Soil Characterisation of an Artificial Island Accounting for Heterogeneity
Bibliographic record
Abstract
The heterogeneous nature of soils and other geo-materials results in uncertainty in design and so it is important to incorporate this heterogeneity in analyses of geo-structure performance. However, before analysing the geo-structure itself, it is first necessary to statistically characterise the site in terms of appropriate soil properties. This paper focuses on the description of this first stage in the analysis by presenting a case study. Numerous artificial sand islands were designed and constructed in the Canadian Beaufort Sea, for use as hydrocarbon exploration platforms, in the 1970's and 1980's. For some of these islands, extensive Cone Penetration Test (CPT) data are available for characterising the hydraulically placed sands and for investigating the general factors affecting the in situ density. This paper investigates data from one of these islands. An existing methodology to statistically evaluate CPT data in terms of state parameter is described, the aim being to characterise the deposited sands in terms of state parameter statistics (mean, standard deviation, probability density function and scale of fluctuation). From the results obtained, a discussion on the factors influencing the quality of the fill in terms of in-situ density is presented.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".