Technical note: Validation of data loggers for recording lying behavior in dairy goats
Bibliographic record
Abstract
Changes in standing and lying behavior are frequently used in farm animals as indictors of comfort and health. In dairy goats, these behaviors have primarily been measured using labor-intensive video and live observation methodologies. The aim of this study was to validate accelerometer-based data loggers for use in goats. Two commercial dairy goat farms in Ontario were enrolled; goats were fitted with data loggers on their rear left legs and the pens were equipped with video. Data loggers compared well with video in identifying lying and standing events on both farms (farm 1 and 2, respectively: sensitivity=99.7 and 99.8%, specificity=99.5 and 99.4%, false readings=0.43 and 0.36%). The loggers were also able to record if the goat was lying on her left or right side (farm 1 only: sensitivity=99.9%, specificity=99.3%, false readings=0.38%), but these measures were only accurate if the loggers were attached with sufficient tension to prevent logger rotation. The mature does enrolled on farm 1 spent 14.5±1.0h/d lying down and frequently changed lying side even within a single lying bout (24±5 shifts/d between left and right sides and 16±5 lying bouts/d). The young goats on the second farm averaged just 8.5±3.2h/d in lying time, and spread this time over 8±4 bouts/d. Data loggers accurately measured lying time and lying bouts in mature does and younger goats on both farms, and lying laterality (e.g., left and right lying sides) in mature does on farm 1.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".