An economic analysis of private incentives to adopt DNA barcoding technology for fish species authentication in Canada
Bibliographic record
Abstract
The increasing spate of species substitution and mislabelling in fish markets has become a concern to the public and a challenge to both the food industry and regulators. Species substitution and mislabelling within fish supply chains occurs because of price incentives to misrepresent products for economic gain. Emerging authenticity technologies, such as the DNA barcoding technology that has been used to identify plants and animal (particularly fish) species through DNA sequencing, offer a potential technological solution to this information problem. However, the adoption of these authenticity technologies depends also on economic factors. The present study uses economic welfare analysis to examine the effects of species substitution and mislabelling in fish markets, and examines the feasibility of the technology for a typical retail store in Canada. It is assumed that increased accuracy of the technology in detecting fraud and enforcement of legal penalties and other associated costs would be likely to discourage cheating. Empirical results suggest that DNA barcoding technology would be feasible presently for a typical retail store only if authentication is done in a third party laboratory, as it may not be feasible on an individual retail store level once fixed and other associated costs of the technology are considered.
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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".