Lying behavior as an early predictor of ketosis in early lactation dairy goats
Bibliographic record
Abstract
Goats frequently have multiple foetuses, a known risk factor for negative energy balance prior to kidding (Brozos et al, Vet Clin North Am Food AnimPract, 2011). This state, coupled with increased energy demands of milk production, also increases the risk of ketosis after kidding. Ketosis is a serious metabolic condition that when left untreated can be fatal. Regardless of severity, ketosis has been shown to negatively affect milk production in dairy cows (Rajala-Schultz et al, J Dairy Sci, 1999). Unfortunately, in goats, this disease is typically only identified when does show clinical signs, and prognosis is poor. At subclinical levels, which frequently go undetected, reduced milk production likely leads to early culling. Clinical symptoms of ketosis include loss of appetite, ataxia, and general lack of mobility, including increased lying behavior (Andrews et al, Small Ruminant Res, 1996). In dairy cows, changes in lying behavior have been shown to be useful as early indicators of compromised health status (Weary et al, J Anim Sci, 2009). Therefore, the aim of this study was to examine whether lying behavior could be used as an early predictor of ketosis after kidding in dairy goats.
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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 | 0.001 | 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".