A model to optimise the requirements of lactating dairy cows for physically effective neutral detergent fibre
Bibliographic record
Abstract
This study modelled multiple physiological responses of dairy cows to physical and chemical characteristics of a diet aiming to optimise their requirements for physically effective neutral detergent fibre, expressed inclusive of particles-dry matter > 8 mm (peNDF > 8). Extensive research data, comprising a wide range of feeding conditions (n = 64 studies and 257 different dietary treatments), were used to parameterise the model, while statistical modelling was used to account for the inter- and intra-experiment variation as well as to derive the model estimates. Physiological thresholds and 'safety limits' of peNDF > 8 for maintaining different physiological variables were derived using non-linear statistical modelling. Results showed that peNDF > 8 content in the diet is a key factor stimulating rumination activity, maintaining optimal ruminal pH and promoting fibre digestion. Modelling data with regard to the association of fibre digestion and time duration of ruminal pH < 5.8 and dietary peNDF > 8 suggests that feeding of less than 13.7% peNDF > 8 (the lower 'safety limit') is critical to prevent depression of fibre digestion in dairy cows. The study also indicated that the beneficial effects of peNDF > 8 on ruminal pH and fibre digestion can be at the expense of the dry matter intake (DMI) level of high-producing cows when the peNDF > 8 threshold of 14.9% in the diet is exceeded. In terms of the optimisation of peNDF > 8 requirements, the modelling data suggest that feeding of 17-18.5% peNDF > 8 can be beneficial in maintaining ruminal pH, while allowing a relatively high DMI (22.3-22.7 kg x d(-1)) for average high-producing dairy cows.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".