Bibliographic record
Abstract
Static maximum speed limits are set to inform motorists of appropriate driving speeds under favourable conditions, and are almost always enacted with an overarching goal of increasing safety while retaining reasonable mobility. The first speed limits actually predate the automobile, and as such they have a long and varied history in protecting the traveling public. Today, the speed limit is by far the most popular tool used by engineers and traffic engineering professionals to manage travel speeds. Besides being a popular Canadian road safety tool, speed limits are almost universally employed by all motorized countries. Despite the long and wide-spread use of speed limits as a road safety tool, there are numerous speed limit setting methodologies, and there is no consensus in the traffic engineering community on a single speed limit setting methodology. While there are literally countless guidelines/methods for setting speed limits, these methods can be roughly categorized into four general approaches. The purpose of this paper is to outline the four general approaches to setting speed limits that are available to the transportation engineering community, and to discuss the strengths and weaknesses of each. The four approaches to setting speed limits are the engineering approach(including the traditional use of the 85th percentile speed, and the road risk methodology), the expert system approach (including VLIMITS, USLIMITS), optimization(using speed limits to minimize the total societal costs), and the safe system approach (linking road types and crash types to travel speeds). (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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".