Effect of teatcup removal settings on milking efficiency and milk quality in a pasture-based automatic milking system
Bibliographic record
Abstract
In automatic milking systems (AMS), it is important\nto maximize the amount of milk harvested per day\nto increase profitability. One strategy to achieve this\ngoal is to reduce the time it takes to milk each cow.\nSeveral studies in conventional milking systems have\nshown that milking time can be reduced by increasing\nthe milk flow rate at which the teatcup is removed. One\nstudy analyzed the effect of increasing the milk flow\nswitch point on milking time in a confinement AMS.\nNo research has been conducted on teatcup removal\nsettings in pasture-based automatic milking systems.\nFurthermore, not all AMS remove the teatcups based\non absolute milk flow rate (kg/min); hence, it is important\nto study alternative strategies. The aim of\nthis experiment was to measure the effect of 3 novel\nteatcup removal strategies on box time (time in the\nAMS), milking time, somatic cell count (SCC), and\nmilk production rate of cows milked in a pasture-based\nautomatic milking system. Each teatcup removal strategy\nin this study was applied for a period of 1 wk to\n1 of 3 groups of cows and then switched to the following\ngroup until cows had transitioned through all\ntreatments. The teatcup removal strategies consisted\nof removing the teatcup when the quarter flow rate fell\nbelow 20% of the quarter rolling average milk flow rate\n(TRS20), when quarter milk flow rate was below 30%\nof the rolling average milk flow rate (TRS30), and when\nquarter milk flow rate dropped below 50% of the rolling\naverage milk flow rate (TRS50). A limit prevented\nteatcup removal if the calculated milk flow rate for\nteatcup removal was above 0.5 kg/min. This limit was\nin place for all treatments; however, it only affected the\nTRS50 treatment. The TRS30 strategy had 9-s shorter\nmilking time and 11-s shorter box time than the TRS20\nremoval strategy. The TRS50 strategy had 8-s shorter\nmilking time and 9-s shorter box time than the TRS20\nteatcup removal strategy. There was no significant difference\nin milking time or box time between the TRS30\nand TRS50 teatcup removal strategies, probably due\nto the large variability in milk flow rate at teatcup removal.\nThe TRS20 and TRS30 strategies did not differ\nin SCC or milk production rate. The 0.5 kg/min limit,\nwhich affected roughly 25% of milkings in the TRS50\ntreatment, may have distorted the effect that this setting\nhad on milk time, box time, milk production rate,\nor SCC. The difference in box time for the TRS30 and\nTRS50 strategies could allow for more than 3 extra\nmilkings per day
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".