Bibliographic record
Abstract
The history of automated people movers is a fascinating story of innovation by governments, companies, entrepreneurs, transportation interest groups, researchers, and individuals. Some believe that the initial work began when the auto manufacturers were conducting in-house research on automated highways and other companies were developing systems using driverless vehicles on separate guideways. However the impetus for the development of these systems in the United States was provided by amendments to the Urban Mass Transportation Act of 1964. The amendments required that a project be undertaken to study and prepare a program of research, development, and demonstration of new systems of transportation. Extensive research studies were undertaken in the late 1960s and 1970s. Several manufacturers developed prototypes and early applications included installations at Tampa and Dallas-Fort Worth International Airports and in Morgantown, West Virginia. The Downtown People Mover studies generated considerable interest in the late 1970s. Research and development work was also underway in Canada, Europe, and Japan. Today there are over 130 installations of various types and configurations throughout the world and many more are under construction or are being considered. This paper travels somewhere in time to review some of the events in the development of this new transit technology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".