A Single-Laboratory Validated Method for the Generation of DNA Barcodes for the Identification of Fish for Regulatory Compliance
Bibliographic record
Abstract
The U.S. Food and Drug Administration is responsible for ensuring that the nation's food supply is safe and accurately labeled. This task is particularly challenging in the case of seafood where a large variety of species are marketed, most of this commodity is imported, and processed product is difficult to identify using traditional morphological methods. Reliable species identification is critical for both foodborne illness investigations and for prevention of deceptive practices, such as those where species are intentionally mislabeled to circumvent import restrictions or for resale as species of higher value. New methods that allow accurate and rapid species identifications are needed, but any new methods to be used for regulatory compliance must be both standardized and adequately validated. "DNA barcoding" is a process by which species discriminations are achieved through the use of short, standardized gene fragments. For animals, a fragment (655 base pairs starting near the 5' end) of the cytochrome c oxidase subunit 1 mitochondrial gene has been shown to provide reliable species level discrimination in most cases. We provide here a protocol with single-laboratory validation for the generation of DNA barcodes suitable for the identification of seafood products, specifically fish, in a manner that is suitable for FDA regulatory use.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".