Chemical Composition, Spatial Homogeneity, and Growth of Indoor Surface Films
Bibliographic record
Abstract
Abstract Organic films on indoor surfaces are ubiquitous, but details about their composition and growth over timescales less than a month are not fully understood. To address these gaps in understanding, organic film samples in an apartment unit were collected over the course of 17 days using passive samplers and analyzed in a non-targeted manner using direct analysis in real-time mass spectrometry (DART-MS). Overall, the chemical composition observed across various locations within the apartment are very similar. Mass spectra also show clear evidence for the growth of semi-volatile compounds from natural sources and consumer products, such as carboxylic acids and plasticizers. Certain compounds show evidence for equilibration, mostly consistent with surface partitioning models based on octanol–air partition coefficients (Koa). Compounds which have higher molecular weight or larger Koa values tend to equilibrate later, leading to an overall shift in the composition of the film as a function of collection time. Growth rates of film thickness are at least 0.05 nm/day based on a limited number of individually calibrated ions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".