A Satellite-Based Mechanical Damage Management Solution
Bibliographic record
Abstract
Numerous industry studies have characterized mechanical damage to be the pipeline industry’s largest single hazard. A proactive approach to preventing incidents due to mechanical damage is desirable. A process combining high-resolution satellite imagery with geomatic technologies such as GIS and image analyses is in the process of being demonstrated to be able to detect, georeference and characterize potentially injurious encroaching activities that may cause mechanical damage. The intrinsic advantages of a satellite imagery-enabled process include the high revisit frequencies (in comparison to typically used aerial patrol frequencies), the wider swath width of monitoring and the analysis -friendly digital nature of the imagery. The successful implementation of such a process will contribute to averting incidents in the many cases where One-call (Call before you dig) systems are not notified. In addition, as a by-product of the process, this service could assist in continuously surveying the right-of-way. Working with leading North American pipeline operators, via+ is developing and bringing to market commercial delivery models of this process. The elements of the process and the technologies current and anticipated capabilities are presented. Sample results of the process implementation are also presented.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".