Bibliographic record
Abstract
This paper describes how the City of Calgary, Canada Transportation and Land Use Planning, Development/Building Approvals Departments developed a policy and protocols for the establishment and technical review of dangerous goods routes. This was required for three reasons, often referred to as ‘triple bottom line’. First, there was the need for a consistent, comprehensive, and technically robust procedure to be developed to ensure viability and integration between the land use planning and transportation network processes (economic). Second, there was the need to ensure an environmentally robust procedure was established to ensure complicity with federal and provincial environmental requirements (environmental). Third, were the requests from the public and politicians to eliminate roadway designations for the transportation of dangerous goods (social). In all cases there is the underlying recognition that many stakeholders do not understand the purpose of a dangerous goods route and that substantive technical evaluation criteria and procedures been established. Additionally, due to Provincial legislation (Alberta) requiring a review of the dangerous goods network at least every five years, evaluation criteria were established that allow the land use and transportation planners to determine what roadways should be designated as dangerous goods routes at the outline plan stage, and allowance for the roadway operating authority to repeat the process to meet the requirements of the enabling legislation.
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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 | 0.141 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.082 | 0.019 |
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".