Bibliographic record
Abstract
Abstract The Pipeline Ice Risk Assessment & Mitigation JIP (PIRAM) developed aset of engineering models and design procedures for implementation intoindustry best practices for risk mitigation and protection of pipelineinfrastructure from ice keel loading. The models established the pipelinemechanical behaviour in response to ice keel load events, and assessedengineering concepts for protection and risk mitigation strategies. Improvedmethodologies for contact frequency and ice keel loads determination formedpart of the integrated model. Pipeline protection against ice gouging is overviewed. A review of subgougeresponse and physical model tests provided a basis for refinement of threedimensional continuum finite element analyses of steady state gouging includingthe implementation of an effective stress based soil plasticity constitutiveroutine. A fully coupled ice, seabed and pipeline interaction model is used tocalibrate a simpler pipeline design approach for design purposes. Thestructural model, improved by considering 3D interaction effects, comparesstrains within the pipeline to those from continuum analyses and from physicalmodel tests. The PIRAM pipeline model provides the engine for the probabilisticassessment of pipeline cover depth using a GIS-based decision-support-systemfor route planning.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".