Hazardmatch: an application of artificial intelligence to landslide susceptibility mapping, Howe Sound area, British Columbia
Bibliographic record
Abstract
HazardMatch est un système informatique conçu pour le dressage de cartes de susceptibilité aux glissements de terrain tout en se servant de la sémantique technologique, un champ d’intelligence artificielle. Ce système fournit un logiciel selon lequel un expert en glissements de terrain peut décrire, en se servant du vocabulaire spécialisé, les caractéristiques des endroits démontrant une haute susceptibilité aux glissements de terrain. Le logiciel se sert du même langage pour générer des descriptions sémantiques de sous-régions d’une région spécifique pour joindre les caractéristiques qui sont constantes. Les cartes de susceptibilité sont donc produites à partir d’un système d’évaluation de ces caractéristiques. L’avantage de cette approche est que les résultats peuvent être facilement expliqués et justifiés à ceux qui ne sont pas experts. De plus, si les résultats sont faux, l’erreur est facilement retraçable à la base de données. Les erreurs peuvent donc être facilement corrigées pour produire des cartes plus précises. Un essai préliminaire de cette méthode a été effectué dans la chaîne côtière à l’est de Howe Sound, Colombie-Britannique. HazardMatch is a computer system for the production of landslide susceptibility maps using semantics and semantic technology, a field of artificial intelligence. It provides a software framework within which a landslide expert can describe, using language as close to natural (specialist) language as possible, the properties of surface locations which are highly susceptible to landslides. It uses the same language to generate semantic descriptions of all sub-areas of the area of interest
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".