Single vs. Pooled: Metabarcoding Based Species Misrepresentation Detection of Sushi in Ontario by Sample Pooling Compared to Conventional DNA Barcoding
Bibliographic record
Abstract
Rising complexity of seafood supply chain necessitates enhanced efficacy and throughput of DNA barcode-based analytical techniques for seafood traceability. This study evaluated the applicability of sample pooling followed by metabarcoding for high-throughput seafood species identification and is the first ever application of this strategy for seafood traceability. Sushi samples from grocery and retail settings were initially tested by conventional DNA barcoding for species identity establishment prior to sample pooling. Species mislabeling, substitution and common name ambiguities were detected in sushi sold in restaurants and grocery stores in Ontario. Sample pooling strategy could establish species identities at variable levels of taxonomic hierarchy, including species and genera informative of species misrepresentation i.e., mislabeling, substitutions, and common name ambiguities. Nonetheless, key uncertainties such as primer universality, resolution, and reference database blind spots must be addressed prior to its broader applications at upper levels of seafood supply chain for testing large consignment of samples.
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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) | 0.001 | 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".