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Record W2312780528 · doi:10.1017/s0031182012002132

Benefits of fidelity: does host specialization impact nematode parasite life history and fecundity?

2013· article· en· W2312780528 on OpenAlexaff
Janet Koprivnikar, H. S. Randhawa

Bibliographic record

VenueParasitology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsBrandon University
Fundersnot available
KeywordsBiologyFecundityHost (biology)NematodeParasite hostingLife history theoryLife historyEcologyFidelityZoologyDemographyPopulation

Abstract

fetched live from OpenAlex

The range of hosts used by a parasite is influenced by macro-evolutionary processes (host switching, host-parasite co-evolution), as well as 'encounter filters' and 'compatibility filters' at the micro-evolutionary level driven by host/parasite ecology and physiology. Host specialization is hypothesized to result in trade-offs with aspects of parasite life history (e.g. reproductive output), but these have not been well studied. We used previously published data to create models examining general relationships among host specificity and important aspects of life history and reproduction for nematodes parasitizing animals. Our results indicate no general trade-off between host specificity and the average pre-patent period (time to first reproduction), female size, egg size, or fecundity of these nematodes. However, female size was positively related to egg size, fecundity, and pre-patent period. Host compatibility may thus not be the primary determinant of specificity in these parasitic nematodes if there are few apparent trade-offs with reproduction, but rather, the encounter opportunities for new host species at the micro-evolutionary level, and other processes at the macro-evolutionary level (i.e. phylogeny). Because host specificity is recognized as a key factor determining the spread of parasitic diseases understanding factors limiting host use are essential to predict future changes in parasite range and occurrence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0080.001

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 head0.017
GPT teacher head0.317
Teacher spread0.300 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2013
Admission routes1
Has abstractyes

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