Rationalization of Low Volume Roads in New Brunswick
Bibliographic record
Abstract
The New Brunswick Department of Transportation and Infrastructure (NBDTI) has over 1,700 km of designated unsurfaced roads totalling more than 3,100 km in length. The majority of these roads are low volume roads in rural environments and serve various functions including access to residences, resources, and seasonal recreation. Until recently, the Department did not have a formal system to document and manage the function of these roads or their physical condition. This lack of information represented a gap in the Department's asset management system. As a first step in quantifying the function and condition of its unsurfaced roads, the Department retained Exp Services to develop a procedure for classifying and rating the unsurfaced road network and to determine what level of upgrades are required to meet a desired standard. This paper describes the development of the classification and rating system and presents a number of interesting results regarding the inventory of the Province's unsurfaced road network. It also presents a number of challenges and limitations encountered. It is the intent that the information and experiences presented in this paper will be of interest to other jurisdictions attempting to better manage their inventory of low volume roads. (A) For the covering abstract of this conference see ITRD record number 201211RT334E.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".