Comparison of Different Methods for Measuring Immunoglobulin Content in Calf Serum
Bibliographic record
Abstract
Objectives. The correlation between an IgG1 ELISA and different methods for immunoglobulin and serum protein determination was examined in calves 1-77 days of age to compare methods to establish failure of passive transfer of immunoglobulins. Materials and Methods. Blood samples were taken from 92 calves and age, sex, general condition and weight (weighing or thorax measure) were recorded. In serum total protein concentration was measured by Biuret's method and refractometer (Atago, Bie and Berntsen A/S). Immunoglobulin contents was masured by the semiquantitative serum glutaraldehyde test (Tennant et al. 1979, 174, 848-853, a commercial whole blood IgG test (Quick Test Calf Whole Blood IgG KTM, Midland BioProducts Corporation) and a direct sandwich IgG1 ELISA developed in our laboratory. Results. Significant correlations was found between serum IgG1 concentration and serum protein concentration measured by refractometer or Biuret and between the two methods of protein determination. Tests positive (>10g/l IgG) with the commercial test kit had significant higher IgG1 concentration (29.79 g/l) than negative test (9.75 g/l). Significant correlation was also demonstrated between IgG1 and the glutaraldehyde coagulation test. The table below shows the sensitivity, specificity, positive (posPV) and negative (negPV) predictive value and % correct classified calves (CCC) with the commercial test kit (CTK), the glutaraldehyde test (GAT) and refractometer (REFR) in relation to an IgG1 >10 g/l = no failure of passive transfer and IgG1 <10 g/l = failure of passive transfer.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".