Natural Hazard and Risk Management for South American Pipelines
Bibliographic record
Abstract
Hazard identification and rating involve the first two of a four-phase natural hazard and risk management (NHRM) system that is being developed to manage natural hazards along linear facilities. In Canada, completing these first two phases is generally straightforward. Baseline data including air photos, geology and topographic maps are readily available; the number and types of hazard exposure are often limited for any given facility; and, the standard of care expected during design and construction is understood and practiced. The NHRM methodology is also being applied on South American pipelines. Greater flexibility is required in obtaining necessary input data. Helicopter and vehicle access are often more limited, and greater reliance must be placed on airphoto interpretation and literature review. Processes of rating hazard exposure are needed for less familiar hazard types, including tsunami, volcanic eruption, and tectonic ground rupture. South American construction and design practices must be accounted for in the rating methodology. Using examples from recently constructed trans Andean pipelines, this paper outlines application of the NHRM system to linear facilities located in areas of diverse hazard exposure and less stringent design and construction practices. Under the broad headings of ‘geotechnical’ and ‘hydrotechnical’ hazards, a methodology for rating eleven different hazard types is outlined. On the geotechnical side, these include tsunami, volcanic eruption, tectonic ground rupture, landslides and debris flows originating off-rights-of-way, and mass movements originating on rights-of-way. Hydrotechnical hazards include scour, degradation, bank erosion, encroachment, and channel abandonment/avulsion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".