Statistical Methods with a Focus on Joint Outcome Modeling and on Methods for Fire Science
Bibliographic record
Abstract
Understanding the dynamics of wildfires contributes significantly to the development of fire science. Challenges in the analysis of historical fire data include defining fire dynamics within existing statistical frameworks, modeling the duration and size of fires as joint outcomes, identifying the how fires are grouped into clusters of subpopulations, and assessing the effect of environmental variables in different modeling frameworks. We develop novel statistical methods to consider outcomes related to fire science jointly. These methods address these challenges by linking univariate models for separate outcomes through shared random effects, an approach referred to as joint modeling. Comparisons with existing approaches demonstrate the flexibilities of the joint models developed and the advantages of their interpretations. Models used to quantify fire behaviour may also be useful in other applications, and here we consider modeling disease spread. The methodologies for wildfire modeling can be used, for example, for understanding the progression of Covid-19 in Ontario, Canada.\nThe key contributions presented in this thesis are the following: 1) Developing frameworks for modelling fire duration and fire size in British Columbia, Canada, jointly, both through modelling using shared random effects and also through copulas. 2) Illustrating the robustness of joint models when the true models are copulas. 3) Extending the framework into a finite joint mixture to classify fires into components and to identify the subpopulation to which the fires belong. 4) Incorporating the longitudinal environmental variables into the models. 5) Extending the method into the analysis of public health data by linking the daily number of Covid-19 hospitalizations and deaths as time series processes using a shared random effect. A key aspect of the research presented here is the focus on extensions of the joint modeling framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".