Automatic and frequency-programmable systems for feeding tmr: state of the art and available technologies
Bibliographic record
Abstract
Feeding Total Mixed Rations (TMR) or Partial Mixed Ration (PMR) has become a common practice for dairy cows as a result of the benefits for the animals and the labour savings for farmers.Characteristic for this feeding system are the -trailed or self propelled -man-operated mechanical mixers.Besides the advantages of the TMR technique, it has the same drawback as most traditional ad libitum feeding systems that the discharge of feed is limited to once, maximum twice a day.During the last 3-5 years, technologies for automatically feeding cows with TMR or PMR have grown in popularity.More than 15 manufacturers are working worldwide on different designs for automatic TMR/PMR feeding systems (AFS) while an estimated 300-400 farms have adopted this technology, mostly located in Northern Europe, Canada and Japan.The different manufacturers offer a wide range of technical solutions.Some of the most important aspects that characterize these systems include the possibility of a variable frequency drive to modulate the ration, to control the feeding times, to stimulate the cow activity and to manage the composition of the total daily ration with the objective to control the feed intake.Management possibilities and work quality seem to be strongly affected by available technical solutions.This paper provides a proposal for the classification of different AFS's and suggestions for future research on feeding strategies; it also focuses on daily feeding frequency and the time intervals between distributions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".