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Review of host pulmonary defenses with reference to cattle

2013· article· en· W3000220265 on OpenAlexaff
Heidi E. Banse, Amelia Wollums, D. L. Step

Bibliographic record

VenueThe Bovine Practitioner · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImmune systemImmunologyRespiratory tractPathogenBiologyInnate immune systemBovine respiratory diseasePhagocytosisDiseaseAcquired immune systemOpsoninRespiratory diseaseSecretionRespiratory tract infectionsRespiratory systemLungMedicine

Abstract

fetched live from OpenAlex

Bovine respiratory disease is a leading cause of morbidity and mortality for the cattle industry. Cattle can be protected from respiratory disease by both structural and dynamic defense mechanisms of the respiratory tract, which are critical for prevention of pathogen entry and colonization. Dynamic defenses include those mediated by the innate and adaptive branches of the immune response. The immune response to pathogens is complex, and requires immune cell migration, proliferation, and adaptation. Secretion of molecules, including cytokines, host defense peptides, and opsonins, by cells within the respiratory tract promotes respiratory defense, including pathogen neutralization and phagocytosis. Ongoing discoveries in the field of immunology should guide the development of novel immunotherapeutic interventions for the prevention and treatment of bovine respiratory disease.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.997

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.004

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 head0.024
GPT teacher head0.287
Teacher spread0.264 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2013
Admission routes1
Has abstractyes

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