Performing a Comprehensive Single Pass Multiple Pipeline Survey
Bibliographic record
Abstract
This paper presents a discussion of the methodologies and technologies implemented to complete a comprehensive and efficient close interval (CI), depth of cover (DOC) and current attenuation surveys over a new NPS 36 fusion bond epoxy (FBE) coated pipeline influenced by telluric and high frequency AC interference. The DOC and current attenuation survey interval was set to be the same as the CI survey interval (5′) to facilitate detailed profile of the pipeline, and to identify any area where marginal cover or geometric anomalies may exist. By completing both surveys in one pass, there was assurance that the CI readings were taken over the target pipeline. The DOC survey was completed with a continuous data stream from an electromagnetic pipe locator utilizing omni-directional antenna coils. All survey readings were recorded with highly accurate real time GPS to allow time synchronization and geographic information system (GIS) implementation of the survey data. GPS synchronization of the stationary, mobile data loggers and rectifiers were verified with multiple daily waveform analysis. Rectifiers were fitted with SCADA communications and GPS RMUs for remote monitoring and operation. Telluric and high frequency interferences were compensated for through waveform logging at both stationary data log and the remote surveyor points. Optimal readings were determined through advanced statistical analysis of the survey data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".