<scp>DNA</scp> barcoding detected improper labelling and supersession of crab food served by restaurants in India
Bibliographic record
Abstract
BACKGROUND: Detection of improper labelling of raw and processed seafood is of global importance for reducing commercial fraud and enhancing food safety. Crabs are crustaceans with intricate morphological as well as genetic divergence among species and are popular as seafood in restaurants. Owing to the high number of crab species available, it can be difficult to identify those included in particular food dishes, thus increasing the chance of supersession. DNA barcoding is an advanced technology for detecting improper food labelling and has been used successfully to authenticate seafood. RESULTS: This study identified 11 edible crab species from India by classical taxonomy and developed molecular barcodes with the cytochrome c oxidase I (COI) gene. These barcodes were used as reference barcodes for detecting any improper labelling of 50 restaurant crab samples. Neighbour-joining tree analysis with COI barcodes showed distinct clusters of restaurant samples with respective reference species. The study demonstrated 100% improper labelling of restaurant samples to cover up acts of inferior crab supersession. CONCLUSION: DNA barcoding successfully identified 11 edible crabs in accordance with classical taxonomy and discerned improper crab food labelling in restaurants of India.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".