The effect of fire frequency on local cembra pine populations
Bibliographic record
Abstract
It has been predicted that global climate change will lead to increasing drought in the Alps during the 21st century, as well as an increased fire risk, fires being currently rare in these mountains. Herein we describe fire frequency reconstruction using high-resolution analyses of macroscopic sedimentary charcoal, pollen, and plant macrofossils. Sediments were sampled from a subalpine pond within the dry western French Alps. Results show that the early-Holocene expansion of Pinus cembra (7200 calibrated years BP) occurred in Acer/Alnus incana/Betula woodlands, which were affected by fires with moderate mean fire-free intervals (MFFI = 173 +/- 61 yr [mean +/- SE]). Superposed Epoch Analyses show that the abundance of P. cembra macroremains decreased significantly after burning, although they never disappeared entirely. Statistics suggest that fires spread through cembra pine communities; they were not stand-replacing fires but mainly surface fires, probably killing nonreproductive pines. An increase in fire frequency occurred 6740 years ago, when four fires appear to have occurred within 140 years. These fires may have been associated with a regional drought and could have affected the composition of the subalpine forest by depleting the local P. cembra population in the short-term. The predicted increase in drought in the future could, therefore, affect the cembra pine ecosystem in the Alps if fire frequency is reduced to intervals of less than 80 years.
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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 | 0.001 | 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".