Bibliographic record
Abstract
Fire scars are widely used to reconstruct fire history, yet patterns of scarring are poorly understood, hampering effective sampling and analysis. Factors that influence the probability a tree will receive a scar (SP) and the fraction of trees that scar (SF) are little studied. We analyzed scarring in 16 fires in ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) forests in northern Arizona. SP was significantly related to char height, presence of a preceding scar, tree diameter, and years since a preceding fire. Mean SF was 0.375, but varied from 0.121 to 0.728, with SF significantly higher with higher mean char height, larger scar dimensions, higher fire severity, larger tree diameter, and where no preceding fire had burned within 30 years. The expected healing times exceeded 55 years for 33% of scars and 100 years for 11% of scars. Scars with a preceding scar were 38% larger than new scars, with expected healing about 20–25 years longer. Scars were clustered, particularly at scales from >20 to >40 m. Scar directions generally aligned with fire-spread directions, which were complex. Variability in SF complicates fire-history methods that use fire counts rather then area burned. Methods that account for spatial and temporal variability in the abundance of evidence are needed.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".