PR686-203903-R02 Ongoing InSAR Geohazard Monitoring of Pipeline Right-of Ways in the Appalachian Mountains
Bibliographic record
Abstract
Ground displacement along pipeline corridors has the potential to compromise pipeline integrity. The Appalachian Mountain region is almost entirely classed as high landslide susceptibility by the United States Geological Survey (USGS) and crossed by numerous transmission pipelines and gathering lines. In phase I of this research project, 3vGeomatics successfully demonstrated a proof of concept on the effectiveness, reliability, and precision of using L-band ( ) SAR satellites for InSAR displacement monitoring of vegetated areas. The second phase of this project improved operational monitoring utility of long wavelength InSAR by leveraging a two and a half year dataset of long-wavelength satellite radar data with improved product formats to facilitate management of pipeline threats posed by both geohazards and third party encroachment. Potential encroachment threats are highlighted by new object detection capabilities that use the same raw SAR data to produce additional intelligence. As with the previous year's results, these phase-2 InSAR and new object detection results were compared with measurements from other sensors including differential light detection and ranging (LiDAR), visual field inspections, and aerial photographs. This project demonstrated the technological readiness and streamlined product formats delivering actionable intelligence from L-band SAR data for operational monitoring of ground displacement and other hazards over entire pipeline networks and associated infrastructure in vegetated areas. There is a related webinar.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.272 | 0.275 |
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".