The effect of bulk density on bite dimensions of cattle grazing microswards in the field
Bibliographic record
Abstract
The effect of sward bulk density on bite mass and bite dimensions was examined in two experiments using oesphageally fisulated cows to graze microswards under field conditions. In Experiment 1 microswards of uniform sward height were hand thinned to create differences in sward bulk density. In Experiment 2 plots were sown with monocultures of a range of ryegrass cultivars selected on the basis of their contrasting sward structures to provide a range of sward bulk densities and grazed down in two consecutive strata. Both methods were successful in providing a range of microswards of different sward bulk densities. In Experiment 1 bite mass increased significantly as sward bulk density increased. In Experiment 2 bites taken from the first stratum did not differ significantly in mass due to bites being deeper in swards with low bulk density, compensating for the lower bite bulk density. However, mass of bites taken mainly from the second stratum were lower in some swards due to lower bite bulk density which was related to tiller density rather than sward bulk density. The results emphasize the importance of sward bulk density in determining bite mass and dimensions, especially for the first stratum, and of tiller density in the bulk density of bites in the lower stratum in sward depletion, the latter requiring to be studied further.
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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 | 0.000 | 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".