Aussagegenauigkeit der Milchleistungsprüfung unter Bedingungen automatischer Melkverfahren – Vergleich deutscher und kanadischer Modellansätze
Bibliographic record
Abstract
Abstract. Title of the paper: Reliability of milk recording applying automatic milking - comparison of German and Canadian model approaches Due to a high variability of milking intervals within animals rate of milk secretion and milk yield per hour in automatic milking systems (AMS) are more variable than in conventional milking systems. Further reasons are technical problems and the absence of milking persons cows with problems are to be milked. The calculation of milk yield obtained in 24 hours, only based on milking during the test day, is not precise enough. To calculate the average milk yield in a testing period as many milking as possible should be taken into consideration. Milk yield calculated according to that procedure does not correspond with milk composition of the test day. Based on 85012 milking on one farm the amount of milking was calculated, required to minimize the variability of milk yield per hour and to obtain a high correlation of the calculated daily yield with the "real" milk yield during the sampling period. Depending on number and state of lactation it was found that between 13 and 16 milking are required to obtain a maximum of accuracy. In all classes 12 milking would result in 95% of the maximum accuracy. Since farm management and type of the AMS may affect the results additional types of AMS and more farms should be included into the evaluation.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".