Introduction to the new Standards - IEEE STD 844.1™-2017 /CSA C22.2 No. 293.1-17 and IEEE STD 844.2™-2017/CSA C293.2-17 for Skin Effect Trace Heating of Pipelines, Vessels, Equipment, and Structures: Copyright Material IEEE, Paper No. PCIC-2018-20
Bibliographic record
Abstract
Through a joint standards development process between the Institute of Electrical and Electronic Engineers (IEEE) and the Canadian Standards Association (CSA), two new standards are now completed. These new standards include a certification standard, IEEE Std 844.1 ™-2017/CSA C22.2 No. 293.1-17 (Standard for Skin Effect Trace Heating of Pipelines, Vessels, Equipment, and Structures-General, Testing, Marking, and Documentation), and an application guide standard, IEEE Std 844.2™-2017/CSA C22.2 No. C293.2-17 (Standard for Skin Effect Trace Heating of Pipelines, Vessels, Equipment, and Structures-Application Guide for Design, Installation, Testing, Commissioning, and Maintenance). Requirements are detailed that have been added or clarified in the skin effect trace heating system certification process. This paper summarizes new applications as well as new recommended installation practices in the application guide. Reflections on the path taken in this joint standard development as well as a look forward at future developments are shared.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.085 |
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".