Development of v acuum Drop p re Diction f unctions in conventional an D Quarter i n Divi Dual m ilking s ystems u sing r esponse s urface m etho Dology
Bibliographic record
Abstract
RoSe-MeieRhofeR, S., h. oz, A. DegiRMencioglu, h. Bilgen, u. StRoBel and R. BRunSch, 2013. Development of vacuum drop prediction functions in conventional and quarter individual milking systems using response surface methodology. Bulg. J. Agric. Sci., 19: 1437-1444 the objective of this study was to develop empirical functions in order to predict and compare vacuum drops in b and dphase and in claw (or junction point) unconventional and quarter individual milking systems using response surface methodol ogy (RSM). the independent variables considered in the study included the system-working vacuum, pulsation rate and ratio and milk flow rate. Experiments based on the central composite design (CCD), one of the designs in RSM and using water and artificial teat were conducted in the laboratory. The data obtained in the laboratory were then used to develop functions in polynomial form that allowed predicting the vacuum drops in b and d-phase and claw for both systems. The coefficient of the determination for all the models was above 93 % except the one developed for the vacuum drop prediction at junction point in quarter individual milking system. it is believed that the models developed in this study will enhance the knowledge in ma
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".