Notice bibliographique
Résumé
The intent of this poster presentation is to demonstrate the various GIS data layers used in the documentation and analysis of snow profiles and avalanche paths. These posters will present a flow chart defining the workflow of this data in a GIS. The historical avalanche path data is loaded in to a database that can relate the hard copy snowpit and weather data. These data layers can be displayed over other GIS base layers such as DEM, DRG, DOQ, soils/geology, and vegetation cover. Integration of realtime weather and snow profile data can be added to this for analysis. I will correlate these types of data and explain how they could be used in analysis to enhance predictions and provide more information. Digital data collection tools will be displayed that can load new data directly in to a GIS Database with little hand entry. For many years the Swiss, Canadian and other snow scientist have been using GIS to monitor, document, and model avalanche occurrence, snow profiles, and weather. In the United States the use has been limited to only a few areas. The recent advances in technology and the lowering of overall cost have made it so that much can be done. This presentation will attempt to familiarize the viewer with the data types and applications. Historical (legacy) data can be used as references in the GIS when the hand drawn avalanche paths are digitized and loaded in to a geodatabase. Avalanche path data (consisting of avalanche archive records and photographs, avalanche mapping of starting zones, size, frequency and area extent of danger), snowpit, and weather data can be converted from hard copy to digital. The weather data is often in digital and can be linked or loaded to the avalanche and snowpit profile database as well. The base data layers such as DRG (digital topo map) and DOQ (aerial photography) provide visual information and the ability to identify avalanche terrain. These can also help in referencing the topography of the avalanche areas. The DEM (digital elevation model) allows various terrain analyses: mean slope, minimum slope, maximum slope, mean aspect, and curvature. When the avalanche path data is overlaid on the DEM it can be analyzed using the nearest neighbor model. This data type is becoming more readily available and at higher resolution (most of the US is now available at 10m and there is 2 meter data for some areas) and accuracy. Much of it is free from the USGS or the USDA as well as many state GIS data clearing houses. Other important GIS data layers are the hydrography (rivers, streams and lakes), geology, vegetation, tree ring, buildings and roads. Hydrography data shows drainages where avalanches could be constructed by potential terrain traps. Vegetation and geologic layers and often combined with slope angles and curvature to help gauge friction parameters (destructive force) from the various sizes of the volume of the avalanche’s release. Tree ring data can help document and predict the frequency and sizes of avalanches along their tracks. Building zoning and road data can define potential areas that will suffer destruction. The analyst can use the GIS to combine this data with real time weather and snow data of the area by modern digital collection tools to assess risk and level of danger. The advent of pocket computers and mobile GIS/GPS software has made it possible to collect digital field data about avalanche paths and snow profiles. This data can be integrated with the historical avalanche database after it has been brought into the digital framework. Likewise it is possible to take the historical data into the field for a reference and create a new file for the latest occurrence. By collecting a new weather and profile and give it a spatial position with a GPS point it possible to hyperlink snowpit profile graphs, photographs, and weather station locations to a specific spatial place. This is useful in viewing the changes to a particular place over a season and the ability to quickly call up past information. Remote weather stations can send weather data every half hour by phone line or wireless transmission. Through the internet a weather program called Meteorologix can bring real time Doppler radar weather images directly into the GIS software view window. As well as digital collection by hand held computer from traditional weather and snow observation sites. With these
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,003 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».