Statistical Analysis of Microbial Volatile Organic Compounds in an Experimental Project: Identification and Transport Analysis
Bibliographic record
Abstract
This paper is based on an experimental project that investigated the capacity of wood frame stud walls to restrain the movement of mould products from the stud cavity into an investigative chamber, representing the indoor environment. While the programme of the research includes the investigation of spores and microbial volatile organic compounds (MVOCs), this paper reports only the analysis of MVOCs. Twenty full-scale wall specimens were constructed, incorporating six experimental factors (air leakage path patterns, mould presence, insulation, vapour barrier, sheathing material, and ambient humidity conditions). For each specimen, four VOC samples were taken simultaneously from the sampling chamber and from the stud cavity through the external sheathing, and one sample was taken from the background laboratory air for comparison. Multiple regression analysis was applied to identify the MVOCs, and subsequently to evaluate the effect of construction factors on the movement of these MVOCs through the envelope. Box—Cox transformation was applied prior to the regression analysis to normalise the data. Five VOCs were identified as related to the presence of mould in the stud cavity, at 5% level of significance. The transport of these MVOCs from the sampling chamber to the cavity was confirmed. However, no significant effect of the parameters related to wall configurations was detected.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".