Fire-scar formation in Jeffrey pine – mixed conifer forests in the Sierra San Pedro Mártir, Mexico
Bibliographic record
Abstract
Little is known about the probability of fire-scar formation. In this study, we examined all mixed conifer trees for fire-scar formation in a 16 ha watershed that burned as part of a 2003 wildfire in Sierra San Pedro Mártir National Park (SSPM), Mexico. In addition, we examine the probability of fire-scar formation in relation to the previous fire interval in forests in the SSPM and Sierra Nevada. Within the 16 ha SSPM watershed, 1647 trees were assessed (100% census) for new fire scars. The SSPM wildfire burned around the base of 78% of the trees, but only 8% developed a new fire scar. Although the years from tree germination to first fire scar could potentially represent a fire-free period, there is clear evidence from this study that the inclusion of this interval when computing fire statistics is not justified. When the time since previous fire was <10 years, 10–30 years, and >57 years, the probability of rescarring was approximately 0.05, 0.5, and 0.75, respectively. In areas where fires were frequent (<10 years), fire frequencies derived from fire scars will likely underestimate true fire frequency, at least in forests that are similar to those studied here.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".