Development of a Pipeline Surface Loading Screening Process
Bibliographic record
Abstract
Pipeline Operators receive numerous requests annually to cross their pipelines. In many of these cases detailed analysis using a number of different methods are performed since no simplified approach is available. The Canadian Energy Pipeline Association (CEPA) with Kiefner and Associates, Inc. undertook the development of a screening methodology for vehicle loading. The hope is a standard approach to these analyses might be established to assist pipeline operating companies. This paper describes an approach detailing the development and implementation of a simplified screening process to assess the effects of surface loads on buried pipelines. A design basis was established based on a literature review to identify theoretical models, standards, codes, and recommended practices that are currently used to assess the surface loading effects on buried pipelines. This design basis was incorporated into a methodology utilized to develop a screening tool which provides a simple “pass/no pass” determination and is based on attributes which are generally easy to obtain (e.g., wheel or axle load, ground surface loading pressure, depth of cover, maximum allowable operating pressure and design factor). Situations which pass the initial screening would require no additional analysis while situations that do not pass the initial screening may need to be evaluated on a more detailed basis. Simplified graphs have been developed to assist in additional screening prior to performing a more detailed evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".