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Record W2139542370 · doi:10.1645/ge-974r.1

Comparison of a Manual and an Automated Method to Estimate the Number of Uterine Eggs in Anisakid Nematodes: To Coulter or Not to Coulter. Is That the Question?

2007· article· en· W2139542370 on OpenAlexaff
M. Victoria Herreras, Francisco E. Montero, David J. Marcogliese, Juan Antonio Raga, Juan Antonio Balbuena

Bibliographic record

VenueJournal of Parasitology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsBiologyNematodeCoulter counterAnisakis simplexHelminthsZoologyEcologyLarva

Abstract

Studies reporting numbers of eggs in vagina and utero in nematodes often give little information of the technique used for the estimations. This situation hampers comparison among studies, because, so far, differences in estimations provided by different techniques have not been assessed. This note examines whether a manual method based on visual counts in aliquots and an automated method using a Coulter counter yield equivalent estimations of egg numbers in vagina and utero of 3 anisakid nematode species (Anisakis simplex, Pseudoterranova decipiens, and Contracaecum osculatum). The number of eggs from 50 females per nematode species was estimated using both techniques. The automated and manual methods yielded similar egg counts (correlation coefficients >0.9 in the 3 species), but the methods were not always statistically equivalent. The automated method was more precise and seemed less dependent on egg density, whereas the manual method was less time-consuming (contrary to previous perceptions) and less expensive. Despite the higher precision of automated counts, the manual technique seemed to produce similar estimates; thus, it may be particularly useful in developing countries where nematode parasitism is prevalent in humans and domestic animals, but scientific resources are limited.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

1 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: medium

Comparison of two laboratory techniques for counting nematode eggs; this is assay/measurement validation within parasitology, the polysemy trap the rubric flags, not a study of research practice.

GPT-5.6 (high)T1
genre: empirical
about Canada: no
confidence: medium

The primary object is a comparison of two research measurement methods and their precision, although it is narrowly domain-specific.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Compares laboratory egg-counting techniques in parasitology; domain measurement methods, not research practice as object.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.597
Teacher spread0.557 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2007
Admission routes1
Has abstractyes

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