Short communication: Accuracy of estimation of lameness, injury, and cleanliness prevalence by dairy farmers and veterinarians
Bibliographic record
Abstract
Lameness, injuries, and cleanliness are considered important indicators of dairy cow welfare, milk production, and milk quality. Previous research has identified that farmers globally underestimate the prevalence of these cow-based measurements, but no information on the perceptions of veterinarians is available. Because veterinarians are often perceived as the main providers of health advice on farms, the objective of the present study was to evaluate the relationship between the true prevalence of lameness, injury (hock, knee, neck), and cleanliness (udder, legs, flanks), and the estimated prevalence of these issues by farmers and their herd veterinarians. A cross-sectional study was conducted between February 2016 and July 2017. First, the farm owner and the herd veterinarian were asked to estimate the prevalence of lameness, of neck, knee and hock injuries, and of udder, leg, and flank cleanliness on the farm. The research team then visited the farm and scored all lactating cows in the herd for each measurement. Linear regression models were used to assess the relationship between the prevalence estimated by the veterinarians and the farmers, of each cow-based measurement, and the true prevalence on the farm. The 93 herds enrolled had a median of 55 milking cows and were housed in tiestall (90%) and freestall (10%) barns. Ten herd veterinarians participated and were involved with 2 to 22 enrolled farms each. A wide variation was detected in the true prevalence of the different cow-based measurements among herds (lameness: range = 19-72%, median = 36%; neck injuries: range = 0-65%, median = 14%; knee injuries: range = 0-44%, median = 12%; hock injuries: range = 0-57%, median = 25%; dirty udder: range = 0-55%, median 13%; dirty legs: range = 0-91%, median = 18%; and dirty flanks: range = 0-82%, median = 20%). For both veterinarians and farmers, the perception of each cow-based measurement prevalence increased incrementally as the herd's true prevalence increased. Overall, farmers and veterinarians underestimated cow-based measurements. Farmers and veterinarians more accurately estimated lameness prevalence in herds with higher prevalence than in herds with low prevalence, suggesting a better awareness of the issue on farms with lameness problems. Injuries were less accurately estimated in herds with higher injury prevalence compared with herds with lower prevalence, suggesting an opportunity for better knowledge transfer in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.138 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".