking machine with a puff-by-puff smoke collection unit to automatically trap crotonaldehyde in the mainstream cigarette smoke. Using this process, we trapped, puff-by-puff, crotonaldehyde in mainstream smoke generated by different smoking regimens and quantitatively analysed the levels of crotonaldehyde using high-performance liquid chromatography with a modified QuEChERS sample pretreatment method. On the basis of the crotonaldehyde in each puff, we determined crotonaldehyde's puff-by-puff release characteristics. The results showed that crotonaldehyde's puff-by-puff release remained nearly constant for the International Organization for Standardization mode while increased polynomial trend was seen ( n ≥ 6) under the Massachusetts and Health Canada smoking regimens. The equation fit for various regimens was good ( R 2 > 0.9192). Release characteristics by puff were classified into four categories: (1) first, second and third puffs; (2) fourth and fifth puffs; (3) sixth puff; and (4) seventh and eighth puffs.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.272
Teacher spread0.260 · 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.

Study designBench or experimental
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

Citations2
Published2018
Admission routes1
Has abstractyes

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