DNA barcoding of fish species reveals low rate of package mislabeling in Qatar
Bibliographic record
Abstract
DNA barcoding technique has made it possible to authenticate various species used for food and medicinal purposes. In the identification of seafood species, studies are concentrated in North America, Europe, and Asia. Elsewhere, including countries in the Middle East and North Africa, studies of this sort are scarce. This study focuses on packaged fresh or minimally processed fish fillet available at eight major supermarket chains in Qatar. A cocktail of eight primers attached with M13 tails established for fish species identification was adopted to facilitate PCR and sequencing. Sequences were compared with those available in the Barcode of Life Databases (BOLD Systems) and BLAST in NCBI databases. Among the 62 unique fish packages with resolved sequences, only three are confirmed to be mislabeled, at a rate of about 5%. Two of the substituted species are high value items while the third species was replaced by another, equally low-cost species. The relatively low rate of mislabeling in the samples is perhaps a result of strict local food safety regulations, which may have led to high consistency between the package labels and their contents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".