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Record W2138249050 · doi:10.3168/jds.2007-0001

Somatic Cell Count During and Between Milkings

2007· article· en· W2138249050 on OpenAlexaffabout
R.G.M. Olde Riekerink, Herman W. Barkema, W. Veenstra, Filip Berg, Henrik Stryhn, Ruth N. Zadoks

Bibliographic record

VenueJournal of Dairy Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of CalgaryUniversity of Prince Edward Island
Fundersnot available
KeywordsMilkingSomatic cell countUdderAnimal scienceHerdMastitisMedicineVeterinary medicineLactationBiologyIce calvingPathologyPregnancy

Abstract

fetched live from OpenAlex

The objectives of the study were to determine 1) how sampling time between milkings affects the sensitivity and specificity of somatic cell count (SCC) as an indicator for intramammary infection (IMI) status, and 2) which cells are responsible for the diurnal variation in SCC. Six Prince Edward Island, Canada, dairy herds were selected. Quarter samples for SCC were collected immediately before the a.m. milking (pre-a.m.), halfway through the a.m. milking, immediately after the a.m. milking, every 60 min after detachment of the milking unit, and immediately before the p.m. milking (pre-p.m.). Compared with the geometric mean SCC at the pre-a.m. milking, SCC of quarters with no IMI between milkings was higher up to 7 h after milking. The pre-p.m. SCC was significantly lower than the pre-a.m. SCC in quarters with no IMI. Specificity of SCC at a cutoff of 200,000 or 500,000 cells/mL as an indicator for IMI status declined substantially after the a.m. milking. In quarters with elevated SCC, the proportion of polymorphonuclear leukocytes was larger immediately after milking. For accurate interpretations of SCC tests--whether by a laboratory, portable SCC device, or the California Mastitis Test--veterinarians, researchers, and udder health advisors should take milk samples immediately before milking.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.021
GPT teacher head0.247
Teacher spread0.226 · 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 teacher head, 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

Citations65
Published2007
Admission routes2
Has abstractyes

Explore more

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