Introduction: Advances in landslide understanding
Bibliographic record
Abstract
Landslide research covers an extremely wide range of aspects: from triggering mechanisms to response of the unstable mass after failure, including transport, deposition, and interaction with protective structures. Advances in landslide research rely on accurate field data; comprehensive monitoring of laboratory experiments, especially those conducted in a centrifuge; and improved numerical analyses. Integrating most of these aspects in a unified analysis of well-documented case histories offers the opportunity to evaluate our current understanding and capabilities. Despite the general accessibility to numerical codes, theoretical analysis remains a most valuable source of knowledge and judgement. Capabilities of the models and their soundness should be demonstrated. This is done, in this Special Issue, (i) by means of simulating previously controlled and well-instrumented experiments and comparing numerical results with measurements and (ii) by calibrating the model through laboratory tests and back-analysis of real cases. The calibrated model can also be used to explore its response to different conditions, not observed in the field. Contributions to this Special Issue offer excellent examples of most of the topics mentioned.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".