Fire history and forest age distribution of an unmanaged <i>Picea abies</i> dominated landscape
Bibliographic record
Abstract
We examined fire history and forest age distribution in an unmanaged, Picea-dominated landscape in the Paanajärvi wilderness, located in northeastern Fennoscandia and northwest Russia. Maximum tree age was determined in 61 systematically located study plots in an area of about 6600 ha. Fire scars were examined in living and dead trees in the study plots and elsewhere in the study area. Charcoal and pollen analyses of peat were performed on samples from two locations. Fires had been rare in the landscape. Nearly half of the dendrochronologically dated fires occurred in a distinct and short period, from 1859 to 1889, in the northeastern part of the area. This nonrandom occurrence of fires, together with the observed signs of past human influence, suggests an anthropogenic origin for the majority of the fires. The fact that 95% of the study area consisted of forests older than 120 years reflects the end of the occurrence of fires in the 1880s. Pollen analysis from the southwestern part of the study area showed that the site had been dominated by Picea at least during the last millennium. Charcoal analysis from the same site indicated that likely more than 1000 years had elapsed since the last fire. In general, the results suggest that the abundance of old forests, with the oldest trees being approximately 300 years of age, belongs to the natural state of a Picea-dominated landscape.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".