Notice bibliographique
Résumé
Abstract Debris flows are one of the most destructive landslide processes worldwide, given their ubiquity in mountainous areas occupied by human settlement or industrial facilities around the world. Given the episodic nature of debris flows, these hazards are often un- or under-recognized. Three fundamental components of debris-flow risk assessments include frequency-magnitude analysis, numerical scenario modeling, and consequence analysis to estimate the severity of damage and loss. Recent advances in frequency-magnitude analysis take advantage of developments in methods to estimate the age of deposits and size of past and potential future events. Notwithstanding, creating reliable frequency-magnitude relationships is often challenged by practical limitations to investigate and statistically analyze past debris-flow events that are often discontinuous, as well as temporally and spatially censored. To estimate flow runout and destructive potential, several models are used worldwide. Simple empirical models have been developed based on statistical geometric correlations, and two-dimensional and three-dimensional numerical models are commercially available. Quantitative risk assessment (QRA) methods for assessing public safety were developed for the nuclear industry in the 1970s and have been applied to landslide risk in Hong Kong starting in 1998. Debris-flow risk analyses estimate the likelihood of a variety of consequences. Quantitative approaches involve prediction of the annual probability of loss of life to individuals or groups and estimates of annualized economic losses. Recent progress in quantitative debris-flow risk analyses include improved methods to characterize elements at risk within a GIS environment and estimates of their vulnerability to impact. Improvements have also been made in how these risks are communicated to decision makers and stakeholders, including graphic display on conventional and interactive online maps. Substantial limitations remain, including the practical impossibility of estimating every direct and indirect risk associated with debris flows and a shortage of data to estimate vulnerabilities to debris-flow impact. Despite these limitations, quantitative debris-flow risk assessment is becoming a preferred framework for decision makers in some jurisdictions, to compare risks to defined risk tolerance thresholds, support decisions to reduce risk, and quantify the residual risk remaining following implementation of risk reduction measures.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,006 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».