Mislabelled: Montreal Investigation Results and How to Fix Canada's Seafood Fraud Problem
Bibliographic record
Abstract
Seafood fraud is a widespread global and Canadian problem. Over the last decade, numerous studies have exposed seafood fraud and mislabelling around the world. A 2016 review by Oceana of more than 200 studies from 55 countries found that one in five of more than 25,000 seafood samples was mislabelled. DNA testing conducted by Oceana Canada from 2017 to 2019 has revealed that an alarming 47 per cent of more than 470 seafood samples tested from food retailers and restaurants in six Canadian cities were mislabelled. The most recent round of testing conducted in Montreal in July 2019 showed that a staggering 61 per cent of samples were mislabelled. Oceana Canada’s national investigation into seafood fraud—the most comprehensive study of its kind conducted in Canada—found farmed fish served up as wild caught; cheaper species substituted for more expensive ones; fish banned in many countries because of health risks masquerading as another species; and exposed rampant problems with the current traceability and labelling standards for fish in Canada. Mislabelling can happen on this scale because the global seafood supply chain is obscure and increasingly complex. Once a fish has been caught, it can travel halfway around the world for processing, crossing many national borders before it ends up on our plate. Canadian labelling standards further complicate this issue: our labelling regulations – despite being amended in 2019 – are outdated and unnecessarily cumbersome. On top of this, Canada has a fragmented regulatory system regarding seafood traceability, with no single agency wholly responsible for addressing it. The federal government has a responsibility to combat this widespread problem. There is a solution: implementing boat-to-plate traceability and comprehensive labelling in Canadian seafood supply chains. Boat-to-plate traceability means that key information is paired with fish products from the point of harvest to the point of sale. By tracking fish products this way, we can significantly reduce instances of fraud and mislabelling, better understand where it happens, enhance confidence in our food system and help Canada’s seafood industry access global markets – many of which already demand stronger traceability. Boat-to-plate traceability works. The European Union implemented measures to track fish at every step from capture to consumption and fraud rates have declined significantly.12 The United States has put boat-to-bordertraceability in place for some at-risk species groups. It’s time for Canada to do more. Mounting evidence shows seafood fraud is an urgent, widespread issue across the country that needs attention from the federal government. Canadians deserve to know that all seafood sold in Canada is safe, honestly labelled and legally caught.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".