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Helpful Places

Government of Canada – DTPR AI Register

AI-Assisted Extraction of Protein Digestibility Data

Research & Development

What it collects

Operational data
Anonymized data
  • Scientific research papers on amino acid composition and protein digestibility. These are published documents containing nutrition data, not personal information about individuals.
Run by
Agriculture and Agri-Food Canada (AAFC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization

What it is for

Research & Development

This system uses AI and automation to read scientific research papers and extract data about how well proteins in food are digested by the human body, focusing on amino acid composition. It calculates amino acid scores against standard reference patterns and presents the results in summary tables or charts. The system is intended for use by Government of Canada employees at Agriculture and Agri-Food Canada and does not process personal information.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Scientific research papers on amino acid composition and protein digestibility. These are published documents containing nutrition data, not personal information about individuals.

Processing

Computer Vision
  • Computer vision and image recognition are applied to detect and extract nutrition data from tables, charts, and figures embedded in scientific research papers.
Optimization
  • Workflow automation and optimization are used to streamline the processing pipeline from document ingestion through data extraction to score calculation and report generation.

What it does

Deciding (Analytical AI)
Human decides
  • Calculates amino acid scores based on various reference patterns and identifies the limiting amino acid. Results are presented to GC employees as summary tables or graphs to inform their research decisions.
Sensing (Perceptive AI)
Human decides
  • Uses computer vision and image recognition to read and interpret figures, tables, and text in scientific research papers, extracting structured nutrition data from unstructured document content.

Outputs

About a measurement
Anonymized data
  • Amino acid scores calculated against reference patterns, summary tables, and graphs highlighting the limiting amino acid — structured numeric outputs derived from the extracted literature data.
Operational data
Anonymized data
  • Summary tables and graphs presenting extracted protein digestibility and amino acid composition data, intended for use by AAFC researchers and GC employees in nutrition science work.

Run by

Agriculture and Agri-Food Canada (AAFC)
  • Agriculture and Agri-Food Canada (AAFC) is the federal department deploying this AI system. Its primary users are GC employees conducting nutrition research on protein digestibility.

Government of Canada AI Register — 2526-AAFC-AAC-006

Built by

Government of Canada
  • The system was developed by the Government of Canada, indicating in-house development rather than a third-party commercial vendor.

Government of Canada AI Register — 2526-AAFC-AAC-006

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs — amino acid scores, summary tables, and graphs — are available to Agriculture and Agri-Food Canada employees and GC staff who are the primary users of the system.

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 TransparencyAI use is disclosed to users of the system. GC employees using this tool are informed that AI and automation are involved in extracting and interpreting nutrition data from scientific literature.

Risks and safeguards

No risks or safeguards have been published for this system yet.