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Record W1990016126 · doi:10.1021/jf103241y

Interlaboratory Evaluation of a Real-Time Multiplex Polymerase Chain Reaction Method for Identification of Salmon and Trout Species in Commercial Products

2011· article· en· W1990016126 on OpenAlexaffabout
Rosalee S. Hellberg, Amanda M. Naaum, Sara M. Handy, Robert Hanner, Jonathan R. Deeds, Haile F. Yancy, Michael T. Morrissey

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsUniversity of Guelph
FundersU.S. Food and Drug AdministrationOregon State UniversityCalifornia Department of Fish and WildlifeMassachusetts Department of Fish and Game
KeywordsTroutPolymerase chain reactionSpecies identificationMultiplex polymerase chain reactionFisheryIdentification (biology)MultiplexBiologyReal-time polymerase chain reactionFish <Actinopterygii>ZoologyEcologyBioinformaticsGenetics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.284
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2011
Admission routes2
Has abstractyes

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