190 ASAS-EAAP Exchange Speaker Talk: Beyond stainless steel and milk pumps: precision technology in the milking equipment world
Bibliographic record
Abstract
Abstract What do you think of when you hear the words: milking equipment? Do you think of the different brands? Boumatic, DeLaval, GEA or Lely? Do you think of parlours, parallels or rotaries? Do you think of teat cups, liners or rubber hosing? Do you think of teat dips and cleaners? If you think of mostly hardware – I can’t blame you. If I look at DeLaval, the company I work for, it has a 136-year history of offering dairy producers revolutionary innovations. In 1878, Gustaf de Laval patented the cream separator, which was the basis for forming our company in 1883. (That was even before Old MacDonald’s farm.) DeLaval has since made other significant improvements in animal health and welfare, milk production, food safety and labor efficiency with technologies like: the vacuum operated milking machine (1917); a commercial rotary (1930); animal identification systems (1978); variable speed vacuum pumps (1977); milking robots (late 1990s); an on-farm lab called Herd Navigator; Clover-shaped liners (2013); a teat spray robot (2015); a body condition score camera (2015); and our latest robot, the VMSTM V300 (2018). DeLaval has been pushing the Brave New World for many years. Today, we have a software program called DelProTM Farm Manager – a powerful data management tool providing valuable information and analysis. It enables the dairy producer to make efficient daily actions and for advisors to monitor and provide profitable and sustainable management actions. As animal scientists, what is your role in this? One avenue is to consider working with the data to provide relevant summaries on what the information is telling us. You can have a role in influencing management changes for dairy farms for the future. While not all farms have access to this data and information, your research projects can help dairy farmers wanting to make improvements to their operations. Producers need non-bias, third party sources they can reference. The dairy farming world is changing; we all need to keep up. Make sure the research that you decide upon fits into this rapid pace.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".