Bioaerosols in the Barcelona subway system
Bibliographic record
Abstract
Subway systems worldwide transport more than 100 million people daily; therefore, air quality on station platforms and inside trains is an important urban air pollution issue. We examined the microbiological composition and abundance in space and time of bioaerosols collected in the Barcelona subway system during a cold period. Quantitative PCR was used to quantify total bacteria, Aspergillus fumigatus, influenza A and B, and rhinoviruses. Multitag 454 pyrosequencing of the 16S rRNA gene was used to assess bacterial community composition and biodiversity. The results showed low bioaerosol concentrations regarding the targeted microorganisms, although the bacterial bioburden was rather high (104 bacteria/m3). Airborne bacterial communities presented a high degree of overlap among the different subway environments sampled (inside trains, platforms, and lobbies) and were dominated by a few widespread taxa, with Methylobacterium being the most abundant genus. Human-related microbiota in sequence dataset and ascribed to potentially pathogenic bacteria were found in low proportion (maximum values below 2% of sequence readings) and evenly detected. Hence, no important biological exposure marker was detected in any of the sampled environments. Overall, we found that commuters are not the main source of bioaerosols in the Barcelona subway system.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".