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Record W3080967175 · doi:10.3390/pathogens9090706

Concentrations of Acute-Phase Proteins in Milk from Cows with Clinical Mastitis Caused by Different Pathogens

2020· article· en· W3080967175 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

VenuePathogens · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of British Columbia
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMastitisAcute-phase proteinMicrobiologyBiologyVeterinary medicineMedicineImmunology

Abstract

fetched live from OpenAlex

Among the new diagnostic methods for mastitis detection under development, milk acute-phase proteins (APPs) are receiving special attention. The study aimed to compare the profile of milk APPs from cows with natural clinical mastitis caused by distinct pathogens. The concentrations of haptoglobin (Hp), serum amyloid A (SAA), alpha-1-acid glycoprotein (AGP), and C-reactive protein (CRP) were measured by Spatial Proximity Analyte Reagent Capture Luminescence (SPARCL). Each APP was compared across the pathogens causing mastitis. The APPs differed statistically (p < 0.05) among the pathogens causing udder infection. There were significant and positive correlations among the concentration profile, for each pathogen, in three of four APPs studied. It can be concluded that the pathogen causing mastitis could modify the profile of release of the APPs in milk. The profile of Hp, AGP, and CRP demonstrated significant correlation, indicating that the three APPs are suggested as biomarkers, in milk, for bovine mastitis.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
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)0.0010.000

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 head0.051
GPT teacher head0.284
Teacher spread0.233 · 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