Bibliographic record
Abstract
This paper presents an automated system that integrates Global Positioning System (GPS) and Geographical Information System (GIS) in a web-based platform named Truck+. It is utilized for estimating, monitoring and forecasting productivity of hauling trucks in earthmoving operations. The developed system consists of GPS for automated site data acquisition, GIS web-based system used as graphical user interface, relational database and Discrete Event Simulation (DES) for stochastic forecasting. The paper also highlights the benefit of utilizing the actual captured data supported by DES for stochastically forecasting productivity of earthmoving operation. DES was applied to forecast productivity in a stochastic approach that makes use of the project’s captured data during the operations involved rather than data from past projects. This makes it more capable of capturing relevant factors that impact project conditions. Truck+ consists primarily of two modules: tracking/monitoring module and forecasting module. The developed system has been implemented in prototype software using object-oriented programming, ArcGIS APIs (Application Programming Interface) and deploys Microsoft Silverlight for creating and delivering rich internet web application and media. Truck+ is capable of generating graphical and tabular reports with various degrees of detail to suit the requirements of project teams. The developed system is applied to a construction project in Montreal area to demonstrate its use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.021 |
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".