Beyond “fire temperatures”: calibrating thermocouple probes and modeling their response to surface fires in hardwood fuels
Bibliographic record
Abstract
The maximum temperatures of thermocouples, temperature-sensitive paints, and calorimeters exposed to flames in wildland fires are often called “fire temperatures” but are determined as much by the properties and deployment of the measurement devices as by the fires themselves. Rather than report device temperatures that are not generally comparable among studies, we show that maximum and time-integrated temperatures of relatively thick (4.8 mm diameter) type-K thermocouple probes (TCPs) can be calibrated to estimate fuel consumption and fire line intensity in surface fires. Although reporting standard fire characteristics is an improvement over reporting device temperatures, TCPs are not ideal instruments for monitoring surface fires, because they provide only point estimates of fire behavior and must be calibrated for different fire environments, TCP characteristics, and deployments. To illustrate how TCPs respond to fires and to point the way towards a more general calibration method, we report results from a numerical model that accurately simulated TCP response to a spreading surface fire.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".