Calibration of a Freeze-Thaw Prediction Model for Spring Load Restriction Timing in Northern New England
Bibliographic record
Abstract
A major problem with low-volume roads located in seasonal frost areas is their susceptibility to damage from trafficking during spring thaw. Therefore, seasonal load restriction (SLR) policies that limit the axle loads of heavy trucks during the spring thaw period have been implemented in many countries in an effort to minimize costly roadway damage. Several agencies have been addressing the question of when to place and remove SLRs and have expressed the need for a prediction model to aid them in the process of posting roads. Models are available which predict the depth of frost and thaw penetration based upon air freezing and thawing indices, requiring only air temperature data for input. Various forms of these models have been used by transportation agencies in the United States and Canada. When using any prediction model, a key element is model validation and calibration for local conditions. The purpose of the research described herein was to calibrate a freeze-thaw index model for use in SLR timing in northern New England. Atmospheric weather data and measured subsurface temperature data obtained from nine field test sites in New Hampshire over a period of three years were used in this analysis. Frost and thaw coefficients for the model were calibrated on a site-specific basis. Results suggest that frost-thaw patterns were reasonably estimated at most of the nine test sites using this model, although the model tended to be too conservative in estimating end-of-thaw dates, with estimated end-of-thaw dates falling after measured dates in many instances.
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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".