Climate–fire interactions during the Holocene: a test of the utility of charcoal morphotypes in a sediment core from the boreal region of north-western Ontario (Canada)
Bibliographic record
Abstract
Charcoal accumulation rates and fire-return intervals were calculated from total charcoal and charcoal morphotypes over the Holocene, from a well-dated sediment core from Lake 239 located in north-western Ontario, and compared with previously published independent climate reconstructions. Both total and morphotype analysis show a two-to-three fold increase in accumulation rates in the early-to-mid Holocene (range: 1 to 6 pieces cm–2 year–1) compared with the early and late Holocene (range: 0 to 2 pieces cm–2 year–1). Fire-return intervals and fire frequencies calculated during these periods, based on peak analysis, showed very different trends. The fire-return interval based on Type M charcoal, a morphotype associated with primary charcoal deposition, was high during the early and late Holocene, and low from ~7500 to 4000 cal year BP, with high inferred fire frequency during the warm mid-Holocene (~12.5 fires per 1000 years), compared with <5 fires per 1000 years over the rest of the Holocene, whereas fire-return interval and fire frequency based on total charcoal did not show patterns consistent with climate. These results suggest that a two- to three-fold increase in fire frequency would not be unexpected in the future under a predicted warmer climate.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".