Assessing performance of LEDSA and Radiance method for measuring extinction coefficients in real-scale fire environments
Why this work is in the frame
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Bibliographic record
Abstract
Two photometric measurement methods (Radiance method and LEDSA) were compared against the established MIREX measurement apparatus under controlled laboratory conditions to assess their capability of measuring extinction coefficients in real-scale fires on a temporal and spatial scale. LEDSA is a tomographic technique based on direct measurements of light intensity from individual LEDs using commercially available DSLR cameras. By discretizing the domain into horizontal layers with homogeneous smoke density, values of the extinction coefficient can be computed using an inverse model based on Beer Lambert’s law. The Radiance method involves measuring the contrast of light and dark areas in images and/or video footage. It was originally developed to investigate the descent of the smoke layer in high-temperature fire events. In this work, the extinction coefficient was deduced from measurements on a contrast board by a straightforward analytical approach. Both methods were shown to yield similar extinction coefficient results in line with the MIREX for an EN 54-7 TF5 n-heptane fire. The Radiance method is able to generate accurate patterns but not values for a TF2 wood smouldering fire, while LEDSA is generally able to reflect the MIREX measurement values, yet requires higher computational effort.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it