Technical note: Evaluation of a scoring system for rumen fill in dairy cows
Bibliographic record
Abstract
Changes in feed intake are useful in early detection of disease in dairy cows. Cost and complexity limit our ability to monitor dry matter intake (DMI) of individual cows kept in loose-housing systems. A 5-point subjective scoring system has been developed to visually describe rumen fill, but no work to date has evaluated these scores as an indicator of feed intake. The objective of this study was to evaluate the performance of within-cow changes in visual rumen fill scores as estimates of changes of DMI and feed intake in dairy cows. Our results illustrate that rumen fill scored on a scale from 1 to 5 has substantial intra- (Cohen's kappa coefficient=0.69) and interobserver (Cohen's kappa coefficient=0.68) repeatability. Within-cow changes in visual rumen fill score are correlated with changes in DMI (Spearman's rank correlation=0.68). The depth of the paralumbar fossa (mean +/- SD; 5.6+/-0.9 cm) changes considerably (up to 4.8 cm) within 70+/-5 min. This more objective measure was also correlated with visual rumen fill scores (Spearman's rank correlation=-0.62). Our results indicate that subjective rumen fill scores are statistically associated with both an objective measure of paralumbar fossa indentation and feed intake. However, much of the variation in visual rumen fill scores is not associated with either measure, suggesting that caution is required in clinical usage of these scores.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 0.004 |
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".