Genetic analysis of MUN and lactose and their relationships with economically important traits in Canadian Holstein cattle
Bibliographic record
Abstract
Traditional milk recording by DHI organizations collects milk weights and samples for each cow. Milk samples are sent to the lab for analysis of fat and protein content, and for the count of somatic cells. More recently, DHI labs are analyzing the milk samples also for milk urea nitrogen (MUN) and for the percentage of lactose. The Programme d'Analyse des Troupeaux Laitiers du Quebec (PATLQ) has been collecting data in Quebec dairy herds on lactose since 2001 and MUN since 1997. While data on MUN is also being collected in other Canadian provinces, testing for lactose percentage in Canada is currently done exclusively in Quebec by PATLQ. Concentrations of MUN are measured at Canadian DHI labs by infrared technology. Infrared MUN values are calculated from prediction equations that use spectrum analyses and are an indirect measure of MUN. MUN can also be measured by wet chemistry methods, which directly measure concentration of urea nitrogen in milk samples. Because of higher costs of wet chemistry analysis, infrared methodology is commonly used by DHI in Canada.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".