Initiative #59
Clustering to characterize extreme marine conditions for the benthic region of the Northeastern Pacific continental margin
Listed in the registry Pilot Original language : English
About the initiative
Description of the initiative
We introduce a method for characterizing extremes that uses machine learning to divide the data into regions with relatively consistent environmental conditions (temperature, oxygen, acidity), and define the extremes based on the historical statistics of variability of each of these fields. unsupervised clustering with k-means
AI family
Automation and Decision Support
- AI sub-category
- Predictive Analytics
- AI capability
- General Cross-cutting Analytical Tools
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Lifecycle
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Ideation
Stage status : Completed
May 29, 2025
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Assessment
Stage status : Completed
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Approved
Stage status : Completed
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Development
Stage status : Completed
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Pilot
Stage status : In progress
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Production
Stage status : Upcoming
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Archived
Stage status : Upcoming
Schedule for this initiative
- Completed
- In progress
- Planned
- Not applicable
- Today
- Ideation
A similar need?
Nobody has come forward yet. If your team faces the same problem, say so: it helps bring initiatives together and share a solution.
Contacts
- Requester
- Quan, Eric
- Sector contact
- Holdsworth, Amber
- Subject matter expert
- Holdsworth, Amber
Data and tools
- Sector
- Ecosystems and Oceans Science
- Region
- Pacific
- Could this initiative be shared with the Treasury Board Secretariat?
- Yes
- Submitted on
- May 29, 2025
Primary users
- Departmental employees
Government priorities and declaration
Declared to TBS Recorded as transmitted to TBS; not editable here.
- Declared name of the AI system
- Characterizing Environmental Extremes Using Machine Learning
- TBS registry identifier
- 2526-DFO-MPO-015
- Status declared to TBS
- Pilot
- Sent to TBS on
- October 10, 2025
- Purpose of the system, as declared
- This project developed a machine learning-based method to identify environmental extremes by segmenting data into regions with consistent conditions (e.g., temperature, oxygen, acidity). Extremes were defined based on historical variability within each region, enabling more targeted and context-aware analysis of environmental changes.