Can scar-based fire history reconstructions be biased? An experimental study in boreal Scots pine
Bibliographic record
Abstract
Determining forest fire history is commonly based on fire scar dating with dendrochronological methods. We used an experimental setup to investigate the impacts of low-intensity prescribed fire on fire scar formation 8 years after fire in 12 young managed Scots pine (Pinus sylvestris L.) stands. Five stands were between 30 and 35 years old and seven were 45 years old at the time of burning. A total of 217 fire scars were recorded in 142 trees. The number of separate scars per tree originating from a single fire ranged from 1 to 6, with 67% of the trees having just one scar. The proportion of fire-scarred trees out of all trees per plot ranged from 0% to 30%, averaging 16.5% in young stands and 2.8% in older stands. Four of the 12 burned plots did not have any trees with fire scars, and these were all in the older age group. This means that in the older stands, in only three of seven plots (43%) did the fire leave scars from which fire can potentially be detected and dated afterwards. Our results suggest that fire scar dating in Scots pine dominated forests may underestimate fire frequency, area, and the importance of historically common low-intensity surface fires in dendrochronological reconstructions of past fire histories.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| 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".