Climate forcing of regional fire years in the upper Great Lakes Region, USA
Bibliographic record
Abstract
Background Drivers of fire regimes vary among spatial scales, and fire history reconstructions are often limited to stand scales, making it difficult to partition effects of regional climate forcing versus individual site histories. Aims To evaluate regional-scale historical fire regimes over 350 years, we analysed an extensive fire-scar network, spanning 240 km across the upper Great Lakes Region in North America. Methods We estimated fire frequency, identified regionally widespread fire years (based on the fraction of fire-scarred tree samples, fire extent index (FEI), and synchronicity of fire years), and evaluated fire seasonality and climate–fire relationships. Key results Historically, fire frequency and seasonality were variable within and among Great Lakes’ ecoregions. Climate forcing at regional scales resulted in synchronised fires, primarily during the late growing season, which were ubiquitous across the upper Great Lakes Region. Regionally significant fire years included 1689, 1752, 1754, 1791, and 1891. Conclusions We found significant climate forcing of region-wide fire regimes in the upper Great Lakes Region. Implications Historically, reoccurring fires in the upper Great Lakes Region were instrumental for shaping and maintaining forest resilience. The climate conditions that helped promote widespread fire years historically may be consistent with anticipated climate–fire interactions due 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".