Quantification of bovine immunoglobulin G using transmission and attenuated total reflectance infrared spectroscopy
Bibliographic record
Abstract
In this study, we evaluated and compared the performance of transmission and attenuated total reflectance (ATR) infrared (IR) spectroscopic methods (in combination with quantification algorithms previously developed using partial least squares regression) for the rapid measurement of bovine serum immunoglobulin G (IgG) concentration, and detection of failure of transfer of passive immunity (FTPI) in dairy calves. Serum samples (n = 200) were collected from Holstein calves 1-11 days of age. Serum IgG concentrations were measured by the reference method of radial immunodiffusion (RID) assay, transmission IR (TIR) and ATR-IR spectroscopy-based assays. The mean IgG concentration measured by RID was 17.22 g/L (SD ±9.60). The mean IgG concentrations predicted by TIR and ATR-IR spectroscopy methods were 15.60 g/L (SD ±8.15) and 15.94 g/L (SD ±8.66), respectively. RID IgG concentrations were positively correlated with IgG levels predicted by TIR (r = 0.94) and ATR-IR (r = 0.92). The correlation between 2 IR spectroscopic methods was 0.94. Using an IgG concentration <10 g/L as the cut-point for FTPI cases, the overall agreement between TIR and ATR-IR methods was 94%, with a corresponding kappa value of 0.84. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for identifying FTPI by TIR were 0.87, 0.97, 0.91, 0.95, and 0.94, respectively. Corresponding values for ATR-IR were 0.87, 0.95, 0.86, 0.95, and 0.93, respectively. Both TIR and ATR-IR spectroscopic approaches can be used for rapid quantification of IgG level in neonatal bovine serum and for diagnosis of FTPI in dairy calves.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".