PR-420-183903-R01 Pipeline Right-of-Way River Crossing Monitoring With Satellites
Bibliographic record
Abstract
The goal of the work described herein is to provide PRCI and the pipeline industry further understanding of the current capabilities and limitations of combined SAR and high resolution optical satellite imagery for the monitoring of pipeline ROWs which span river crossings. Four Areas of Interests (AOIs) with pipeline ROWs that span river crossings were selected for analysis: South Saskatchewan River, Saskatchewan, Canada operated by SaskEnergy Incorporated; Thompson Creek, Louisiana, USA operated by Colonial Pipeline Company; Gila River, Arizona, USA operated by Kinder Morgan Incorporated; and Humber Estuary, UK, operated by National Grid. For each AOI, monitoring requirements were defined by the operators. Amplitude Change Detection (ACD) and Interferometric Synthetic Aperture Radar (InSAR) were performed for all AOIs; results correlated to the defined monitoring requirements are discussed. A high level summary of the role of combined SAR and optical satellite operational monitoring of pipeline river crossings is listed below: - InSAR "Phase" used for (a) Subsidence (b) Slope Movement - SAR "Amplitude" used to both detect and classify (a) large scale Land Cover/Land Use Change (e.g. bridge construction), (b) flooding, (c) river channel changes, (d) river bed exposure, and (e) vessel traffic. - SAR "Amplitude" used to detect changes resulting from (a) small scale Land Cover/ Land Use (e.g. construction of individual buildings), and possibly (b) bank erosion and (c) pipeline exposure. Optical Satellite imagery is required for classification of these changes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.163 | 0.145 |
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".