Performance testing of wildland fire chemicals using a custom-built heat flux sensor
Bibliographic record
Abstract
A simple and effective laboratory test methodology was developed for differentiating wildland fire chemicals based on the ignition time of vegetative fuel samples. The test apparatus consisted of an electric-powered radiant heater that was used to produce a uniform radiant thermal load to ignite the vegetative fuel samples. The samples, treated with wildland fire chemicals, were mounted on to a load cell to determine the transient mass loss during the combustion process. A custom-built heat flux sensor, that was modified and tested to reduce high errors, was used to determine the time to flaming ignition. The time to flaming ignition was also measured using transient mass loss data of the vegetative fuel samples. Statistical t-test analysis was conducted on the time to flaming ignition to determine whether the results were statistically significant for the different chemical treatments. The results indicated that the test methodology allowed for effective differentiation between the wildland fire chemical treatments by comparing their mean ignition times. The narrow standard deviations of the average ignition times suggested that the test methodology was able to produce repeatable results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".