Inferring energy incident on sensors in low-intensity surface fires from remotely sensed radiation and using it to predict tree stem injury
Bibliographic record
Abstract
Remotely sensed radiation, attractive for its spatial and temporal coverage, offers a means of inferring energy deposition in fires (e.g. on soils, fuels and tree stems) but coordinated remote and in situ (in-flame) measurements are lacking. We relate remotely sensed measurements of fire radiative energy density (FRED) from nadir (overhead) radiometers on towers and the Wildfire Airborne Sensor Program (WASP) infrared camera on a piloted, fixed-wing aircraft to energy incident on insitu, horizontally oriented, wide-angle total flux sensors positioned ~0.5 m above ground level. Measurements were obtained in non-forested herbaceous and shrub-dominated sites and in (forested) longleaf pine (Pinus palustris Miller) savanna. Using log–log scaling to reveal downward bias, incident energy was positively related to FRED from nadir radiometers (R2 = 0.47) and WASP (R2 = 0.50). As a demonstration of how this result could be used to describe ecological effects, we predict stem injury for turkey oak (Quercus laevis Walter), a common tree species at our study site, using incident energy inferred from remotely sensed FRED. On average, larger-diameter stems were expected to be killed in the forested than in the non-forested sites. Though the approach appears promising, challenges remain for remote and insitu measurement.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".