PR-271-173903-R01 Evaluation of Current ROW Threat Monitoring, Application and Analysis Technology
Bibliographic record
Abstract
This project is a study to provide operators with research on satellite remote sensing systems and how they may address specified pipeline threats. The output is recommendations on satellite monitoring programs that can address those threats and the extent to which they are cost effective. The benefits include a common terminology/understanding of the threats that concern operators and how satellite technologies can help to improve the monitoring, response, and potential mitigation of those threats. This project includes the satellite remote sensing applications for 3rd party damage threats, hazards, and leak detection. The current and near-launch satellite capabilities are mapped against the various potential threats, and operators are provided with a guide on which satellite systems provide the best value against specific threats. The project studies the value of recent 'free and open' missions, the traditional commercial missions, and the emergence of several venture capital funded SmallSat missions that are proving to be disruptive to the benefit of the industry. Modeling and simulation of scenarios on a selected pipeline system highlight gaps in satellite technology capabilities (sensor or monitoring coverage) to provide operators with a clear understanding of the limitations of the current missions and where other technologies may provide a better solution. A review of current suppliers is also presented. In addition to the RFP Scope of Work, the primary input into developing the satellite approaches is a series of interviews with operators, and research from previous studies in this area.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.027 |
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".