187 Utilizing automation to improve efficiency, health, and production in the dairy industry
Bibliographic record
Abstract
Abstract The dairy industry has an increasing availability of equipment that is readily available for the automation of management tasks, including milking and feeding, as well as the monitoring of dairy cow behavior. Such automation not only has the ability to improve production and time efficiency on farm, but also increases our ability to monitor individual cows. Rapid adoption of automated (robotic) milking systems (AMS) is a good example of such benefits. Producers who have adopted this technology suggest they gain more time flexibility, find work to be less stressful and physically demanding, and report improved quality of their own life as well as that of their cows. At the same time, adoption of AMS may be associated with improvements in cow health and productivity. Similar findings apply for the adoption of automated milk feeders for dairy calves. The adoption of automated milk feeders has been done in effort to provide higher milk allowances, and thus raise healthier and better growing calves, reduce labor, and improve working conditions on farm. While less studied, automation in feeding of lactating cows also holds much promise for improving feeding accuracy and precision; thus not only reducing labor needs, but also having positive effects on cow production and efficiency. Finally, there is also rapid adoption of automated technologies for individual behavioral monitoring of dairy cows. These technologies have widely been adopted for estrus detection. In addition, they are also useful for detection of health disorders, both in occurrence and in advance of clinical symptoms. This, in turn, allows producers to identify and implement prevention and treatment protocols at earlier time points. It is anticipated that in the future such behavioral monitoring will play a larger role in terms of informing management decisions on farm.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".