Indoor/Outdoor Relationships, Trends, and Carbonaceous Content of Fine Particulate Matter in Retirement Homes of the Los Angeles Basin
Bibliographic record
Abstract
Hourly indoor and outdoor fine particulate matter (PM2.5), organic and elemental carbon (OC and EC, respectively), particle number (PN), ozone (O3), carbon monoxide (CO), and nitrogen oxide (NOx) concentrations were measured at two different retirement communities in the Los Angeles, CA, area as part of the Cardiovascular Health and Air Pollution Study. Site A (group 1 [G1]) was operated from July 6 to August 20, 2005 (phase 1 [P1]) and from October 19 to December 10, 2005 (P2), whereas site B (group 2 [G2]) was operated from August 24 to October 15, 2005 (P1), and from January 4 to February 18, 2006 (P2). Overall, the magnitude of indoor and outdoor measurements was similar, probably because of the major influence of outdoor sources on indoor particle and gas levels. However, G2 showed a substantial increase in indoor OC, PN, and PM2.5 between 6:00 and 9:00 a.m., probably from cooking. The contributions of primary and secondary OC (SOA) to measured outdoor OC were estimated from collected OC and EC concentrations using EC as a tracer of primary combustion-generated OC (i.e., "EC tracer method"). The study average outdoor SOA accounted for 40% of outdoor particulate OC (40-45% in the summer and 32-40% in the winter). Air exchange rates (hr(-1)) and infiltration factors (Finf; dimensionless) at each site were also determined. Estimated Finf and measured particle concentrations were then used in a single compartment mass balance model to assess the contributions of indoor and/or outdoor sources to measured indoor OC, EC, PM2.5, and PN. The average percentage contributions of indoor SOA of outdoor origin to measured indoor OC were approximately 35% (during G1P1 and G1P2) and approximately 45% (for G2P1 and G2P2). On average, 36% (G2P1) to 44% (G1P1) of measured indoor OC was composed of outdoor-generated primary OC.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".