VOC emission from building materials - the impact of specimen variability - a case study
Bibliographic record
Abstract
Significant progress has been made in the development of standardized methods for the testing and analysis of volatile organic compound (VOC) emission characteristics of different building materials. These standardized techniques facilitate product-product comparisons with regard to VOC emissions, and also provide a basis with which to examine the impact of environmental conditions on VOC emissions. Variability of the test specimen itself is one factor that must be considered when evaluating such tests. In an attempt to gauge this effect, a series of samples of oriented strand board (OSB) were collected and subjected to chamber tests for VOC emissions under standardized conditions (23oC, 50% RH, 1 air change per hour, 0.4 m2/m3 loading). Specimens were collected directly from the mill sites of three different manufacturers. Repeat samples were also collected from the same retail outlet on three separate occasions (same manufacturer, 3 different production dates), from separate panels produced on the same production date, and from multiple locations within the same panel. Variability in the VOC emissions from these samples was found to exceed the analytical uncertainty by an order of magnitude in certain cases.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".