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Record W3005495007 · doi:10.1016/j.ijppaw.2020.02.002

Not playing by the rules: Unusual patterns in the epidemiology of parasites in a natural population of feral horses (Equus caballus) on Sable Island, Canada

2020· article· en· W3005495007 on OpenAlexafffundabout
Emily Jenkins, Amber-Lynn Backwell, Jennifer L. Bellaw, Julie Colpitts, Alice Liboiron, David McRuer, Sarah A. Medill, Sarah Parker, Todd Shury, Martha A. Smith, Christina Tschritter, Brent Wagner, Jocelyn Poissant, Philip D. McLoughlin

Bibliographic record

VenueInternational Journal for Parasitology Parasites and Wildlife · 2020
Typearticle
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsGovernment of CanadaUniversity of CalgaryParks CanadaUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaWestern College of Veterinary Medicine, University of Saskatchewan
KeywordsFecesVeterinary medicineBiologyHorseEquusDewormingPopulationEggs per gramHelminthsZoologyEcologyMedicine

Abstract

fetched live from OpenAlex

), provides insight into unusual patterns of parasite epidemiology in a nutrition-limited environment, and has conservation and biosecurity implications for this unique equine population, as well as for parasite management in domestic horses.

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 score0.021
Threshold uncertainty score0.942

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 integrity0.0000.000
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 head0.052
GPT teacher head0.374
Teacher spread0.322 · 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

Citations22
Published2020
Admission routes3
Has abstractyes

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