Evaluation of enzyme-linked immunosorbent assays performed on milk and serum samples for detection of neosporosis and leukosis in lactating dairy cows.
Bibliographic record
Abstract
Serum and milk samples from 1229 cows on 22 Ontario dairy farms were individually tested for antibodies specific for bovine leukosis virus (BLV) and Neospora caninum by enzyme-linked immunosorbent assay (ELISA). Antibodies against BLV were present in 361 serum samples (29.4%) and 369 milk samples (30.0%). Comparing the 2 tests, agreement was almost perfect (k = 0.86; 95% CI = 0.83 to 0.90) and the proportions of samples positive were not significantly different (P = 0.56). Both tests identified the same 3 herds free of bovine leukosis virus. Antibodies against N. caninum were detected in 138 serum samples (11.2%), and 111 milk samples (9.0%). Agreement between the 2 tests was moderate (k = 0.52; 95% CI = 0.43 to 0.59). Four herds were free of neosporosis by the serum test, while 10 herds were negative by the milk test. The ELISA on milk samples facilitates sample collection to classify herds free of BLV; the milk N. caninum ELISA was less reliable in predicting herd-level infection.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".