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Record W6929655519 · doi:10.5061/dryad.mkkwh70zp

Long-term change in the parasite burden of shore crabs (Hemigrapsus oregonensis and H. nudus) on the northwestern Pacific coast of North America

2021· dataset· en· W6929655519 on OpenAlexaffabout

Bibliographic record

VenueDRYAD · 2021
Typedataset
Languageen
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAbundance (ecology)Intertidal zoneParasite hostingShoreLarvaSnail

Abstract

fetched live from OpenAlex

The abundances of free-living species have changed dramatically in recent decades, but little is known about change in the abundance of parasitic species. We investigated whether populations of several parasites have shifted over time in two shore crab hosts, Hemigrapsus oregonensis and H. nudus, by comparing the prevalence and abundance of three parasite taxa in a historical dataset (1969–1970) to contemporary parasite abundance (2018–2020) for hosts collected from 11 intertidal sites located from Oregon, USA to British Columbia, Canada. Our data suggest that the abundance of the parasitic isopod Portunion conformis has varied around a stable mean for the past fifty years. No change over time was observed for larval acanthocephalans. However, larval microphallid trematodes increased in prevalence over time among H. oregonensis hosts, from a mean of 4.7% to 61.8% between the historical and contemporary time points. The substantial increase in the prevalence of larval microphallid trematodes could be due to increased abundances of their bird final hosts, increased production of parasite infective stages by snail intermediate hosts, or both. Our study highlights the variability among parasite species in their temporal trajectories of change.

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 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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.149
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.288
Teacher spread0.257 · 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
GenreDataset

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

Citations1
Published2021
Admission routes2
Has abstractyes

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