Bibliographic record
Abstract
This article discusses how there are no gadgets in the world that regulate daily behavior as much as traffic signals, except perhaps mobile phones. It has been estimated that the daily commuter goes through at least 10 signals on his journey to work. However, unlike mobile phones, traffic signals cannot be ignored or switched off by their daily users, at least not without legal consequences. There are about 311,000 traffic signals in the United States, and probably three million signals around the world. If these signals can be influenced to allow for a little bit easier travel every day, there would be significant delay and fuel savings benefits with considerable improvement to the environment. The use of a digital computer to control traffic signals was initiated in Toronto in 1963, but this computer system, which required a five-ton air conditioning unit to keep it cool and operational, was merely a gigantic clock which initially used pre-determined signal plans to regulate traffic flows in different periods of the day. There were signal plans for every occasion – rush hour plans, holiday plans, a baseball plan, and even a snow plan. Traffic responsive control was invented and now the computer could intelligently select the right signal plan from a table based on traffic conditions indicated by detectors. For many years, traffic responsive control has been an integral part of the majority of traffic systems offered in the market. However, this control technique has seldom been used because it does not eliminate the basic requirement for signal improvements, which is signal plan update. Adaptive control became commercially available in the late 70s and they were offered as a total solution, which included the central computer, controllers and the interface units, as well as the fixed time portion of the signal system. These adaptive control systems were expensive and not within easy reach of the average municipality. It was not until recently that lower cost systems were offered that allow the user to implement adaptive control on an incremental basis. However, some experts would argue that these are not truly adaptive control systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".