An experimental evaluation of fire history reconstruction using dendrochronology in white oak (<i>Quercus alba</i>)
Bibliographic record
Abstract
Dendrochronological analysis of fire scars on tree cross sections has been critically important for understanding historical fire regimes and has influenced forest management practices. Despite its value as a tool for understanding historical ecosystems, tree-ring-based fire history reconstruction has rarely been experimentally evaluated. To examine the efficacy of dendrochronological analysis for detecting fire occurrence in oak forests, we analyzed tree cross sections from sites in which prescribed fires had been recently conducted. The first fire in each treatment unit created a scar in at least one sample, but the overall percentage of samples containing scars in fire years was low (12%). We found that scars were created by 10 of the 15 prescribed fires, and the five undetected fires all occurred in sites where fire had occurred the previous year. Notably, several samples contained scars from known fire-free periods. In summary, our data suggest that tree-ring analysis is a generally effective tool for reconstructing historical fire regimes, although the following points of uncertainty were highlighted: (i) consecutive annual burns may not create fire scars and (ii) wounds that are morphologically indistinguishable from fire scars may originate from nonfire sources.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".