Bibliographic record
Abstract
As Apple, Google, and other high-tech companies team with automakers to make cars smart enough to drive themselves, do roads need to be as dumb as the concrete and asphalt they're made of? Europe doesn't think so. The European Union has invested more than 200 million [euro], or $227 million, over the past few years in a number of research programs aimed at creating intelligent infrastructure that will communicate with smart cars. The goal is to eliminate congestion in growing urban areas, help the environment, and above all, save lives. The costs of auto travel in Europe are large, multifarious, and growing. European Union drivers currently own one third of the world's one billion cars, and congestion costs the region about 1 percent of gross domestic product (GDP) every year, a number that is rising. Transport is also responsible for about a quarter of EU greenhouse gas emissions, which Brussels aims to reduce by as much as 80 percent of 1990 levels by 2050. And nearly 27,000 people died on European roads last year. On average, up to 50 million people are injured in car accidents in Europe each year, with about 600,000 hospitalized at a cost of about 160 billion [euro], according to figures compiled by the World Health Organization and the European Transport Safety Council. Against this background, the European Union has made intelligent transport a priority in its research and innovation programs. Smart roads, capable of warning drivers of hazardous road conditions and approaching cars well before such hazards enter their field of vision, are a leading component of the effort. Powering this smart infrastructure is the latest advances in sensors, wireless communications, and computers, all tied together by the Internet. Smart roads are one part of a system designed to provide vehicles with 360-degree awareness of their surroundings via a set of in-car sensors, transmitters, and processors that allow cars to communicate with each other and gather real-time data from road infrastructure, including signs and traffic signals as well as the road itself. Within this system, roads would incorporate sensors and other technology to provide vehicles and drivers information about hazardous conditions and other critical events, well before they're within eyeshot. The technology, known as vehicle-to-everything, or V2X, could reduce accidents by as much as 80 percent, European researchers claim. Estimates for developing and implementing this advanced road transport infrastructure across Europe range between 80 billion [euro] and 140 billion [euro]. Hermann Mezer, the chief executive of the European Road Transport Telematics Implementation Coordination (ERTICO), a public-private organization involved in the production of intelligent transport systems, called the systems a game changer. Not surprisingly, a number of technology players want to carve out a piece of that business, among them Germany's Siemens, NXP in the Netherlands, and a number of US companies, including Cisco and IBM. Siemens, a key provider of traffic management systems in Europe, has been quick to expand into intelligent infrastructure, especially environmental detection systems and road works warning systems. The Munich-based engineering giant is working closely with NXP, a specialized chipmaker headquartered in Einhoven that is among the first to develop and deliver V2X chipsets for high-volume manufacturing. The Dutch company is also collaborating with Singapore's Nanyang Technological University to build a smart mobility test bed in the southeast Asian city-state. The project involves 100 vehicles and 50 roadside units that will test V2X technologies over the next four years. …
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.131 | 0.108 |
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".