Prediction rate functions of landslide susceptibility applied in the Iberian Peninsula
Bibliographic record
Abstract
Prediction rate functions of landslide susceptibility applied in the Iberian Peninsula A. G. Fabbri1, F. F. Chung2, P. Napolitano3, J. Remondo4 & J. L. Zezere5 1ITC, 7500 AA Enschede, & SPINlab, Vrije Universiteit, I081 HV Amsterdam, The Netherlands 2 Geological Survey of Canada, Ottawa KIA OE8, Canada 3ACTA, Via D. Fontana 40, 80128 Napoli, Italy 4DICITIMAC, Universidad de Cantabria, 39005 Santander, Spain 5 Centro de Estudos Geograficos, Universidade de Lisboa, I699 Lisboa, Portugal Abstract The prediction models of landslide susceptibility that we have developed, generate not only predicted hazard maps but also prediction-rate curves, which allow us to estimate the probabilities of the occurrences of future landslides from the hazard maps. For a risk-analysis estimating the \“economic” values of the population, properties, economic activities, etc., the estimates of the probabilities are absolutely critical statistics. To use the hazard maps for risk analysis, we must be able to estimate the probability of occurrence of a future landslide at each hazard level in the maps. Without the estimates of such probabilities, the hazard maps can provide only indicators of landslide hazard, but they cannot be directly useful for a decision process. With those probabilities, however, decision makers can quantitatively assess the economic sterilization due to the possible damage under the assumptions of appropriate scenarios. Hence, they can take a learned and informed decision. Predictions are based on \“Favourability Functions” that integrate the spatial relationships between the distribution of trigger zones of specific dynamic types of landslides and the surrounding mapping units and contour intervals. The latter two represent the spatial support to estimate the likelihood of further failures.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".