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Record W2913447538 · doi:10.1139/gen-2018-0101

DNA barcoding of fish species reveals low rate of package mislabeling in Qatar

2019· article· en· W2913447538 on OpenAlexvenueno aff
Kuei‐Chiu Chen, Dalia Zakaria, Heba Altarawneh, Gabriala Andrews, Gowrii Saswathy Ganesan, Karen John, Sarah Khan, Heta Ladumor

Bibliographic record

VenueGenome · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
FundersWeill Cornell Medicine - QatarQatar National Research FundWeill Cornell Medical CollegeFonds National de la Recherche LuxembourgQatar Foundation
KeywordsDNA barcodingBiologyBarcodeSpecies identificationFish <Actinopterygii>Identification (biology)FisheryFish productsZoologyEvolutionary biologyEcologyBusinessMarketing

Abstract

fetched live from OpenAlex

DNA barcoding technique has made it possible to authenticate various species used for food and medicinal purposes. In the identification of seafood species, studies are concentrated in North America, Europe, and Asia. Elsewhere, including countries in the Middle East and North Africa, studies of this sort are scarce. This study focuses on packaged fresh or minimally processed fish fillet available at eight major supermarket chains in Qatar. A cocktail of eight primers attached with M13 tails established for fish species identification was adopted to facilitate PCR and sequencing. Sequences were compared with those available in the Barcode of Life Databases (BOLD Systems) and BLAST in NCBI databases. Among the 62 unique fish packages with resolved sequences, only three are confirmed to be mislabeled, at a rate of about 5%. Two of the substituted species are high value items while the third species was replaced by another, equally low-cost species. The relatively low rate of mislabeling in the samples is perhaps a result of strict local food safety regulations, which may have led to high consistency between the package labels and their contents.

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)0.000
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 score0.057
Threshold uncertainty score0.283

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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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