[A investigation of thirdhand smoke pollution in 3 types of places of Nanjing, 2014].
Bibliographic record
Abstract
OBJECTIVE: To investigate thirdhand smoke (THS) pollution in certain places of Nanjing, as well as to analyze its distribution characteristics. METHODS: From March to May, 2014, we selected 3 types of places (residencies, public places and transportation vehicles) that were close to people's living in Jianye,Yuhua,Jiangning,Xuanwu,Gulou and Pukou districts of Nanjing city.For each of the above 3 types of places, 2-3 smoking and non-smoking (smoking ban) locations were investigated, totally 51 locations, 9-10 samples were collected each location, totally 477 samples. The surface wipe sampling method in conjunction with liquid chromatography-tandem mass spectrometry (LC-MS/MS) was utilized to quantify the levels of nicotine that served as the tracer of THS pollution.One-way ANOVA and t-tests were employed to compare the levels of nicotine collected at different places and locations. RESULTS: Totally 477 samples were collected in this study, of which 27.0% was from residencies (129/477), 61.0% (291/477) from public places and 11.9% (57/477) from transportations. The levels of indoor surface nicotine in smoking residences, public places and transportations were (214 ± 55),(1 408 ± 177) and (1 511 ± 785) µg/m(2), respectively, which were all higher than those in the corresponding non-smoking places ((23 ± 9),(62 ± 11), and (46 ± 15) µg/m(2); t values were 13.79, 13.15, 3.45, respectively. P values were <0.001, <0.001 and 0.006, respectively).In the smoking places, the levels of surface nicotine on walls, desks, sofas, cabinets, door backsides and air conditioning openings were (171 ± 62),(232 ± 38),(373 ± 151),(903 ± 239), (978 ± 212), (1 721 ± 517) µg/m(2) (F = 7.06, P = 0.009).In the smoking condition, the levels of surface nicotine collected from public places were higher (F = 9.25, P = 0.024), while under non-smoking (smoking ban) conditions, the levels of surface nicotine collected from residences were lower (F = 7.88, P < 0.001). CONCLUSION: THS pollution was widespread in public places, residences and transportations in Nanjing city, which was more serious in the smoking environments than non-smoking (smoking ban) environments; the contamination was less serious in non-smoking (smoking ban) private residences; in the smoking condition, the levels of surface nicotine were relatively high at locations close to air conditioning openings, door backsides and cabinets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".