Fire history and secondary vegetation succession in the forest-tundra near Churchill, Manitoba
Bibliographic record
Abstract
Concerns about increasing CO2, in the global atmosphere and how this may affect climatic patterns and ultimately the structure, composition and distribution of the northern boreal forest prompted an examination of secondary succession in a segment of forest-tundra south of Churchill, Manitoba. Fire is the main disturbance in the boreal forest and climate change is expected to have an impact on the frequency of fire and the area burned in these forests. By reconstructing the fire history of these forests, we can evaluate the potential effects a change in fire activity will have upon this landscape. In addition to our lack of knowledge on fire in the forest-tundra of Manitoba, little is also known about the response of the vegetation and forest stand dynamics following fire. The fire history of the Churchill, Manitoba forest-tundra was reconstructed following the determination of the time-since-last-fire (TSLF) dates from 119 locations using dendroecological methods. The dates were used to create a TSLF map. A bootstrapping procedure was used prior to the fire frequency analysis. The point data used for the bootstrap analysis was coded by forest type (black spruce, white spruce and eastern larch). Area data from the TSLF map and frequency data from the bootstrap analysis were converted into 25-year age classes and used to produce reverse cumulative area/frequency distributions to assess possible changes in the fire-cycle. The fire-cycle for homogenous fire periods was calculated using 1) natural fire rotation (area data) and 2) fire frequency analysis fitted to an negative exponential model (area and frequency data). All forest groupings showed a change in the fire-cycle in the AD 1700's to a longer fire-cycle. Using the natural fire rotation (NFR) the fire-cycle has changed from 345 years to 558 years at AD 1725...
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".