Mapping fuels in the Chihuahuan Desert borderlands using remote sensing, geographic information systems, and biophysical modeling
Bibliographic record
Abstract
This study integrated field, geographic information systems, and remotely sensed data to generate spatially explicit fuel maps for Big Bend National Park in Texas and the Maderas del Carmen Protected Area in Coahuila, Mexico. We used hierarchical cluster analysis, and classification and regression trees to (i) identify the dominant fuel types in each of the study areas and (ii) build spatially explicit predictive fuels maps. Four fuel types were identified that differed significantly in their live and dead fuel characteristics. Spectral characteristics, topographic position, soil moisture, and solar radiation were the major influences on fuel distribution patterns. Fine-fuel loads were highest in open woodlands on lower topographic positions that had high grass cover. The highest shrub loadings were found on exposed, upper topographic positions. Timber-type fuel loads with high 1, 10, 100, and 1000 hour fuels loads dominated high-elevation valley bottoms. The error rates of the maps were approximately 16%, which falls within the range of typical fuel mapping misclassification rates. The map products from this study are currently being used as inputs for landscape-scale fire modeling and for guiding fuel-reduction treatments using fire and fire surrogates, such as thinning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".