Detection of Anti‐ <i>Brucella</i> Antibodies in Llama ( <i>Lama glama</i> )
Bibliographic record
Abstract
Seven llamas were immunized with killed Brucella abortus S1119.3 cells and bled sequentially, resulting in 64 samples. An eighth llama was kept as a negative control. In addition, 299 llama and 2075 alpaca sera, submitted for diagnostic testing, were included. Sera from all llamas were tested by the buffered antigen plate agglutination test, the complement fixation test, and the indirect enzyme immunoassays using smooth and rough lipopolysaccharides. A competitive enzyme immunoassay and fluorescence polarization assays were also performed. The sensitivity values for llama sera ranged from 92.2% to 100% and the specificity values from 89.6% to 100%. No alpacas were immunized. The specificity values for alpaca sera ranged from 94.8% to 100% specific although some sera gave an 'agglutination like' reaction after about 10 minutes of incubation. The complement fixation test could not be used, as 31% of the sera were anticomplementary and 4% were false positive.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".