Bibliographic record
Abstract
Abstract With the oil industry's continued quest for oil and gas in frontier offshore locations, several developments have taken place in regions characterized by seasonal ice cover including the US Beaufort, North Caspian, and Sakhalin Island. In these projects, pipeline transportation systems have been used, which are a cost effective, safe and reliable mode of hydrocarbon transport to shore. Ice gouging is one of the key design issues that affect engineering considerations with respect to strain based design, target burial depth requirements, cost, and safety. It is generally accepted that offshore pipelines in ice environments will need to be trenched and backfilled for protection. Burial depths can be greater than those that might normally be required for pipelines in temperate climates. Burial depth requirements will be a function of the design ice gouge depth (to prevent interaction between the ice and pipe) and an acceptable level of subgouge deformation beneath a gouging ice keel (which potentially strains the pipeline). There still exists uncertainty on the magnitude and extent of subgouge soil deformations due to ice gouging and the importance of sediment transport mechanisms for biasing gouge statistics. Other challenges that must be considered include strudel and hydrodrnamic scour, thaw settlement and frost heave, and upheaval buckling. These considerations may also influence burial depth requirements.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".