Historical high-severity fire patches in mixed-conifer forests
Bibliographic record
Abstract
Two ends of the fire regime spectrum are a frequent low-intensity fire regime and an infrequent high-intensity fire regime, but intermediate fire regimes combine high- and low-severity fire over space and time. We used fire-scar and tree-age data to reconstruct fire regime attributes of mixed-conifer and aspen forests in the North Rim area of Grand Canyon National Park, with a goal of estimating patch sizes of historical high-severity fire and comparing them with modern patch sizes. We used three methods based on (i) aspen groves, (ii) even-aged stands, and (iii) inverse distance weighting, to estimate occurrence and patch sizes of historical high-severity fire. Evidence of high-severity fire was common in the 1800s, and high-severity fire years were associated with drought. High-severity fire patch sizes likely ranged from 10−1 to 102 ha. However, the forest is quite young, and we cannot rule out a historical large high-severity fire that could have reinitiated much of the 1400 ha study area. Fire scars, which are indicative of low-severity fire, were also common. Historical fire was likely heterogeneous across the landscape. Maintaining heterogeneity of fire severity, size, and frequency would promote heterogeneity of forest structure and composition and resilience to future disturbances.
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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) | 0.000 | 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".