Comparing methods of reconstructing fire history using fire scars in a southwestern United States ponderosa pine forest
Bibliographic record
Abstract
Fire scars have been used to understand the historical role of fire in ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.) ecosystems, but sampling methods and interpretation of results have been criticized for being statistically invalid and biased and for leading to exaggerated estimates of fire frequency. We compared "targeted" sampling, random sampling, and grid-based sampling to a census of all 1479 fire-scarred trees in a 1 km 2 study site in northern Arizona. Of these trees, 1246 were sufficiently intact to collect cross-sections; of these, 648 had fire scars that could be cross-dated to the year of occurrence in the 200-year analysis period. Given a sufficient sample size (approximately n ≥ 50), we concluded that all tested sampling methods resulted in accurate estimates of the census fire frequency, with mean fire intervals within 1 year of the census mean. We also assessed three analytical techniques: (1) fire intervals from individual trees, (2) the interval between the tree origin and the first scar, and (3) proportional filtering. "Bracketing" fire regime statistics to account for purported uncertainty associated with targeted sampling was not useful. Quantifying differences in sampling approaches cannot resolve all the limitations of fire-scar methods, but does strengthen interpretation of these data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".