Bibliographic record
Abstract
For a number of years now, the Borough of Saint-Laurent has aimed at becoming families' choice location for their home environment. This objective is reflected in its Family Policy, its Local Transportation Plan, and its its Local Sustainable Development Plan for 2011-2015. The actions it has taken over the past few years have proven successful, and consequently, in Statistics Canada's 2011 census, Saint-Laurent was named a leader in Montreal's demographic growth, with a rate of 10.6%, compared to 1.8% for the City as a whole. Saint-Laurent therefore has many young families on its territory, with 14-year-olds and under even representing 18% of its population. In addition, with its central geographic location, Saint-Laurent is subject to considerable traffic (the population and the 100,000 or so workers on its territory daily generate 400,000 trips, connecting with Saint-Laurent, all means of transportation combined—including 80,000 during morning rush hours, coming from the island. Its 43-kilometre territory boasts a major industrial sector. More precisely, the Borough consists of nearly 380 km of roads to maintain, including a number of major arteries of the City of Montreal (autoroutes 13, 15, 40). In addition to regular maintenance of the public roadways in winter, its snow clearance crews carry out an average of six big snow loading operations per year, representing a challenge to the safety of both pedestrians and motorists alike. Furthermore, there are some almost non-stop operations, such as spreading abrasives. Some vehicles—salt spreaders for example—may be used more than 2,000 hours during the season. Added to all this, over the past few winter seasons, there was a lot of ice, increasing the risk of pedestrians falling. Installing lateral protection (side guards) is therefore intended as a very efficient, effective precaution that has the advantage of being economical as well. And lastly, Saint-Laurent's Administration must ensure that the public roadways are shared equally and safely.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".