Statistical modelling of seafood fraud in the Canadian supply chain
Bibliographic record
Abstract
Abstract Seafood misrepresentation, encompassing product adulteration, mislabelling, and substitution, among other fraudulent practices, has been rising globally over the past decade, greatly impacting both the loss of important fish species and the behaviour of human consumers alike. While much effort has been spent attempting to localise the extent of seafood mislabelling within the supply chain, strong associations likely existing among key players have prevented timely management and swift action within Canada and the USA in comparison to European nations. To better address these shortcomings, herein frequentist and Bayesian logistic Generalised Linear Models (GLMs) are developed in R and Stan for estimation, prediction and classification of product mislabelling in Metro Vancouver, British Columbia, Canada. Obtained results based on odds ratios and probabilities paint a grim picture and are consistent with general trends found in past studies. This work paves the way to rapidly assess the current state of knowledge surrounding seafood fraud nationally and on a global scale using established statistical methodology.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".