Occurrence neighbourhoods and risk assessment from landslide hazard in northern Spain
Bibliographic record
Abstract
This contribution analyzes the problem of selecting the desirable characteristics of a study area when using geo-information for natural risk assessment.Shape, boundary, density of detail of the study area and the distribution of hazardous occurrences can be fundamental in conditioning the estimation of values in a map of expected risk.A study area in the Basque Country of northern Spain is used in which previous studies produced maps of risks for linear infrastructures, land uses and buildings, from thousands of shallow translational landslides.The area is reconsidered here in terms of five telescopic sub-areas corresponding to different neighbourhoods of the landslide occurrences.The results of the corresponding hazard predictions are interpreted via prediction-rate tables and curves obtained from blind tests, i.e., prediction maps obtained using only part of the occurrences cross-validated with the distribution of the remaining occurrences.The subsequent introduction of socioeconomic thematic maps and scenarios enables the derivation of risk maps based on the prediction rates, the hazard maps and the socioeconomic indicator values.The comparison of the risk maps from the different study-area datasets is used to assess their impact on risk values and to provide guidance on how to perform the selection maintaining greater significance.A critical issue is the loss of significance when reducing study area neighbourhoods closer or further away from the hazardous locations.The application is an example of a general purpose spatial predictive modelling processing strategy for which dedicated software has been developed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".