Lacustrine charcoal peaks provide an accurate record of surface wildfires in a North European boreal forest
Bibliographic record
Abstract
We evaluated the skills of different palaeofire reconstruction techniques to reconstruct the fire history of a boreal landscape (Russian Karelia) affected by surface fires. The analysis of dated lacustrine sediments from two nearby lakes was compared with independent dendrochronological dating of fire scars, methods which have rarely been used in context of surface fires. We used two sediment sub-sampling volumes (1 and 3.5 cm 3 , wet volumes) and three methods of calculating the Charcoal Accumulation Rate to reconstruct fire histories: CHAR number, charcoal surface area and estimated charcoal volume. The results show that palaeofire reconstructions obtained with fossil charcoal data from lake sediments and dendrochronology are similar and complementary. Dendrochronological reconstruction of fire scars established 12 fire dates over the past 500 years, and paleo-data from lake sediments identified between 7 and 13 fire events. Several ‘false fire events’ were also recorded in the charcoal chronologies, likely because of errors associated with the estimation of the sediment accumulation rate in the unconsolidated part of the sediment. The number of replicates, that is, number of sub-samples and lakes analyzed, had an effect on the number of identified fire events, whereas no effect was seen in the variation in the analyzed sediment volume or the choice of the charcoal-based metric. Whenever possible, we suggest the use of the dendrochronological data as an independent control for the calibration of charcoal peak series, which helps provide more realistic millennia-long reconstruction of past fire activity. We also argue for the use of 1 cm 3 sample volume, a sampling protocol involving sampling of more than one lake, and sufficient number of intra-sample replicates to achieve skilful reconstructions of past fire activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".