Determination of major and minor components of smoke from full-scale fire tests of furnishings by FTIR spectroscopy
Bibliographic record
Abstract
Over the last half of the 20th century, there was an increase in the use of man-made polymers, foams, composites and modified (fire-retarded) traditional materials for furnishings. With the increasing use of new materials, the measurement of a broader range of combustion products is required in small- and full-scale fire tests to provide the basis for hazard assessments. Recently, FTIR (Fourier Transform Infrared) spectrometers are being used to provide additional smoke gas analysis. This approach can provide continuous quantitative monitoring for the fire gases that have characteristic spectral bands in the infrared region of the spectrum. An FTIR spectrometer was used for smoke gas analysis on a series of full-scale room fire tests with lining materials typically found in retail outlet. It was also used for a series of tests using residential furnishings (mattresses and sofas). In the latter case, tests were conducted using a calorimeter facility as well as in a ten-storey test facility. In this paper, the CO and CO2 concentrations measured using the FTIR are compared to the results obtained using the traditional IR analyzer. The results for other selected fire products and by-products will also be discussed, including the dependence of their emissions on fire conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".