Bibliographic record
Abstract
Marine soils are primarily formed through the transportation of rock and soil particles from adjacent land areas to the sea/ocean by wind, ice, rivers, and rainwater runoff, which accumulate on the seafloor. Waterfront structures are continually being constructed globally, either directly on these soils or in conjunction with reclamation projects to create new commercial land. These soils vary from coarse-grained (gravelly sands, sands, and generally soils exhibiting sand-like behaviour) to fine-grained (clay-like behaviour), and their particle size distribution depends on the distance from the landmass, the mechanism of transportation, and the coastal processes that may affect them. A special and common category of these soils is the mixture of coarse-grained and fine-grained materials that exhibit, depending on the location investigated, either sand-like or clay-like behaviour, which can be challenging to differentiate. In this study, three different nonlinear dynamic analysis techniques are applied to assess the impact of such soils on reclamation and wharf waterfront structures. This paper compares these techniques and discusses the outcomes, while also proposing a method to reasonably simulate marine transitional soils for designing new waterfront and/or marine structures.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".