Bibliographic record
Abstract
According to the 2015 AER database there are approximately 473,000 km of high pressure pipelines in Alberta alone, ∼93,000 km of which have been discontinued. Of these 93,000 km of discontinued lines approximately half have been abandoned. When a pipeline is abandoned it is typically flushed out, marked and has all corrosion protection and monitoring systems removed. In the absence of corrosion protection all pipelines will inevitably corrode. This can create long term issues for abandoned pipelines. Two of the critical issues are potential ground subsidence and the creation of unintended water conduits. Ground subsidence is caused when an abandoned pipeline corrodes to the point of allowing the surrounding soil to fall into the empty pipe. Ground stability issues can result depending on the size and depth of the pipeline. Stability issues can appear suddenly in the form of sinkholes, slow slumping troughs or cracks in the earth and anything in between. Regarding unintended water conduits the main concern centres on a pipeline under or near water crossings or in areas of saturated soil. The pipeline corrosion can eventually provide a mechanism for water (or other constituents) to enter and migrate through the empty pipe and then be discharged further down the line. This uncontrolled migration can potentially have environmental impacts depending on what gets transported and where it ends up being discharged. One common industry method to protect abandoned lines from the issues described above is to backfill them with foam or grout. Backfilling areas of abandoned lines can help protect against ground instability and subsidence as the line corrodes away. Creating plugs or cutting/capping abandoned pipelines can protect against the possibility of water conduits. The use of paste technology is not common to the industry in this regard however it can provide many benefits over current foams and grouts. This paper will discuss paste technology and it use as a backfill option for pipeline abandonment.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".