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Morphological and morphometric differentiation of dorsal-spined first stage larvae of lungworms (Nematoda: Protostrongylidae) infecting muskoxen (Ovibos moschatus) in the central Canadian Arctic

2015· article· en· W609672917 on OpenAlexafffundabout
Pratap Kafle, Manigandan Lejeune, Guilherme G. Verocai, Eric P. Hoberg, Susan Kutz

Bibliographic record

VenueInternational Journal for Parasitology Parasites and Wildlife · 2015
Typearticle
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsCanadian Science Centre for Human and Animal HealthUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesGovernment of Nunavut
KeywordsBiologyZoologyArcticParasite hostingLarvaMorphometricsEcology

Abstract

fetched live from OpenAlex

Umingmakstrongylus pallikuukensis and Varestrongylus eleguneniensis are the two most common protostrongylid nematodes infecting muskoxen in the North American Arctic and Subarctic. First stage larvae (L1) of these lungworms have considerable morphological similarity that makes their differential diagnosis very difficult. Using light microscopy, we studied in detail the L1 of these two species and identified the key differences in morphological and morphometric attributes. Thirty L1 of each species from naturally infected muskox were heat-killed and then assessed for morphological and morphometric features that could be used for species-level differentiation. Key differentiating features include: length and morphology of the tail extension, curvature of the body, ventral post-anal transverse cuticular striations, and total body length. A laboratory guide for differentiation of L1 based on these species-specific characters was prepared and used by an experienced observer to identify an additional 35 L1 extracted from a different set of fecal samples from free-ranging muskoxen with mixed infections. The identities of these L1 were confirmed by sequence analysis of the ITS-2 region of the nuclear ribosomal DNA. Accuracy of morphological identification was 100 percent, reflecting the reliability of the proposed guide for differentiation. Using the guide, three minimally trained lab assistants each fixed and accurately identified 10 of 10 randomly selected L1. Ability to morphologically differentiate these facilitates the monitoring of overlapping range expansion of both parasites in the Canadian Arctic. Studies enabling species-level parasite identification are also critical for defining biodiversity, detecting mixed infections, and understanding host-parasite interactions. Morphological identification is a simple, reliable and cost-effective alternative to labor and equipment intensive molecular methods and can easily be performed in low resource settings.

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 score0.006
Threshold uncertainty score0.994

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 head0.049
GPT teacher head0.354
Teacher spread0.305 · 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

Citations23
Published2015
Admission routes3
Has abstractyes

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