This Solicitation opportunity from Government of Canada was posted on January 17, 2024. The submission period has ended. Browse the details below for market research, or find similar active opportunities.
Development and parameterization of an ecosystem model in the SpaDES platform.
Closed
NRCAN-5000078028CanadaSubmission Closed
Contract Overview
Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.
General Info
Agency
Government of Canada → Natural Resources CanadaView Agency
NAICS
N/A
Place of Performance
*Canada, CANSet-Aside
NONE
Documents
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Timeline
PhaseClosed
Submission Closed
Organization & Contact Information
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AgencyGovernment of Canada → Natural Resources Canada
Contacts1 person available
OfficeN/A
Office AddressN/A
Contacts
Full Description
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Advance Contract Award Notice (ACAN)
An ACAN is a public notice indicating to the supplier community that a department or agency intends to award a contract for goods, services or construction to a pre-identified supplier, thereby allowing other suppliers to signal their interest in bidding, by submitting a statement of capabilities. If no supplier submits a statement of capabilities that meets the requirements set out in the ACAN, on or before the closing date stated in the ACAN, the contracting officer may then proceed with the award to the pre-identified supplier.
Definition of the requirement:
The Department of Natural Resources Canada (NRCan) has a requirement for a technical and ecological application of an ecosystem model for northern Ontario implemented in SpaDES (Spatially Discrete Event Simulator) that will assess the natural range of variation (NRV) in ecosystem characteristics including tree composition, age and boreal landbird habitat. The results will be integrated as SpaDES-compatible modules that can be run over any part of the managed area covered by boreal managed forest zone (MFZ) in Ontario. This allows for continuous flexibility and updating as policies and regulations change, and as datasets and models improve.
