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Supplementary Information from Long-term change in the parasite burden of shore crabs (<i>Hemigrapsus oregonensis</i> and <i>H. nudus</i>) on the northwestern Pacific coast of North America

2021· article· en· W6977268473 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAbundance (ecology)Parasite hostingIntertidal zoneShoreLarvaSnailTaxon

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 50 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 4.7–61.8% 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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.560
Threshold uncertainty score0.628

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5600.090

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 head0.026
GPT teacher head0.275
Teacher spread0.250 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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