Multi‐millennial fire frequency and tree abundance differ between xeric and mesic boreal forests in central<scp>C</scp>anada
Bibliographic record
Abstract
Summary Macroscopic sedimentary charcoal and plant macroremains from two lakes, 50 km apart, in north‐westernOntario,Canada, were analysed to investigate fire frequency and tree abundance in the central boreal forest. These records were used to examine the controls over the long‐term fire regime, and vegetative dynamics associated with fire return intervals (FRIs). There were 52 fire events atLakeBen (surrounded by a xeric landscape) between 10 174 calibrated years before present (cal. yearbp) and the present with an averageFRIof 186 years with values oscillating between 40 and 820 years. Forty‐three fire events were recorded atLakeSmall (surrounded by a mesic landscape) between 9972 cal. yearbpand the present with an averageFRIof 229 years and a range of 60–660 years.FRIs atLakeSmall decreased significantly afterc. 4500 cal. yearbp, whereas atLakeBenFRIs remained similar throughout theHolocene. DifferentFRIdistributions and independence in the occurrence of fire events were detected between 10 000 and 4500 cal. yearbpfor the two sites. Between 4500 cal. yearbpand the present, similarFRIs were observed, but fires continued to occur independently. LongerFRIs resulted in declining abundance ofLarix laricinain both landscapes. LongerFRIs resulted in a decline in the abundance ofPicea marianain the xeric landscape, but a marginal increase in the mesic landscape. Abundances ofPinus banksiana,Pinus strobusandBetula papyriferawere unrelated toFRI, underlying that these species maintain their local abundance irrespective of fire frequency. Synthesis. Our results show contrasting fire regime dynamics between a xeric and mesic landscape in central boreal forests,Canada. These results highlight the influence of local factors as important drivers of fire frequency at centennial to millennial scales. Local site factors, especially soil moisture, need to be incorporated into predictive models of vegetation response to climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".