Zero‐energy determination: Confirmation of vessel and pipeline de‐energized state through noninvasive techniques with strain gauges
Bibliographic record
Abstract
The objective of this research is to identify ways to reliably detect residual material and the associated energy through noninvasive methods using a portable, field‐deployable system in order to prevent loss of containment and injury to workers. Leaking valves, defective pressure gauges, and blocked bleeders may cause residual liquid or gas to remain in process equipment, sometimes holding equipment at elevated pressures or allowing a toxic or flammable atmosphere to remain in spite of efforts to clear the equipment. This creates the potential for serious injury to workers when they open, enter, or begin to work on equipment unaware of the hazardous energy still present. The term, “zero energy,” has been used within the context of this research to refer to “a state characterized by the complete absence of hazardous energy.” Hazardous energy is defined as “energy that could cause injury due to the unintended motion, energizing, startup, or release of such stored or residual energy in machinery, equipment, piping, pipelines, or process systems” http://employment.alberta.ca/documents/WHS/WHS‐LEG_ohsc_p15.pdf . This research examines a method to determine if a vessel has achieved zero energy, denoted by internal pressure equal to ambient pressure with no residual liquid present, using strain gauges. © 2014 American Institute of Chemical Engineers Process Saf Prog 33: 195–199, 2014
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".