STRESS CORROSION CRACKING: A CANADIAN PROSPECTIVE FOR OIL AND GAS PIPELINE
Bibliographic record
Abstract
Stress corrosion cracking (SCC) is a form of environmentally assisted cracking (EAC) that is of great signifi cance to Canadian oil and gas pipelines. In these pipelines when ground water penetrates under the pipe coating, longitudinal cracks develop and grow at a maximum through-wall rate of about 0.6 mm/year. Over the last decades, thousands of colonies of these cracks have been found all across pipelines in Canada (Figure 1). These cracks frequently go dormant at depths of about 1 mm. Occasionally, for reasons as yet not understood, the cracks continue to propagate and this can lead to pipe rupture. This phenomenon led to several serious ruptures within the Canadian pipeline system between 1985 and 1995 and the phenomenon was the subject of two National Energy Board (NEB) inquiries in the 1990’s [1]. Pipeline steels are often susceptible to SCC in two basic forms of cracking, namely Intergranular and Transgranular. Intergranular cracks initiate and propagate at grain boundaries and usually form at high-pH (8.5-10.5). They are initiated at the outer surface of pipe and the cracking results from the generation of a carbonate-bicarbonate solution under disbonded coatings. On the other hand, transgranular cracks cut through the grain and is usually manifested at near-neutral-pH SCC (5.5-8.5). Transgranular cracks in high-pressure gas pipelines has been found to be
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".