The detection of Digital Dermatitis in the milking parlor with easy tools\nComparing milking parlor inspections with the gold standard of chute inspections
Bibliographic record
Abstract
This study assesses milking parlor inspections for detection of Digital Dermatitis (DD) with easy tools, by comparing it to chute inspections. Chute inspections are considered to be the gold standard in detecting DD1. The study was done in November 2013 and February/March 2014 on 9 Alberta dairy farms. In both periods, the 9 farms were visited for a milking parlor inspection and a chute inspection, resulting in 2 pairs of inspections per farm. Each pair consisted of a milking parlor inspection which was followed by a chute inspection after an average of two days. A total of 2833 cows had both hind feet inspected in both the milking parlor and the chute. During the milking parlor inspections, feet were cleaned with a medium pressurized hose, and followed by visual observation of the rear feet in the milking parlor with a small mirror and a headlamp (see picture 1 and 2). During chute inspections, feet were lifted one by one, cleaned with paper towel and then scored. Scoring of the lesions was done according to a six point scoring system (M0-M4.1) which was adapted by Berry et al2. from Döpfer et al. 19973. We calculated sensitivity (Se), specificity (Sp), kappa values and %agreement to compare the two observations.\nWe found a Se of 0.90 and Sp of 0.87 for the detection of DD in the milking parlor. The kappa value after comparing the two diagnostic tests was 0.76, which is considered to be satisfactory4. The Se, Sp and kappa value for correctly classifying was lower than for detecting lesions and indicated that classifying is more difficult than just detecting. \nThis easy detection method for DD is a cost and time friendly alternative for chute scoring, if the frequency of inspections is high as it is in certain research projects and in managing strategies in which the farmer wants to follow the DD statuses of cows.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".