Nonlinear Mixed Models Applied to Ruminal Degradability Studies
Bibliographic record
Abstract
This article presents an application of three classical models to studies of ruminal degradation kinetics, namely Ørskov and McDonald’s model (1979); Van Milgen, Murphy and Berger’s model (1991), and Richard’s model proposed in France, Dijkstra, and Dhanoa (1996). Our approach is focused on accounting for animal e ects given that measurements are repeated in the same animal. The models were studied under the perspective of nonlinear mixed-e ects (NLME) models. In this way, we intended to accommodate the problems of response variance heterogeneity and correlations between repeated measures. To apply the proposed method, we used data from an experiment conducted in a Latin square design to assess the dry matter degradability of the following three silages: Elephant grass (Pennisetum purpureum Schumach.) silage treated with bacterial inoculant, Elephant grass silage treated with enzyme-bacterial inoculant, and corn (Zea mays L.) silage. Samples were incubation for 0, 2, 6, 12 , 24, 48, 72 and 96 h. For these experimental data, the Van Milgen, Murphy, and Berger’s model showed better performance than the others. The proposed approach indicated that inclusion of animal e ects is important for obtaining more accurate information and can be considered in NLME modeling. Furthermore, it was also possible to perform an easy-to-interpret analysis of contrasts between treatments by using Tukey’s test.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".