Somatic cell count assessment at the quarter or cow milking level
Bibliographic record
Abstract
The aim was to investigate whether on-line somatic cell count (SCC) assessment, when combined with electrical conductivity (EC), should be implemented at the udder quarter or at the cow level. Data were collected from 3 farms with automatic milking systems, resulting in 3,191 quarter milkings used in the analyses. Visual observations of foremilk and quarter milk samples for laboratory SCC analysis were used to define 2 gold standards. One was based on visual observation only and the other was based on a combination of visual observation and SCC (using a reference value of 500,000 cells/mL), which means that a quarter milking must have visually abnormal milk as well as an increased SCC to be categorized positive. On-line SCC assessment took place at the quarter level during the first part of the milking. Composite cow level samples were used for laboratory SCC analysis and to compare the performance of SCC assessment at quarter and cow levels. The EC at the quarter level was measured by in-line sensors of the automatic milking system. Alerts for SCC indicators were calculated based on straightforward reference values. Alerts for EC were based on straightforward reference values, or on interquarter ratios. The latter was calculated by dividing the value of a given quarter by the average value of the 2 lowest quarters of that milking. The EC and SCC indicators were combined with either a Boolean "and" or "or" function. Receiver operating characteristic curves were used to visually present results using different threshold values. Sensitivity, specificity, and success rate at the quarter level and false alert rate per 1,000 cow milkings were used to compare indicators at given sensitivity or specificity levels. Quarter level SCC assessment was superior to cow level assessment (transformed partial area under the curve=0.70 vs. 0.62) when combined with EC measurement at quarter level. When aiming for the same sensitivity level (e.g., 50%) with all visual abnormal milk as the gold standard, more false alerts were generated with cow level assessment (137 per 1,000 cow milkings) compared with quarter level SCC assessment (75 per 1,000 cow milkings). As a comparison, using EC alone resulted in 292 false alerts per 1,000 cow milkings in the same situation. Therefore, it is concluded that quarter level SCC assessment was superior to cow level assessment when combined with EC measurement at quarter level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".