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Record W2121310709 · doi:10.1645/ge-928r.1

Cryptosporidium and Giardia in Marine-Foraging River Otters (Lontra canadensis) From the Puget Sound Georgia Basin Ecosystem

2007· article· en· W2121310709 on OpenAlexaff
Joseph K. Gaydos, Woutrina A. Miller, Kirsten Gilardi, Ann C. Melli, H. Schwant, Christian Engelstoft, Heather Fritz, P. A. Conrad

Bibliographic record

VenueJournal of Parasitology · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsMinistry of Environment
Fundersnot available
KeywordsCryptosporidiumGiardiaBiologyWildlifeOtterFecesEcologyZoologyMicrobiology

Abstract

fetched live from OpenAlex

Species of Cryptosporidium and Giardia can infect humans and wildlife and have the potential to be transmitted between these 2 groups; yet, very little is known about these protozoans in marine wildlife. Feces of river otters (Lontra canadensis), a common marine wildlife species in the Puget Sound Georgia Basin, were examined for species of Cryptosporidium and Giardia to determine their role in the epidemiology of these pathogens. Using ZnSO4 flotation and immunomagnetic separation, followed by direct immunofluorescent antibody detection (IMS/DFA), we identified Cryptosporidium sp. oocysts in 9 fecal samples from 6 locations and Giardia sp. cysts in 11 fecal samples from 7 locations. The putative risk factors of proximate human population and degree of anthropogenic shoreline modification were not associated with the detection of Cryptosporidium or Giardia spp. in river otter feces. Amplification of DNA from the IMS/DFA slide scrapings was successful for 1 sample containing > 500 Cryptosporidium sp. oocysts. Sequences from the Cryptosporidium 18S rRNA and the COWP loci were most similar to the ferret Cryptosporidium sp. genotype. River otters could serve as reservoirs for Cryptosporidium and Giardia species in marine ecosystems. More work is needed to better understand the zoonotic potential of the genotypes they carry as well as their implications for river otter health.

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)0.001
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 score0.011
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.253
Teacher spread0.246 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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