Bibliographic record
Abstract
Abstract Hydrocarbon basins in global arctic regions hold significant resources with economic potential to support an array of economic sectors including energy, transportation, petrochemical, agricultural, and manufacturing industries. Due to the harsh physical environment, northern and arctic regions present significant technical and logistical challenges that influence the development of engineering solutions through the design process and may affect the project viability and sanction with respect to technical or economic factors. One of the more significant hazards and extreme loading events encountered is ice gouging due to the interaction of ice features with the seabed subject to environmental driving forces. Trenching and pipeline burial is viewed as one of the most effective mitigation techniques used to promote pipeline serviceability and reduce the risk of pipeline damage; however, there are limitations and constraints with current technologies with respect to the maximum trenching depth and production rates that affect project logistics and economic risk. In addition, there are other physical environmental factors that present challenges including the short open water season, low temperatures, and presence of special terrain characteristics (e.g., hardpan, permafrost, massive ground ice). Current practice used to define system demand (i.e., geotechnical loads) and system capacity (i.e., pipeline mechanical performance) has limitations due to inherent uncertainties with the statistics of physical data sets, experimental techniques, and engineering models used in the analysis. Advancements in computational methods have provided improved engineering tools to analyze these complex nonlinear processes with probabilistic methods providing an objective framework to assess design options with respect to technical, economic, and environmental criteria that meet specified target safety levels. Consideration of cumulative effects, climate change, and sustainability factors add an additional layer of complexity to the engineering framework in terms of social context and political values.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".