Interlaboratory Evaluation of a Real-Time Multiplex Polymerase Chain Reaction Method for Identification of Salmon and Trout Species in Commercial Products
Bibliographic record
Abstract
This interlaboratory study evaluated a real-time multiplex polymerase chain reaction (PCR) method for identification of salmon and trout species in a range of commercial products in North America. Eighty salmon and trout products were tested with this method by three independent laboratories. Samples were collected in the United States and Canada, and only the collecting institution was aware of the species declaration. Following analysis with real-time PCR, all three laboratories were able to identify species in 79 of the 80 products, with 100% agreement on species assignment. A low level of fraud was detected, with only four products (5%) found to be substituted or mixtures of two species. The results for two of the fraudulent products were confirmed with alternate methods, but the other two products were heavily processed and could not be verified with methods other than real-time PCR. Overall, the results of this study show the usefulness and versatility of this real-time PCR method for the identification of commercial salmon and trout species.
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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".