Agreement of Predicted 305-Day Milk Yields Relative to Actual 305-Day Milk Weight Yields
Bibliographic record
Abstract
The objectives of this study were to compare the multiple trait prediction (MTP) model estimate of 305-d lactation yield with the 305-d daily milk yield data from on-farm automated meters and software and to examine the accuracy of electronic identification (ID). Twenty-four-hour milk and component yields are calculated by using milk weights and samples collected 8 to 10 times/yr by Dairy Herd Improvement (DHI) organizations. Daily milk weights were collected from cows on 20 Canadian farms that used parlor milking systems with electronic ID and that were enrolled in a regular DHI program. A total of 10,175 DHI test days from 1,103 cows with complete 305-d lactation yields were entered into the MTP model, and lactation yields were predicted. Test days were grouped into first, second, and third and greater lactations and within each lactation group, days in milk were categorized in 3 stages (5 to 60, 61 to 120, and 120 to 305 d in milk) for a total of 9 classes. Agreement analysis was used to compare the 305-d sum of daily milk to the MTP 305-d lactation yield predictions by using inputs from test days throughout the lactations. Results indicated that the MTP model overestimated lactation yields across all parity groups, ranging from 310 to 1,552 kg in parity 1, 640 to 2,000 kg in parity 2, and 567 to 1,476 kg in parity 3 and greater. A preliminary examination of electronic ID accuracy was conducted on 4 farms. Two electronic ID systems were examined for cow ID accuracy by verifying the ID number appearing in the parlor with the corresponding ear tag number. There were no ID errors on 3 of 4 farms tested and only a very small number of errors (3/80) on the fourth farm, indicating that the electronic ID systems used in milking parlors identify cows accurately.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".