Understanding the Disintegration of Sensitive Clays using Remolding Energy
Bibliographic record
Abstract
Sensitive clay materials are found in several areas of the world including Alaska, Canada, Norway and Sweden. Studies by various researchers suggest that a knowledge about the complete stress-strain curves help in the assessment of the flow slide potential of sensitive clays. The post-peak stress-stain behaviour of sensitive clays particularly indicates the disintegration process in the material as well as helps in the estimation of the energy involved in the disintegration process. The energy concept is a subject of current study by several researchers working on investigation of flow slides on sensitive clays. Several terms such as degradation energy, strain energy or remolding energy have been used to indicate the energy available for disintegration of sensitive clays; this is referred to as remolding energy (RE) in this work and is simply defined as the strain energy involved in the disintegration or remolding of a material. A closer examination of the concept of RE provides an understanding of the overall mechanical behavior of sensitive clays during flow slides. In this paper, concept of RE analytically proposed by the authors is elaborated in light of laboratory tests conducted to determine RE of sensitive clays.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".