Fire frequency for the transitional mixedwood forest of Timiskaming, Quebec, Canada
Bibliographic record
Abstract
Fire history was reconstructed for a 2500-km2 area at the interface between the boreal coniferous and northern hardwood forests of southwestern Quebec. The fire cycle, the time required for an area equal to the study site to burn once over, was described using a random sampling strategy that included dendrochronological techniques in conjunction with provincial and national government archival data. Physiographic elements were not found to spatially influence fire frequency; however, human land-use patterns were observed to significantly affect the fire frequency. A temporal shift in fire frequency was also detected, which coincided with the period of Euro-Canadian colonization and known extreme dry years for the study site. Additionally, a fire-free period was identified in the most recent times that could be associated with fire suppression and climate change. The estimated cycles (approx. 188314 years) for the southeastern section of the study area were thought to better represent the natural cycles for this transition zone as a result of less anthropogenic influence. The importance of gap-type dynamics becomes evident with the increased presence of old-growth forest, given the derived fire cycle estimations for the region. Even-aged management with short rotations, consequently, is questioned because fire cycle estimations suggest more complex harvest systems using an ecosystem management approach.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".