Bibliographic record
Abstract
We are facing a climate emergency, with significant changes in weather patterns and more extreme weather events, both storms and drought. These changes can have significant deleterious effects on our geotechnical infrastructure. While intense storms are important as potential triggering events for landslides, there needs to be an awareness that seasonal wetting-drying cycles can cause deterioration of soils, leading to failures; the magnitude of these cycles is likely to increase with climate change. In the UK there has been an increased frequency of landslides occurring on slopes forming the national railway network, often leading to temporary closures of railway lines and significant disruption to rail passengers. Laboratory and field testing to investigate these effects shows that there is a shift in soil water retention curves with drying/wetting cycles, resulting in a progressive loss in suction at the same water content point within each cycle. Triaxial tests on unsaturated specimens demonstrate significant losses in strength, at the same water content, as the material is subject to drying/wetting cycles, due to this loss of suction. The result is a progressive deterioration in strength with seasonal cycles. This lecture will propose a novel solution of water-holding barriers that can isolate the soil from environmental impacts.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".