Application of narrow‐spectrum illumination and image processing to measure surface char formation in lateral ignition and flame spread tests
Bibliographic record
Abstract
The Lateral Ignition and Flame Spread Test (LIFT) is used to characterize fire ignition and flame spread on solid materials. This test requires the operator to visually monitor the flame spread over a combustible material and manually record the position of the flame during an experiment. Visual inspection limits the quantity of data obtained from a test and introduces uncertainty in the measurement. In this study, we use narrow-spectrum light with a peak wavelength of 450 nm and a digital camera with frequency-matched optical filters to capture images of surface charring, which underlies the flaming combustion, in a LIFT apparatus. The imaging technique reduced unwanted energy emissions from the flame in the visible light spectrum, allowing the test operator to directly view the charring of the material; which is otherwise hidden behind the flames. We describe data processing routines to analyze the sequences of high-resolution images. The method improves temporal and spatial resolution of the surface charring compared to visual observations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".