Statistical modelling of seafood fraud in the Canadian supply chain
Phillips, J. D.; De Vuono-Fraser, F. A.
Show abstract
AbstractSeafood 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.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Machine learning-based short-term forecasting of COVID-19 hospital admissions using routine hospital patient data 89%
- Estimation of the probability of epidemic fade-out from multiple outbreak data 88%
- Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number 88%
Similar papers in this journal
- Genomic and machine learning-based screening of aquaculture associated introgression into at-risk wild North American Atlantic salmon (Salmo salar) populations. 90%
- In-field genetic stock identification of overwintering coho salmon in the Gulf of Alaska: Evaluation of Nanopore sequencing for remote real-time deployment 88%
- Dirichlet-multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.