The effects of forest fuel connectivity on spatiotemporal dynamics of Holocene fire regimes in the central boreal forest of North America
Bibliographic record
Abstract
Understanding fire regime dynamics is central to predicting forest structure and the compositional dynamics of boreal forests. Spatial and temporal variations in fire frequency in central Canadian boreal forests over the last 10 000 years were examined to evaluate the influence of bottom-up controls on the regional fire regime. We analysed macroscopic charcoal larger than 160 μm from sediment cores from six lakes to reconstruct fire history and performed GIS analysis of regional landscape features to investigate how fire frequency has changed temporally and how non-climatic factors may have affected long-term fire frequency. Our generalized linear mixed model revealed that temporal changes in fire return intervals (FRIs) were highly dependent on landscape connectivity as inferred through the abundance of natural firebreaks in the form of open water lakes and wetlands. FRIs did not change significantly among highly connected landscapes throughout the Holocene; in contrast, FRIs were significantly longer among poorly connected landscapes in the early Holocene (10–5 cal ka BP), suggesting that the abundant regional firebreaks limited fire spread. All sites had similar FRIs in the late Holocene. The diminishing influence of firebreaks suggests that the regional climate during the late Holocene has overshadowed the influences of the bottom-up controls on fire activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".