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Record W2010425204 · doi:10.1139/z03-118

Spatiotemporal heterogeneity in recruitment of larval parasites to shore crab intermediate hosts: the influence of shorebird definitive hosts

2003· article· en· W2010425204 on OpenAlexvenueno aff
A. David M. Latham, Robert Poulin

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
FundersMarsden FundUniversity of Otago
KeywordsBiologyEcologyParasitismPopulationSpatial heterogeneityAbundance (ecology)Host (biology)Zoology

Abstract

fetched live from OpenAlex

Parasitism is a major biotic determinant of animal population dynamics and community structure. Temporal and spatial heterogeneity in parasitism is commonly observed in intermediate host populations. Understanding the causes of temporal and spatial variation in the recruitment of parasites is crucial if we are to manage host populations and animal communities effectively. Here, the temporal and spatial dynamics of Profilicollis antarcticus and Profilicollis novaezelandensis (Acanthocephala) infections in three species of shore crabs (Macrophthalmus hirtipes, Hemigrapsus edwardsii, and Hemigrapsus crenulatus) are examined in relation to the distribution and abundance of shorebird definitive hosts. Temporal patterns of infection were observed in M. hirtipes but not the other two species. Spatial heterogeneity in recruitment of acanthocephalan larvae to M. hirtipes and H. edwardsii populations was found both within and between locations. Weak evidence is found that infection levels in crab populations are related to the distribution and abundance of shorebird hosts both temporally and spatially. In this system, abiotic factors seem to be at least as important in determining how infection levels vary in time and space as the input of parasite eggs from bird definitive hosts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.804

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)0.0000.000

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 head0.033
GPT teacher head0.319
Teacher spread0.286 · 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

Citations38
Published2003
Admission routes1
Has abstractyes

Explore more

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