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Record W2134658177 · doi:10.1177/104063870301500217

Evaluation of a Digestion Assay and Determination of Sample Size and Tissue for the Reliable Detection of <i>Trichinella</i> Larvae in Walrus Meat

2003· article· en· W2134658177 on OpenAlexaff
Daniel Leclair, Lorry B. Forbes, Sandy Suppa, Alvin A. Gajadhar

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2003
Typearticle
Languageen
FieldMedicine
TopicParasitic Diseases Research and Treatment
Canadian institutionsCanadian Food Inspection AgencyMakivik Corporation
Fundersnot available
KeywordsTrichinellaBiologyLarvaTongueGastroschisisVeterinary medicineFisheryZoologyAnatomyFood sciencePathologyEcologyMedicine

Abstract

fetched live from OpenAlex

A digestion assay was validated for the detection of Trichinella larvae in walrus (Odobenus rosmarus) meat, and appropriate samples for testing were determined using tissues from infected walruses harvested for food. Examination of muscles from 3 walruses showed that the tongue consistently contained approximately 2-6 times more larvae than the pectoral and intercostal muscles. Comparison of numbers of larvae in the root, body, and apex of the tongue from 3 walruses failed to identify a predilection site within the tongue, but the apex was considered an optimal tissue because of the high larval density within the tongue and the ease of collection. All 31 spiked samples weighing 50 g each and containing between 0.1 and 0.4 larvae per gram (lpg) were correctly identified as infected, indicating that the sensitivity of this procedure is adequate for diagnostic use. A sample size of 10 g consistently detected larvae in 2 walrus tongues containing > or = 0.3 lpg (n = 40), and until additional data are available, sample sizes from individual walrus tongues should be a minimum of 10 g. This study provides the preliminary data that were used for the development of a food safety analytical protocol for the detection of Trichinella in walrus meat in arctic communities.

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)0.002
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.023
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 head0.049
GPT teacher head0.357
Teacher spread0.309 · 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.

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

Citations16
Published2003
Admission routes1
Has abstractyes

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