Benefits of Global Standards on the Use of Optical Fiber Sensing Systems for the Impact of Construction of New Utilities and Tunnels on Existing Utilities
Bibliographic record
Abstract
Distributed Optical Fiber Sensing is a mature technology given its strong record of over 20 years. Nevertheless, underground utilities are yet to embrace it as an everyday tool despite its enormous capability. One dimensional long buried utilities and tunnels offer the best application for the use of this technology. Research studies around the world offer the promise of this technology in monitoring the impact of ground movements on underground utilities and tunnels. No application standards existed that governed the use of this technology within any jurisdiction in the world in September 2012. A global task group on optical fiber sensing systems (OFSS) was born to become a unique pool of talent and experience on the subject with over 40 leading experts from 17 countries, which went on to author two companion standards American Society for Testing and Materials (ASTM) F3079-14 and F3092-14, within ASTM Technical Committee F36. This paper provides a brief overview of how OFSS work, what is in these standards, why OFSS is poised to become the most versatile innovation among all measurement tools for field monitoring, what problems the task group faced during the development of the standards and how the members of the task group resolved these problems, what the benefits are of such global standards and the future plans for the global OFSS task group. The most paramount goal of the authors is to share the lessons they learned during the development of the standards with the delegates of this conference.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".