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Record W4321510086 · doi:10.21423/aabppro20153523

Bovine thoracic ultrasonography

2015· article· en· W4321510086 on OpenAlexaff
Ryan D. Rademacher

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsMedicineUltrasonographyBovine respiratory diseaseIntensive care medicineRadiologySurgeryImmunology

Abstract

fetched live from OpenAlex

Bovine respiratory disease (BRD) continues to be the major animal health concern facing the North American cattle feeding industry. Despite improvements in technologies and the development of new antimicrobials, morbidity and mortality rates have remained flat or even increased. Thoracic ultrasonography (TUS) is a technology that has shown promise as a chute-side diagnostic tool for BRD. Degree of lung consolidation, as determined by TUS, has been negatively correlated to clinical outcome in cattle pulled for signs attributable to BRD and not treated with antimicrobials (negative controls). Thoracic ultrasonography is relatively simple to perform, and many of the available ultrasound machines and probes used for bovine reproductive ultrasonography can also be used to examine the lungs and pleura. While the procedures and techniques for large-scale use in a production setting remain to be validated, practitioners may currently be able to use the technology to add accuracy and value to their recommendations for case management of individual animals.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.005

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 head0.304
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2015
Admission routes1
Has abstractyes

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