LOS DESAFÍOS CONFRONTADOS POR LOS PROYECTOS CONVENCIONALES DE TRANSPORTE Y EL POTENCIAL DE LOS SISTEMAS INTELIGENTES DE TRANSPORTE PARA UNA CIUDAD EN DESARROLLO, LIMA, PERÚ
Bibliographic record
Abstract
LOS DESAFÍOS CONFRONTADOS POR LOS PROYECTOS CONVENCIONALES DE TRANSPORTE Y EL POTENCIAL DE LOS SISTEMAS INTELIGENTES DE TRANSPORTE PARA UNA CIUDAD EN DESARROLLO, LIMA, PERÚ CHALLENGES TO CONVENTIONAL TRANSPORTATION PROJECTS AND THE POTENTIAL FOR INTELLIGENT TRANSPORTATION SYSTEMS IN A DEVELOPING CITY, LIMA, PERU Manuel J. Martíneza DOI: https://doi.org/10.33017/RevECIPeru2007.0004/ RESUMEN Este artículo examina los desafíos que encuentran los proyectos convencionales de transporte urbano cuando se implementan durante el proceso de motorización de Lima. Como solución potencial, se recomienda que se priorice la investigación y desarrollo de los Sistemas Inteligentes de Transporte para Lima. Palabras clave: Sistemas Inteligentes de Transporte, Telecomunicaciones, Transporte Urbano, Política de Transporte Urbano. ABSTRACT This paper presents the challenges to the implementation of conventional transportation projects in developing cities with motorization process and recommends research in Intelligent Transportation Systems. Keywords: Intelligent Transportation Systems, Telecommunications, Urban Transportation, Urban Transportation Policy.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".