An Improved Approach for Selecting and Validating Burn Severity Indices in Forested Landscapes
Bibliographic record
Abstract
Burn severity maps based on remotely sensed reflectance data provide a useful way for land managers and researchers to represent and compare spatial variation in fire effects among wildfires and prescribed fires. A need exists for an objective and rigorous selection approach that ensures the best possible spatial predictions of burn severity. The aim of this study was to present and test a methodology for selecting the optimal burn severity index from a suite of calculation and validation options that can be used to produce data for more rigorously comparing ecological effects of fire that occur in contrasting phenologies. In our study, we cross-validated remote sensing data with field data and we tested the predictive ability of 12 cross-validated index calibrations that were generated using common statistical approaches, to predict field-measured burn severity indices collected at burned and unburned areas in New Jersey Pinelands National Reserve. We demonstrate the utility of our approach, provide convincing evidence for the use of CBI as a field-based index over WCBI, and provide a cross-validated method for calculating burn severity in this vegetation type that can be used by managers and researchers.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 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".