Short communication: Evaluation of serum immunoglobulin G concentrations using an automated turbidimetric immunoassay in dairy calves
Bibliographic record
Abstract
The absorption of maternal antibodies associated with colostrum feeding is critical to the health of calves. Multiple assays have been described to assess serum immunoglobulin G (IgG) concentrations in calves. However, none are ideal for routine use on farms. The purpose of this study was to evaluate the reliability of a new commercially available immunoassay and portable analyzer for measuring serum IgG concentrations in dairy calves. Serum from 100 Holstein calves that had received colostrum was collected for this study. Immunoglobulin G concentrations were run on each calf using both the rapid immunoassay method and radial immunodiffusion assay. Serum IgG concentrations in calves from this study ranged from 460 to 3,640 mg/dL (mean ± SD: 1,515 ± 71) as measured by radial immunodiffusion and 402 to 3,586 mg/dL (mean 1,473 ± 70) as measured by the immunoassay. Based on regression analysis, the automated results closely paralleled those obtained by radial immunodiffusion with a coefficient of determination value of 0.98. Based on the results of this study, the immunoassay technique using the portable analyzer represents a reliable method that can be run within 15 min and provide an accurate serum IgG level. Although the cost is not insignificant, this assay could be easily implemented on a dairy farm to help monitor transfer of passive immunity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".