Bibliographic record
Abstract
Lameness is a major problem in present dairy farming. In order to find whether there are other ways to detect lameness in dairy cows instead of gait scoring, the use of HOBO’s, a brand of accelerometers, as a lameness detection method is investigated. The aim of the project is to determine whether HOBO’s can be used as a more specific and sensitive test method for lameness detection in dairy cows. \nIt is thought that lame cows can be detected based on their lying times, because literature describes that lame cows, compared to sound cows, are more likely to lie down for longer periods of time. This is thought to be due to claw lesions or other problems inducing lameness causing pain when weight is placed on the hoofs during rising and lying down in the stalls. For the same reason it is thought that lame cows have a lower number of lying bouts and the duration of lying bouts are longer. \nThe research was part of a running project named “the lameness and longevity project” and took place in Alberta, Canada. Seventeen farms were visited in this province and beside lying times and lying bout information collected using HOBO’s, gait scores were performed using video images made on the farms. This data was used to test the hypotheses. \nThe analysis showed that cows lying down between 8 and 14 hours a day are not necessarily sound and cows lying down less than 8 hours a day are not necessarily lame cows. Cows lying down over 14 hours a day are more likely to be lame and should be watched closely. Also, corrected for farm, the number of bouts was not lower for lame cows and duration of bouts was not longer for lame cows, as was expected.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".