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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".