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Record W4413100293

Clinical and diagnostic comparisons of bovine foot rot and bovine digital dermatitis lesions and management differences in feedlot cattle in Alberta.

2025· article· en· W4413100293 on OpenAlexaboutno aff
Susan Pyakurel, Lithira Amarajeewa, Cameron G. Knight, Angélica Dias, Karin Orsel

Bibliographic record

VenuePubMed · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsnot available
Fundersnot available
KeywordsFeedlotFoot rotVeterinary medicineFoot (prosody)Skin lesionAnimal scienceCattle DiseasesBiologyDermatologyMedicineArt
DOInot available

Abstract

fetched live from OpenAlex

Background: Bovine foot rot (BFR) and bovine digital dermatitis (BDD) are infectious foot lesions with overlapping clinical features that complicate diagnosis. Objective: surveys. Animals and procedure: Lame cattle were assessed and bacteria in punch biopsy samples (PB-samples), swabs, and subcutaneous samples (SC-samples) were quantified using quantitative real-time PCR. In addition, PB-samples were used for hematoxylin and eosin and Warthin-Faulkner staining. Feedlot records and producer surveys captured risk factors and producers' opinions on management practices. Results: BDD. Conclusion and clinical relevance: spp. population differences in dermis and SC-samples could indicate distinct, species-level roles in BFR pathogenesis. In addition, risk factors such as weight and lameness scores could not distinguish between BFR and BDD.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.850

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.233
Teacher spread0.208 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicMycotoxins in Agriculture and Food→French-language works237,207→