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AI-Powered Ocean Health and Carbon Sequestration Monitoring

Ecology · Research & Development

What it collects

About a measurement
Anonymized data
  • Publicly available ocean sensor data from Ocean Networks Canada, including historical measurements used to pre-train the model, as well as live readings from the ROV's novel sensor suite (e.g. water chemistry, optical signals relevant to chlorophyll estimation).
Run by
National Research Council Canada (NRC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

EcologyResearch & Development

This system uses a transformer-based machine learning model to predict chlorophyll concentrations in the ocean, helping researchers assess marine ecosystem health and capacity for carbon uptake. Developed by the National Research Council Canada in partnership with the University of Victoria, it processes publicly available ocean sensor data using a remotely operated vehicle. The system is currently in development and is used exclusively by Government of Canada employees — it does not affect members of the public directly, and no personal information is collected.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Publicly available ocean sensor data from Ocean Networks Canada, including historical measurements used to pre-train the model, as well as live readings from the ROV's novel sensor suite (e.g. water chemistry, optical signals relevant to chlorophyll estimation).

Processing

Classification & Prediction
  • A transformer-based machine learning model pre-trained on historical ocean data predicts chlorophyll concentrations. The model is a sequence-based predictor that classifies and estimates numeric outputs from structured sensor readings.

What it does

Sensing (Perceptive AI)
Human decides
  • A novel suite of sensors mounted on a remotely operated vehicle (ROV) captures raw ocean measurements, which are then processed into structured data for the predictive model. Researchers review the structured outputs.
Deciding (Analytical AI)
Human decides
  • A transformer-based machine learning model predicts chlorophyll concentrations from sensor inputs. The model scores and classifies ocean conditions; researchers interpret the predictions to assess ecosystem health and carbon sequestration capacity.

Outputs

About a measurement
Anonymized data
  • Predicted chlorophyll concentration values and derived assessments of marine ecosystem health and carbon sequestration capacity. These outputs are numeric estimates produced for research use by GC employees.

Run by

National Research Council Canada (NRC)
  • NRC is the Government of Canada department accountable for deploying this AI system, in partnership with the University of Victoria, to monitor ocean health and carbon sequestration.

Government of Canada AI Register — 2526-NRC-CNRC-002

Built by

University of Victoria
  • A research team from the University of Victoria partnered with NRC's Ocean program to develop and operate this monitoring system.

Government of Canada AI Register — 2526-NRC-CNRC-002

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs and data are accessible to NRC researchers and their University of Victoria partners (GC employees and affiliated researchers). No public access is described.
Not available to me
  • The register states AI use is not disclosed to users; the primary users are GC employees. There is no mechanism described for members of the public to access the system's outputs or data.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Algorithmic TransparencyThe system is listed on the Government of Canada's public AI register, which provides high-level information about its purpose, data sources, and capabilities. However, the register notes that AI use is not disclosed to users of the system. Members of the public may request further information through the National Research Council Canada.

Risks and safeguards

  • Civil liberties harmThe project includes a cybersecurity component to assess vulnerabilities in the ROV's sensor network, protecting data integrity and privacy. No personal information is collected, reducing civil liberties risks. The system is in development and used only by GC employees, limiting exposure.