Fire regimes of the piñon–juniper woodlands of Big Bend National Park and the Davis Mountains, west Texas, USA
Bibliographic record
Abstract
While piñon woodlands cover much of arid North America, surprisingly little is known about the role of fire in maintaining piñon forest structure and species composition. The lack of region-specific fire regime data for piñon–juniper woodlands presents a roadblock to managers striving to implement process-based management. This study characterized piñon–juniper fire regimes and forest stand dynamics in Big Bend National Park (BIBE) and the Davis Mountains Preserve of the Nature Conservancy (DMTNC) in west Texas. Mean fire return intervals were 36.5 and 11.2 years for BIBE and DMTNC, respectively. Point fire return intervals were 150 years at BIBE and 75 years at DMTNC. Tree regeneration in west Texas piñon–juniper woodlands occurred historically during favorable climatic conditions following fire years. The presence of multiple fire scars on our fire-scar samples and the multicohort stands of piñon suggested that low intensity fires were common. This study represents one of the few fire-scar-based fire regime studies for piñon–juniper woodlands. Our results differ from other studies in less topographically dissected landscapes that have identified stand-replacing fire as the dominant fire regime for piñon–juniper woodlands. This suggests that mixed-severity fire regimes are typical across southwestern piñon forests, and that topography is an important influence on fire frequency and intensity.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".