Multi-Pipeline Geographical Information System Based on High Accuracy Inertial Surveys
Bibliographic record
Abstract
This paper describes the GIS system developed for the Pemex’ pipeline network in the Valley of Mexico. The pipeline UTM coordinates, which are the basis of the GIS, were obtained from the high accuracy Geopig® inertial and caliper surveys. The survey data also included information on pipeline features and anomalies, and was incorporated into the GIS together with the metal loss data from the past in-line inspections. The system is based on the ArcView® GIS Software with the Arc View 3D Analyst™ extension that allows both the cartography and pipeline data to be viewed in 3-D space. It stores information on pipeline plan, profile, girth weld locations, dents, wall thickness, bending strains, metal loss and other features in relation to known landmarks such as roads, buildings, political boundaries and hydrology. This allows for very efficient and accurate location of pipe defects and anomalies, which is particularly beneficial where there are several pipelines running in the same right-of-way. It helps to eliminate unnecessary excavations, as well as to coordinate, plan and schedule pipeline repairs. The additional benefit of a multi-pipeline GIS system is the ability to store various information for all the pipelines in one database, which is easy to manage and update. The GIS also gives the ability to plot detailed maps, query data for effective solutions and visualize scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".