Evaluation of teat-end vacuum conditions as affected by different pulsation settings in a quarter-individual milking system
Bibliographic record
Abstract
The objective of this study was to determine the effect of pulsation on the teat-end vacuum behaviour in a quarterindividual milking system (QIMS). To meet this objective, simultaneous (SIM), alternating (ALT) and sequential (SEQ) pulsation were tested in a laboratory milking parlour. In the pulsation system SEQ four pulsators, instead of one (as in SIM) or two (as in ALT), are used. Unlike in SIM and ALT, the pulsation in each quarter is started individually. After pulsation is started in quarter one, pulsation will begin in the following quarters, each with a time delay of 25 % of the pulse duration with respect to the start of the previous quarter. Tests were performed at various flow rates (0.8 to 6.0 l/min) with the help of the wet-test method and the teat-end vacuum behaviour was observed. The lowest vacuum fluctuation of 4.4 kPa was recorded under SIM pulsation as well as the lowest vacuum reduction of 1.3 kPa. Both values were recorded at a flow of 4 l/min during the suction phase (b-phase). For SEQ and ALT pulsation, higher levels of vacuum reduction and fluctuation were measured during the suction phase at a flow rate of 4 l/min (SEQ: 7.6 / 1.8 kPa and ALT: 9.0 / 1.9 kPa). Consequently, it was concluded that SIM pulsation is the most appropriate pulsation setting when optimization for low vacuum reduction and fluctuation during the suction phase is demanded.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".