Short communication: Influence of sampling interval on the accuracy of predicting bulk milk somatic cell count
Bibliographic record
Abstract
Although bulk milk somatic cell count (BMSCC) is, in most instances, not a good proxy for actual average herd somatic cell count (SCC), BMSCC is the only SCC parameter available to monitor trends in udder health for a large number of farms worldwide. The frequency of sampling BMSCC varies considerably between countries, and it is unknown to what extent the sampling interval of BMSCC or variation in BMSCC data itself influences the accuracy. The aim of this study was to assess the effect of sampling interval and variation of the BMSCC data on the accuracy to predict BMSCC. Because BMSCC is measured at regular time intervals, an artificial neural network (ANN) was used to determine both the effect of sampling interval and variation of the BMSCC data. The intervals examined in this study ranged from 4 to 14 d and were compared with the baseline of a standard 2-d sampling interval. The BMSCC data were collected every other day for a 24-mo period on 949 farms, and all series were created by exclusion of BMSCC data in between the original 2-d sampling interval series. The effect of variation of BMSCC was determined by comparing the error of the ANN model in 2 subsets of farms, those with the lowest SD (n=239) and those with a high SD of BMSCC data (n=236). No significant differences were found in any of the sampling intervals between the 2 cohorts of low and high SD in BMSCC. Overall, compared with the 2-d sampling interval, on average the error of the ANN model was 32,600 cells/mL for all farms included, ranging from 15,000 cells/mL (4-d interval) to 41,000 cells/mL (14-d sampling interval). Therefore, the length of the sampling interval greatly influences the usefulness of BMSCC data to monitor trends in udder health at the herd 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".