Sistema de control de proyecto de la construcción de la Estación Jipijapa y segmento de túnel del Metro de Quito mediante el análisis del valor ganado
Bibliographic record
Abstract
The correct and adequate management of a project as vast as the Metro de Quito is of extreme importance, due to the huge amounts of money and tight schedules that are worked with, and the necessity of counting on a public transportation system delivered on time by the city of Quito. This works prime objectives were to develop Dashboard, for the control of the project’s progress, based on the Earned Value Analysis and its graphs and indicators so that cost estimations when finishing the project could be made. This was achieved by building a database, based on monthly reports, and by taking the summary of all the information that was easy to analyze and understand through and Excel program that shows graphs and indicators based on filtered information. A dashboard was designed, based on the project’s progress for the construction of the subway station Jipijapa and the tunnel segment between stations El Labrador and Jipijapa, and sub-dashboards. The programming was done in MS Excel with through Macros. The control of the physical, economic and scheduled progress was made based on the initial chronogram, and the progress of the information provided in the monthly inspection reports delivered by Metro Alianza regarding the progress of each activity for each work package. The system allows to have the relevant information of cost, time and scope for monitoring the project, and to have relevant information for the proper management of the project.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".