cl_maintenanceAndUpdateFrequency

RI_540

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    The Soils Parent Materials raster prediction surface was crated using a Random Forest classifier trained with single component polygons from soils, terrain and ecosystem mapping projects, and 18 topographic predictor layers derived from the provincial 25m DEM. It is a prediction of mode of deposition of the soils parent material. The model was run by Ecoprovince and also enforced topographic constraints on some materials. The results of the Ecoprovince models were compiled into a provincial raster layer. The methodology and results are described in the research paper, Improved Soil Mapping in British Columbia, Canada with Legacy Soil Data and Random Forest Digital Soil Mapping Across Paradigms, Scales and Boundaries, 2016, ISBN : 978-981-10-0414-8 C. Bulmer, M. G. Schmidt, B. Heung, C. Scarpone, J. Zhang, D. Filatow, M. Finvers, S. Berch, S. Smith, DOI 10.1007/978-981-10-0415-5_24. This layer is used in the [Soils Information Finder Tool](https://catalogue.data.gov.bc.ca/dataset/af8c3ff1-c64c-4ee5-9412-f02a85bbbfec).

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    Territory of intervention of the regional directorates of the General Directorate of Civil and Fire Safety (DGSCSI). Purpose: To know the limits of the territories covered by the various regional directorates of civil and fire safety.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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    This layer represents the regional boundaries that the Ministry of Social Development and Poverty Reduction maintains. All service and regional offices belong to one of the regions. This layer is a multipart polygon feature. Please note that this dataset refers to WorkBC boundaries before April 2019 and is no longer valid.

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    IDMI project boundary

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    Urban areas in the territory of the city of Saguenay**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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    Quad biking trails in Saguenay**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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    The Coastal Oceanography and Ecosystem Research section (DFO Science) reviewed the presence of Cod in the Population Ecology Division (DFO Science) Ecosystem Survey trawls to describe the likelihood of presence. The survey consists of a stratified random design using a bottom trawl. This layer was created for consideration in oil spill response planning. A version of this dataset was created for the National Environmental Emergency Center (NEEC) following their data model and is available for download in the Resources section. Cite this data as: Lazin, G., Hamer, A.,Corrigan, S., Bower, B., and Harvey, C. Data of: Likelihood of presence of Atlantic Cod in Area Response Planning pilot areas. Published: June 2018. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/af2bf6c0-481d-4445-bbc6-7a785d2a9aa9

  • The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 * Note: the forest composition (leading tree genus) map depicts forest attributes in 2001. How can this data be used? The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.

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    As the COVID-19 pandemic spreads, researchers and health professionals have noted large differences in the impact that the infection has on individuals. Whereas some remain asymptomatic and unaware of their infection or experience only mild symptoms, others require hospitalization, ventilation, and may even die. As research evidence accumulates, both nationally and internationally, it appears that certain health characteristics, such as obesity or the presence of chronic conditions, increase the risk of severe outcomes among those who are infected with the novel coronavirus. To better understand which segments of the Canadian population may be vulnerable to severe health outcomes related to COVID-19, Statistics Canada and the Public Health Agency of Canada have worked collaboratively to build an index of underlying health conditions in the adult household population. Using information from the 2017/2018 Canadian Community Health Survey, new data tables released today estimate the proportion of the adult household population who may be at greater risk of severe health outcomes related to COVID-19 due to the presence of underlying health conditions.

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    All manmade waterbodies, including reservoirs and canals, for the province