Bibliographic record
Abstract
In protected areas where ecologically beneficial fire impacts are promoted through efforts to accommodate or support fire processes on the landscape, fire and park managers who are challenged to manage active fires in limited areas must assess fire risks, predict fire effects, and evaluate the long-term ecological consequences of fire management alternatives. In this thesis, three fire regime models designed to support fire management planning in protected areas are developed and presented. First, the statistics of extreme values was applied to historical fire and weather data to characterize dry-spell and fire extremes in the province of Ontario. The results of this landscape-level analysis of fire processes indicated that regional differences in the magnitude of extreme fire events is related to extreme dry-spell events, ecological classification, and level of fire protection. Second, postfire field study data were used to develop a logistic regression model to predict white pine (Pinus strobus L.) mortality following an intense surface fire. Results of this stand-level analysis of fire effects indicated that tree size and fire intensity are key determinants of postfire mortality, corroborating existing evidence that mature white pine are resistant to intense surface fire. Third, a simple model of white pine stand dynamics was developed to evaluate prescribed fire scheduling strategies for achieving ecological objectives using both simulation modeling and goal programming methods. Results of this stand-level analysis of long-term fire processes illustrated the usefulness of simulation models for identifying a preferred fire return interval within the range of natural intervals, given specific management objectives. Simulation results supported the intermediate disturbance hypothesis and suggest that in forest stands subject to regular, non-catastrophic disturbance, frequency of disturbance may be a key mitigating factor in the diversity-stability relationship. Goal programming results demonstrated that optimal prescribed fire schedules based on habitat and visual quality goals can produce ecological outcomes consistent with outcomes associated with schedules based on a preferred natural fire return interval. This multi-objective approach to prescribed fire scheduling in an uneven-aged stand is a novel formulation of the optimal fire management problem.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".