Estimating burn severity at the regional level using optically based indices
Bibliographic record
Abstract
During the last decades, the average number of fires per year increased significantly. A twofold increase was observed in the Mediterranean Basin, whereas in the western United States, the increase was fourfold. Regional models for burn severity estimation are necessary to avoid time consuming and costly fieldwork at each individual site. Furthermore, the estimation errors should be assessed by burn severity classes to avoid overestimating models accuracy. To develop such models, this study assessed the relationship between the composite burn index (CBI) and several spectral indices across five burned sites in northeastern Spain. The nonlinear models coupled with spectral indices containing information from the short wavelength infrared provided the best statistical fit of the data at most individual sites and for the pooled data set. The estimation errors for highly burned sites were well below 10%, but for burned sites of low and moderate severity, the errors increased significantly. A strong linear relation was found between burn severity at the plot level and understory and overstory composites. This study demonstrates (i) the model consistency at the regional level and (ii) the need for new estimation methods in areas affected by low to moderate burn severities, even for relatively homogeneous forests.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".