Revisiting landslide risk terms: IAEG commission C-37 working group on landslide risk nomenclature
Bibliographic record
Abstract
Abstract Significant effort has been devoted during the last few decades to the development of methodologies for landslide hazard and risk assessment. All of this work requires harmonization of the methodologies and terminology to facilitate communication within the landslide community, as well as with stakeholders and researchers from other disciplines. Currently, glossaries, and methodological recommendations exist for preparing landslide hazard and risk studies. Nevertheless, there is still debate on the usage of some terms and their implementation in practice. In 2016, the IAEG commission C-37 established a working group with the objective of preparing a standard multilingual glossary of landslide hazard and risk terms. The glossary aims for the international harmonization of the terms and definitions with those used in associated disciplines (e.g., seismology, hydrology) while considering landslides specifically. The glossary is based on previously published glossaries, including those prepared by ISSMGE TC32, FedIGS, JTC1, and UNISDR. This article presents comments on the meaning of some of the terms that have required further discussion. The English version of the glossary is also included.
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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".