THE EFFECT OF INDIVIDUAL COMPONENTS OF TOTAL MIXED RATION (TMR) ON PRECISION DOSING TO MIXER FEEDER WAGONS
Bibliographic record
Abstract
At present, in large-scale breeding of cattle occurs no longer feeding with one forage feeding system. The cattle are fed with feeding technique called total mixed ration (TMR). In TMR are all the feeds (bulky and grainy) and mineral and vitamin supplements mixed into a homogenous mixture. For the mixing of individual components of TMR are used mixer feeder wagons, that can be used not only for mixing of feeding ration, but also for discharging the fodder from wagon to fodder table in barn. Very important is the accuracy of dosing the individual components into the ration. The aim is to ascertain the precision during loading of individual components into the mixer feeder wagons. When loading, the dosing accuracy is influenced by many factors. Most important ones are used technique (loaders, hoppers, chopping devices, silage block cutters), human factor (expertise and responsibility of the operator), physical properties of the individual components (size, shape and density) and the loaded weight of components. On a cattle-breeding farms (600 pcs), was performed accuracy monitoring of loading selected individual components of TMR, common to several kinds of recipes, such as CCM (corn cob mix), haylage, silage and straw into mixer feeder wagons Storti Labrador 120 and Cernin C11. These mixer feeder wagons are equipped with electronic tensometric scales and responder for transfer of data to PC. From the PC software was, by the individual components, investigated programed weight (kg), actually loaded weight (kg) and deviations between programed and actually loaded weight (%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".