Web-Based 3-D GIS and Its Applications for Pipeline Planning and Construction
Bibliographic record
Abstract
A pipeline project normally not only covers a large geographic range, but also deals with a variety of data sources, such as geological, geographical, environmental, engineering and socioeconomic data. GIS has proven to be the effective approach to integrating, managing and analyzing these heterogeneous data sources. Due to the nature of pipeline applications, the third dimension of geospatial data is of considerable importance for pipeline planning, construction and maintenance. There is an increasing demand for the development of a 3-D GIS for pipeline applications. With the advent of Internet, distributed computing and computer graphics technologies, development of web-based 3-D GIS becomes technologically possible. The combination of 3-D GIS and web-based computing technologies opens a whole new avenue to the pipeline industry. In this paper, we will address the development of a web-based 3D GIS in terms of benefits and technical challenges. The detailed system architecture as well as the algorithms developed is also discussed. Finally, potential applications for the pipeline industry are introduced and a prototype system, GeoEye 3D, developed by the Department of Geomatics Engineering at the University of Calgary is described.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".