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Record W2131861991 · doi:10.1017/s1466252310000204

Transmission of swine pathogens: different means, different needs

2011· review· en· W2131861991 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnimal Health Research Reviews · 2011
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsBoehringer Ingelheim (Canada)
Fundersnot available
KeywordsTransmission (telecommunications)Mycoplasma hyopneumoniaeBiosecurityBiologyPasteurella multocidaEpidemiologySarcoptes scabieiVirologyVeterinary medicineMiteMedicinePathologyEcologyComputer science

Abstract

fetched live from OpenAlex

There seems to be two main types of pathogens that cause diseases in swine: those that are mainly introduced through direct pig contacts, and those that are often, and in some situations mainly introduced by indirect transmission means. In this review, the mange mite (Sarcoptes scabiei), toxigenic Pasteurella multocida and Brachyspira hyodysenteriae will be used as examples of the first type, and foot and mouth disease virus, Mycoplasma hyopneumoniae and porcine reproductive and respiratory syndrome (PRRS) virus as examples of the second. It is now clear from various epidemiological studies as well as experimental and field data that aerosol transmission of some swine pathogens plays an important role in their epidemiology. As previous biosecurity programs did not take this factor into consideration, it can at least partially explain why many of these programs suffered frequent failures and why air filtration is now becoming increasingly popular in North America. Identifying and quantifying transmission means should be a priority for every important infectious disease for which it has not been done.

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.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.560
GPT teacher head0.473
Teacher spread0.086 · 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