Responding to Climate Change with Rational Approaches for Managing Seasonal Weight Programs in Manitoba
Bibliographic record
Abstract
Recent changes in Canada's regional climate have made historic weather and strength recovery trends less reliable predictors of when to start and end spring load restrictions (SLR) and winter weight premiums (WWP). The Manitoba Department of Infrastructure and Transportation (MIT) and FPInnovations have recently developed rational methods for starting and ending these seasonal weight programs in Manitoba. The SLR method links weather-based indices to when test pavements started to weaken, and to when their strength was substantially recovered in the spring. The analysis resulted in the implementation of a new SLR policy with starting and ending thresholds based on cumulative thawing index (CTI). The WWP method links weather-based indices to when the strength of freezing test pavements stabilized, and to when these frozen pavements began to warm and weaken in late winter. Starting and ending thresholds based on cumulative freezing index (CFI) and CTI were recommended as interim thresholds for WWP policy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".