Demarcation of Right of Way (ROW) and Re-Installation of Damaged Markers of Transmission Pipeline in Balochistan, Pakistan through Geomatics Technologies: A Case Study of Zarghun to Quetta High Pressure Natural Gas Supply
Bibliographic record
Abstract
The alignment of gas pipelines in a rugged terrain has always been a serious challenge around the globe but they can be perfectly aligned with the help of Geo-Spatial technologies. When South Zarghun gas fields were discovered in Pakistan, gas transmission pipeline laying process between gas fields and Quetta city was as challenging task for the Government of Pakistan. Very rough terrain and extreme weather conditions especially in winter season were the two major constraints for the alignment of pipeline. Geomatics survey was essential before transmission pipeline construction work, done twice by private consultant in Pakistan’s local geographic coordinate system. SSGC took decision in 2006 for the construction of pipeline but unfortunately it could not be executed due to law & order situation in Pakistan especially in Balochistan province and later all bench marks of pipeline alignment route were vanished by the locals. In 2012, SSGC again started pipeline construction work on war steps. Previous pipeline alignment survey was converted into existing SSGC’s GIS coordinate system i.e. UTM Zone 42 WGS84 using ESRI ArcGIS software. Identification & re-installation of all bench marks and re-routing of pipeline were done by SSGC GIS survey team with the help of global positioning system (GPS) and satellite data. Finally, right of way (ROW) of transmission pipeline from Zarghun gas fields to Quetta city was designed and produced in the form of GIS map for pipeline construction activities.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".