Late Holocene tundra fires and linkages to climate and vegetation in Northern Alaska
Bibliographic record
Abstract
Charcoal particles in lake sediments can reveal past fires and linkages to climate and vegetation change. We use analyses of charcoal accumulation rates from two lakes on the Alaskan North Slope to reconstruct past fire activity, and charcoal morphology to identify changes in fuel sources. Charcoal peak analyses were used to calculate individual fire-return intervals (FRIs; years between fire) and mean FRIs (mFRIs) with 95% confidence intervals at local and regional scales. The Lake I4 core (RTS7U2, basal age 7046 cal year B.P.) shows shorter FRIs after ∼3000 cal year B.P. based on the >90 µm charcoal size fraction (regional burning), which coincides with Neoglacial cooling and decreasing moisture. A second higher-resolution core from nearby Kirk Lake (RTS5U3, basal age 743 years) captures short FRIs (mFRI = 198 (105–133) years), suggesting frequent burning compared to the late Holocene portion of Lake I4 core (mFRI = 378 (294–455) years). mFRIs from the larger charcoal size fractions (>125 µm; local burning) at both sites overlap with modern fire cycles observed in the region over the past 82 years. However, the Kirk Lake watershed burned more frequently than other sites in the region, likely related to abundant local shrub cover. These analyses suggest that tundra fires are related to climate variability, but local-scale feedbacks with vegetation can result in heterogenous burning, with implications for ongoing Arctic greening and warming.
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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 | 0.001 | 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".