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Record W2380328407

Retention and Distribution Patterns of Phenolic Compounds in Cigarette Filters

2014· article· en· W2380328407 on OpenAlexaboutno aff
Wen Jian-hu

Bibliographic record

VenueTobacco Science & Technology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsnot available
Fundersnot available
KeywordsPhenolChemistryCatecholFilter (signal processing)Methyl acetateHydroquinoneChromatographyp-CresolOrganic chemistryAcetic acid
DOInot available

Abstract

fetched live from OpenAlex

To study the effects of filter structure on the retention and distribution patterns of phenolic compounds in cigarette filters, the retention rates of normal acetate filter to phenol, o-cresol, m/p-cresol, hydroquinone, resorcin and catechol under ISO and Health Canadian Intense(HCI) smoking regimes were compared, and the spatial distribution patterns of the 7 phenolic compounds in filter were investigated. The retention behavior in peripherally grooved acetate filter, paper-acetate combined filter and ventilated cavity acetate filter was also investigated under ISO smoking regime. The results showed that: 1) The retention rate of normal acetate filter to phenolic compounds under ISO smoking regime ranged from 49% to 87%, and that to phenol and o/m/p-cresol was higher than to the others. Under HCI smoking regime, the retention rate to phenol and o/m/p-cresol increased to a certain extent, while that to the others decreased. 2) The filters in the order of retention rate to phenolic compounds were: normal acetate filter ≈ peripherally grooved acetate filter paper-acetate combined filter ventilated cavity acetate filter. 3) Acetate filter possessed obvious retention selectivity to phenolic compounds, which was the result of the interaction between phenolic compounds and filter materials.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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