Bibliographic record
Abstract
Abstract The rising demand for seafood and trade globalization has brought about a rapid increase in the number of fish species traded. Consequently, the occurrence of mislabelling is growing as well, reaching levels of concerns in USA, Canada and Europe. In this light, the evolving consciousness of consumers and the new exigencies of commerce call for greater safety and quality requirements. These factors have made urgent the need for efficient traceability systems, aimed to ensure transparency on the identity and origin of the traded products and the compliance with the regulations concerning illegal fisheries and labelling. Moreover, greater efforts are necessary to create a list of market names that can be recognized both locally and internationally, in order to overcome the confusion regarding fish denominations. In this context, molecular analysis represents the most promising challenge to verify and support traceability in the seafood chain. Nowadays, the three mitochondrial genes cytb , COI and 16srRNA are the most targeted for this purpose and, among the available procedures, the DNA bar coding is the most commonly applied to verify the labelling compliances, also at the official level. In this review, the most important issues relating to these topics have been reported and discussed.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".