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Record W2147040763 · doi:10.3109/19401736.2011.588217

FISH-BOL and seafood identification: Geographically dispersed case studies reveal systemic market substitution across Canada

2011· article· en· W2147040763 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMitochondrial DNA · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Food Inspection AgencyOntario Genomics InstituteGenome CanadaAlfred P. Sloan Foundation
KeywordsBarcodeDNA barcodingIdentification (biology)BiologyFisheryGeographyBusinessEcologyMarketing

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: The Fish Barcode of Life campaign involves a broad international collaboration among scientists working to advance the identification of fishes using DNA barcodes. With over 25% of the world's known ichthyofauna currently profiled, forensic identification of seafood products is now feasible and is becoming routine. MATERIALS AND METHODS: Driven by growing consumer interest in the food supply, investigative reporters from five different media establishments procured seafood samples (n = 254) from numerous retail establishments located among five Canadian metropolitan areas between 2008 and 2010. The specimens were sent to the Canadian Centre for DNA Barcoding for analysis. By integrating the results from these individual case studies in a summary analysis, we provide a broad perspective on seafood substitution across Canada. RESULTS: Barcodes were recovered from 93% of the samples (n = 236), and identified using the Barcode of Life Data Systems "species identification" engine ( www.barcodinglife.org ). A 99% sequence similarity threshold was employed as a conservative matching criterion for specimen identification to the species level. Comparing these results against the Canadian Food Inspection Agency's "Fish List" a guideline to interpreting "false, misleading or deceptive" names (as per s 27 of the Fish Inspection regulations) demonstrated that 41% of the samples were mislabeled. Most samples were readily identified; however, this was not true in all cases because some samples had no close match. Others were ambiguous due to limited barcode resolution (or imperfect taxonomy) observed within a few closely related species complexes. The latter cases did not significantly impact the results because even the partial resolution achieved was sufficient to demonstrate mislabeling. CONCLUSION: This work highlights the functional utility of barcoding for the identification of diverse market samples. It also demonstrates how barcoding serves as a bridge linking scientific nomenclature with approved market names, potentially empowering regulatory bodies to enforce labeling standards. By synchronizing taxonomic effort with sequencing effort and database curation, barcoding provides a molecular identification resource of service to applied forensics.

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.

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)0.000
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 score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.268
Teacher spread0.243 · 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