MétaCan
Menu
Back to cohort
Record W2072161944 · doi:10.3390/s130607633

Development of a Control System for the Teat-End Vacuum in Individual Quarter Milking Systems

2013· article· en· W2072161944 on OpenAlexaboutno aff
Ulrich Ströbel, Sandra Rose-Meierhöfer, Hülya Öz, Reiner Brunsch

Bibliographic record

VenueSensors · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsnot available
Fundersnot available
KeywordsMilkingQuarter (Canadian coin)UdderVolumetric flow rateVacuum levelLevel sensorAnimal scienceElectrical engineeringOperations managementEngineeringMechanical engineeringMastitisPhysicsBiologyMechanics

Abstract

fetched live from OpenAlex

Progress in sensor technique and electronics has led to a decrease in the costs of electronic and sensor components. In modern dairy farms, having udders in good condition, a lower frequency of udder disease and an extended service life of dairy cows will help ensure competitiveness. The objective of this study was to develop a teat-end vacuum control system with individual quarter actor reaction. Based on a review of the literature, this system is assumed to protect the teat tissue. It reduces the mean teat-end vacuum in the maximum vacuum phase (b) to a level of 20 kPa at a flow rate of 0.25 L/min per quarter. At flow rates higher than 1.50 L/min per quarter, the teat-end vacuum can be controlled to a level of 30 kPa, because in this case it is desirable to have a higher vacuum for the transportation of the milk to the receiver. With this system it is possible for the first time to supply the teat end with low vacuum at low flow rates and with higher vacuum at increasing flow rates in a continuous process with a three second reaction-rate on individual quarter level. This system is completely automated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.035
GPT teacher head0.228
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations13
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueSensorsSame topicMilk Quality and Mastitis in Dairy CowsFrench-language works237,207