Visualization tools for assessing the Markov property: sojourn times in the forest Fire Weather Index in Ontario
Bibliographic record
Abstract
Abstract In Canada, the Fire Weather Index (FWI) provides forest fire managers with an overall measure of fire danger. Specifically, the FWI is a numerical rating of the potential intensity of a forest fire based on its potential spread rate and the amount of vegetation available for combustion. In our analyses, we consider daily FWI time series, recorded over 42 fire seasons from a sample of fire‐weather stations in Ontario, Canada. Graphical exploratory analyses of the data, including stalagmite plots (a new interactive, three‐dimensional visualization tool), show that the FWI switches between epochs of nil and non‐nil behaviour. This paper develops a framework for assessing sojourn times in these two phases. At some sites, the FWI process appears to begin each year as an approximate Markov process before gradually losing its Markovian character. However, a time‐homogeneous discrete time Markov chain model is insufficient overall, because those sojourn times are not found to be geometrically distributed. Instead, the duration of epochs in each of these phases can be more accurately modelled using beta‐geometric random variables which incorporate seasonality of phase‐specific run length behaviour using local likelihood methods. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 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".