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Record W2138700486 · doi:10.1645/ge-2317.1

Experimental Transmission of Hepatozoon clamatae (Apicomplexa: Adeleida) to the Wood Frog, Rana sylvatica, and to the Mosquito Culex pipiens

2010· article· en· W2138700486 on OpenAlexaff
Lisa M. Harkness, Ashley E. Drohan, Cory M. Dickson

Bibliographic record

VenueJournal of Parasitology · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsAcadia University
Fundersnot available
KeywordsBiologyHepatozoonCulex pipiensHost (biology)ZoologyParasite hostingRanaApicomplexaEcologyLarvaAnatomyMalariaImmunology

Abstract

fetched live from OpenAlex

Hepatozoon clamatae naturally infects the erythrocytes of green frogs (Rana clamitans), bullfrogs (Rana catesbeiana), and northern leopard frogs (Rana pipiens) in northeastern North America and uses the mosquito Culex territans as a definitive host. In this study, we show that the wood frog, Rana sylvatica, supports merogonic development, but not gamogonic development, of this protozoan parasite, and that the mosquito Culex pipiens serves as an experimental definitive host for sporogonic development. Two wood frogs were each force-fed Cx. territans, containing oocysts of H. clamatae in their Malpighian tubules, which had fed on blood of infected green frogs 30 days previously. Free merozoites were observed in 1 wood frog 35 days after inoculation, but intraerythrocytic gamonts were not observed. Fifteen Cx. pipiens were fed on a mixture of infected frog blood and physiological saline. Thirty days after blood feeding, 2 mosquitoes were infected with oocysts of H. clamatae, whereas the other 13 mosquitoes either were negative for infection or had died. The observed absence of gamogonic development of this parasite in wood frogs are discussed in light of previous records of host specificity of Hepatozoon species for their anuran hosts, and the importance of Cx. pipiens as an additional definitive host for H. clamatae.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.491

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 integrity0.0000.001
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 head0.012
GPT teacher head0.294
Teacher spread0.282 · 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 designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

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