Grants and Contributions
About this information
In June 2016, as part of the Open Government Action Plan, the Treasury Board of Canada Secretariat (TBS) committed to increasing the transparency and usefulness of grants and contribution data and subsequently launched the Guidelines on the Reporting of Grants and Contributions Awards, effective April 1, 2018.
The rules and principles governing government grants and contributions are outlined in the Treasury Board Policy on Transfer Payments. Transfer payments are transfers of money, goods, services or assets made from an appropriation to individuals, organizations or other levels of government, without the federal government directly receiving goods or services in return, but which may require the recipient to provide a report or other information subsequent to receiving payment. These expenditures are reported in the Public Accounts of Canada. The major types of transfer payments are grants, contributions and \'other transfer payments\'.
Included in this category, but not to be reported under proactive disclosure of awards, are (1) transfers to other levels of government such as Equalization payments as well as Canada Health and Social Transfer payments. (2) Grants and contributions reallocated or otherwise redistributed by the recipient to third parties; and (3) information that would normally be withheld under the Access to Information Act and the Privacy Act.
$830,000.00
May 15, 2020
For-profit organization
Developing Machine Learning and Knowledge Engineering for an Evolving Agriculture Marketplace
948784
ML in grain market analysis will transform the grain services industry. In the financial and insurance industries, leading banks and financial services companies are deploying AI technology, including ML (ML), to streamline their processes, optimize portfolios, decrease risk and underwrite loans amongst other things. The benefits of ML that are already being recognized in these industries can be applied to grain market analysis and portfolio recommendations. This project first seeks to develop and build on FarmLink’s current analytical protocols using ML techniques and secondly to take its enhanced recommendations to use ML to build a customer facing advanced marketing tool. The benefit to our famer customers will be a streamlined, simple to use individualized tool to empower their financial business plans and a step towards a Farmer 4.0.
$220,000.00
Oct 31, 2024
Academia
Leveraging machine learning for improving field pea yield and seed quality
1023601
By leveraging data from ongoing SPP projects, the team will use machine learning (ML) to integrate multi-omics data as a means to elucidate molecular mechanisms underlying pea yield and seed quality.
$237,500.00
Oct 3, 2024
For-profit organization
Automated Geotechnical and Geological Mapping using Machine Learning and Computer Vision
1026037
The objective of the project is to automatically identify and classify geological and geotechnical features from LiDAR data to improve decision making around safety and orebody knowledge within the mining and tunnelling sectors.
$37,500.00
Apr 1, 2020
For-profit organization
Usage-Based Agricultural Insurance Using Federated Machine Learning for International Markets
944635
The intent of this project is to develop key market contacts and potential collaborators, as well as to obtain legal expertise to develop a strategy for licensing or sale of the product, intellectual property protection.
$172,000.00
Aug 1, 2025
For-profit organization
Machine Learning Model for SAS to R Conversion in Pharmaceutical Industry
1033612
This project is to develop an accurate automatic translation program for migrating SAS code to R. SAS is a legacy statistical software widely used in pharmaceutical, insurance, and financial sectors. R is an open-sourced statistical computing and data visualization programming language that can enable these modern functionality.
$250,000.00
Apr 1, 2024
For-profit organization
Automated Geotechnical and Geological Mapping using Machine Learning and Computer Vision
1018635
The objective of the project is to automatically identify and classify geological and geotechnical features from LiDAR data to improve decision making around safety and orebody knowledge within the mining and tunnelling sectors. The project is a continuation of RockMass’s IP.
$4,500.00
Aug 10, 2017
Signal processing and machine learning to make sense of noisy signals
URU
$125,000.00
May 10, 2017
Group actions and symplectic techniques in Machine Learning and Computational Geometry
RGPIN
$25,000.00
Aug 23, 2017
Capturing Machine Learned 3D Foot Shapes from a Single Camera
EGP
$140,000.00
May 10, 2017
Computational Foundations of Machine Learning in the Era of Big Data
RGPIN