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Record W1984939912 · doi:10.4102/ojvr.v76i1.66

Canine babesiosis : tick-borne diseases

2009· article· en· W1984939912 on OpenAlexfundno aff
Johan P. Schoeman

Bibliographic record

VenueOnderstepoort Journal of Veterinary Research · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsnot available
FundersInstitute of Musculoskeletal Health and Arthritis
KeywordsBabesiosisSubclinical infectionBabesiaTick-borne diseaseMedicineDiseaseCATSBiologyTickVeterinary medicineVirologyImmunologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Babesiosis is a tick-borne disease affecting humans and many domestic and wild animals. Domestic animals showing appreciable morbidity and mortality include dogs, cats, cattle and horses. Both canine and feline babesiosis are diseases characterised by haemolytic anaemia, icterus and haemoglobinuria. Canine babesiosis can range from chronic or subclinical to peracute and fatal, depending on the virulence of the species and the susceptibility of the host. This paper deals with canine babesiosis with specific reference to pathogenesis, clinical findings, complications, diagnosis and treatment, as well as newly identified prognostic factors in Babesia rossi babesiosis.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.003

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 head0.079
GPT teacher head0.368
Teacher spread0.290 · 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
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

Citations98
Published2009
Admission routes1
Has abstractyes

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