Using Classification Trees to Build Flexible and Intuitive Winter Weather Indices
Bibliographic record
Abstract
In northern latitudes, road salt remains a key element of winter maintenance operations. At the same time, there is increased pressure to reduce salt usage without compromising level of service or safety. Road salt usage models provide a way of benchmarking and understanding spatial-temporal variations in maintenance operations, and therefore have value in working toward improved salt management practices. The current study outlines a new approach for modeling road salt usage that addresses many of the limitations of past models. This approach is developed and illustrated using automatic vehicle locator data for three seasons and one provincial highway patrol near Ottawa, Canada. Using categorical, hourly salt application rates for specific highway segments as the dependent variable, and various sources and types of forecast and observed weather conditions as the independent variables, five different treatment modes are modeled using classification trees. Results are promising in terms of both the accuracy of predictions and the ability of this inductive approach to identify key explanatory variables and related threshold values that affect the probability of different treatment options. The winter index that results from this approach can incorporate both the most likely treatment mode as well as its probability, and can be scaled such that the same data inputs and outputs can be used to characterize winter weather at various temporal scales, from individual storms to entire winter seasons.
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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 | 0.004 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".