Notice bibliographique
Résumé
Flooding in southern Alberta (Canada) in June 2013 resulted in four fatalities and unprecedented damage to property. More than 250 mm of rain fell over a 36 hour period in the foothills west and southwest of Calgary and began rapidly flowing east through the province's river valleys bringing destruction across southern Alberta. Areas along the Bow, Elbow, Highwood, Red Deer, Sheep, Little Bow, and South Saskatchewan rivers and their tributaries were particularly affected. Bow River experienced flows eight times higher than normal, and Elbor River experienced flows twelve times higher than normal. A total of 32 states of local emergency were declared and 28 emergency operations centres were activated as water levels rose and numerous communities were placed under evacuation orders. City of Calgary (4th largest city in Canada with population of 1.1 M) was hit very hard. Over 100,000 people were displaced throughout the region. More than 35,000 people were without power for weeks. Downtown Calgary was out of reach for two weeks. Twenty bridges were closed and many roads without access for days. Some 2,200 Canadian Armed Forces (CAF) troops were deployed to help in flooded areas. Total damage estimates exceeded C$6 billion and in terms of insurable damages, is the costliest disaster in Canadian history. Two weeks later in July 2013 the City of Toronto (the largest city in Canada with population of 4.7 M) experienced a heavy rainfall event, exceeding 126 mm over two hours (74.4 mm July average), that overwhelmed stormwater and sanitary sewer systems. At least 500,000 people were affected, 1,400 train passengers stranded for hours, all major traffic arteries flooded and over 300,000 people left without the power for a significant period of time. Urban flood losses, including damage from water and sewage that entered homes and businesses through the backup of municipal sewers, were extensive, approaching C$1 billion and making this storm the most expensive storm in the Province of Ontario. These two events prompted the Minister of Public Safety Canada to include in his ‘Report on Plans and Priorities’ to the Prime minister the following statement: …‘The rising cost of natural disasters and the financial burden on Ottawa is the country's biggest public safety risk’… Actions to prevent or reduce the risk of flood damage in Canada should include actions to address both riverine and urban flooding. Riverine floods are the most common natural hazard experienced by Canadians. The Canadian Disaster Database, for example, identifies 62 floods in Canada during the ten-year period from 2003 through 2012. In the 1960s and 1970s few Canadians experienced damage from urban flooding. However, over the past few decades there has been an alarming increase in urban flood losses. Indeed, water damage from sewers backing up into basements and other losses due to extreme rainfall in urban areas likely resulted in urban flood losses more than ten times greater than riverine flood damage. Best practices to prevent and reduce the risk of loss from riverine flooding are well known, and have been tested around the world for several decades. Prohibition of development in zones of flood risk, investments in structural flood defence and a variety of other tools are available to eliminate or reduce the expected loss from riverine flooding. The foundation for riverine flood management involves a clear determination of acceptable risk of flood damage. Best practices for reducing the risk of urban flooding have emerged over the past 25 or 30 years and are distinct from actions to reduce the risk of loss from riverine flooding. The frequency and severity of urban flood damage is determined by factors that include rainfall patterns, lot level actions by property owners and the state of the local sewer infrastructure. Every household connected to the storm or sanitary sewer system is at some risk of loss. Best practices to reduce the risk of urban flood damage include lot level actions by property owners and public investments in sewer systems. The tragic losses in southern Alberta and Toronto have opened a window of opportunity over the next 12 to 24 months for the Government of Canada and other stakeholders to take action to reduce the risk of loss from flooding. Some of the recommendations include:
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».