Development of Tools for Improved Spring Load Restriction Policies in Ontario
Bibliographic record
Abstract
Application of seasonal load restrictions to certain parts of the highway network can lead to lost productivity and a substantial impact on the economy. Once these restrictions are in place, the payload of certain heavy vehicles must be reduced. One of the largest challenges particularly in Northern Ontario is to design and monitor roads concisely to mitigate damage caused by seasonal effects. This is a complex problem as many of the roads are gravel or surface treated and there is limited funding available for the construction and maintenance of these facilities. Thus, it is vital that these roads are protected, particularly during the vulnerable spring thaw period. In order to properly protect these facilities, it is necessary to monitor them in a coordinated manner which utilizes both temperature and pavement data. The advancement in data availability has been greatly enhanced with the implementation of road weather information system (RWIS). This paper describes an on-going study in Ontario which has involved the installation of two thermistor probes in Thunder Bay and New Liskeard in the Winter of 2005. At each location, a thermistor assembly was lowered into the excavated area and
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".