The reconstruction of burned area and fire severity using charcoal from boreal lake sediments
Bibliographic record
Abstract
Although lacustrine sedimentary charcoal has long been used to infer paleofires, their quantitative reconstructions require improvements of the calibration of their links with fire regimes (i.e. occurrence, area, and severity) and the taphonomic processes that affect charcoal particles between the production and the deposition in lake sediments. Charcoal particles >150 µm were monitored yearly from 2011 to 2016 using traps submerged in seven head lakes situated in flat-to-rolling boreal forest landscapes in eastern Canada. The burned area was measured, and the above-ground fire severity was assessed using the differentiated normalized burn ratio (dNBR) index, derived from LANDSAT images, and measurements taken within zones radiating 3, 15, and 30 km from the lakes. In order to evaluate potential lag effects in the charcoal record, fire metrics were assessed for the year of recorded charcoal recording (lag 0) and up to 5 years before charcoal deposition (lag 5). A total of 92 variables were generated and sorted using a Random Forest-based methodology. The most explanatory variables for annual charcoal particle presence, expressed as the median surface area, were selected. Results show that, temporally, sedimentary charcoal accurately recorded fire events without a temporal lag; spatially, fires were recorded up to 30 km from the lakes. Selected variables highlighted the importance of burned area and fire severity in explaining lacustrine charcoal. The charcoal influx was thus driven by fire area and severity during the production process. The dispersion process of particles resulted mostly of wind transportation within the regional (<30 km) source area. Overall, charcoal median surface area represents a reliable proxy for reconstructing past burned areas and fire severities.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".