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Record W2762815108 · doi:10.1080/22423982.2017.1380994

Tracking sources of<i>Clostridium botulinum</i>type E contamination in seal meat

2017· article· en· W2762815108 on OpenAlexaffabout
Daniel Leclair, Jeffrey M. Farber, Franco Pagotto, Sandy Suppa, Bill Doidge, John W. Austin

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2017
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsMakivik CorporationHealth Canada
Fundersnot available
KeywordsBotulismContaminationClostridium botulinumFur sealFood scienceSeal (emblem)Food contaminantVeterinary medicineMedicineBiologyMicrobiologyGeographyFisheryToxinEcology

Abstract

fetched live from OpenAlex

Botulism in Nunavik, Quebec is associated with the consumption of aged marine mammal meat and fat. The objective was to identify meat handling practices presenting a risk of contamination of seal meat with C. botulinum. Potential sources of contamination were assessed through interviews with igunaq producers from five communities of Nunavik. These sources were verified by detection and isolation of C. botulinum from igunaq prepared in the field from seal carcasses. Interviews indicated practices presenting a risk for contamination included: placing meat or fat on coastal rocks, using seawater for rinsing, and ageing meat in inverted seal skin pouches. Although the presence of C. botulinum type E spores was detected in only two of 32 (6.3%) meat or fat samples collected during the butchering process, two of four igunaq preparations from these samples contained type E botulinum toxin. Analysis of C. botulinum type E isolates recovered from these preparations indicated that shoreline soil may be a source of contamination. Seal meat and fat may be contaminated with C. botulinum type E during the butchering process. Measures can be adopted to reduce the risks of contamination in the field and possibly decrease the incidence of type E botulism in Nunavik.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.040
GPT teacher head0.358
Teacher spread0.318 · 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 designObservational
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

Citations13
Published2017
Admission routes2
Has abstractyes

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