Mastitis Detection of Online Quarter-Milk Conductivity for Dairy Cows by Using the Artificial Neural Network
Bibliographic record
Abstract
By using an online electrical conductivity (EC) measurement system for mastitis inspection of dairy cow, the variance of quarter-milk conductivity (QMC), ECR ratio among the quarter-milk conductivity and milk temperature can be measured during the milking. The somatic cell counts (SCC) of the foremilk from each quarter of dairy cows were measured at the day before and after of the field tests as the criterion to identify the healthiness of the udder. Therefore, it can be classified whether a quarter of dairy cow infected with mastitis or not according to the QMC and ECR indices which were used a back-propagation artificial neural network (ANN). All data of QMC and ECR were acquired from the field test by the online EC measurement system for mastitis would be classified three different ratios of healthy quarters to mastitis quarters (H/M ratio) in the training data sets, and be classified four different H/M quarter ratios in the testing data sets which would be executed to valid. The analysis results showed that lower H/M quarter ratio in the training data set had a better predictive probability of the ANN for mastitis quarters, and higher H/M ratio in the training data had a better predictive probability of the ANN for healthy quarters. In addition, as the H/M quarter ratio in the testing data increased, the predictive probability of true-positive response, P(PTP), decreased significantly, while the predictive probability of true-negative response, P(PTN), increased significantly. However, the probability of total correct response, P(TCR), of the ANN to identify the mastitis quarters was 88.2%. It is feasible to identify the mastitis cows according to online QMC and ECR of dairy cows using a back-propagation ANN.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".