Bibliographic record
Abstract
Bovine mastitis is one of the most important bacterial diseases of dairy cattle throughout the world. Mastitis is responsible for great economic losses to the dairy producer and to the milk processing industry resulting from reduced milk production, alterations in milk composition, discarded milk, increased replacement costs, extra labor, treatment costs, and veterinary services. Economic losses due to bovine mastitis are estimated to be $2 billion in the United States, $400 million in Canada (Canadian Bovine Mastitis and Milk Quality Research Network-CBMQRN) and $130 million in Australia per year. Many factors can influence the development of mastitis; however, inflammation of the mammary gland is usually a consequence of adhesion, invasion, and colonization of the mammary gland by one or more mastitis pathogens such as Staphylococcus aureus, Streptococcus uberis, and Escherichia coli.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.048 | 0.058 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.020 | 0.043 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.795 | 0.108 |
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; both teacher heads agree on what is shown here.
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".