Periglacial fires and trees in a continental setting of Central <scp>C</scp>anada, <scp>U</scp>pper <scp>P</scp>leistocene
Bibliographic record
Abstract
Fire is a key factor controlling global vegetation patterns and carbon cycling. It mostly occurs under warm periods during which fuel builds up with sufficient moisture, whereas such conditions stimulate fire ignition and spread. Biomass burning increased globally with warming periods since the last glacial era. Data confirming periglacial fires during glacial periods are very sparse because such climates are likely too cold to favour fires. Here, tree occurrence and fires during the Upper Pleistocene glacial periods in Central Canada are inferred from botanical identification and calibrated radiocarbon dates of charcoal fragments. Charcoal fragments were archived in sandy dunes of central Saskatchewan and were dated >50000-26600 cal BP. Fragments were mostly gymnosperms. Parallels between radiocarbon dates and GISP2-δ¹⁸O records deciphered relationships between fire and climate. Fires occurred either hundreds to thousands of years after Dansgaard-Oeschger (DO) interstadial warming events (i.e., the time needed to build enough fuel for fire ignition and spread) or at the onset of the DO event. The chronological uncertainties result from the dated material not precisely matching the fires and from the low residual ¹⁴C associated with old sample material. Dominance of high-pressure systems and low effective moisture during post-DO coolings likely triggered flammable periglacial ecosystems, while lower moisture and the relative abundance of fuel overshadowed lower temperatures for fire spread. Laurentide ice sheet (LIS) limits during DO events are difficult to assess in Central Canada due to sparse radiocarbon dates. Our radiocarbon data set constrains the extent of LIS. Central Saskatchewan was not covered by LIS throughout the Upper Pleistocene and was not a continental desert. Instead, our results suggest long-lasting periods where fluctuations of the northern tree limits and fires after interstadials occurred persistently.
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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 | 0.002 |
| 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".