Prediction of immunoglobulin G content in bovine colostrum by near-infrared spectroscopy
Bibliographic record
Abstract
The objective of this work was to assess the potential of near infrared spectroscopy to predict the immunoglobulin G (IgG) content in bovine colostrum. Liquid colostrum samples (n=157) were collected from Holstein cows from 2 dairy farms in southern Chile. Samples were obtained within 1h of parturition and scanned in folded transmission (transflectance) in the visible-near infrared range. Multivariate regression models (modified partial least squares) were developed with spectral data against IgG content measured by radial immunodiffusion. The best calibration included a mathematical treatment of the spectra by a second derivative plus standard normal variate and detrending. The best equation explained a high proportion of the variation in IgG content (R(2) of 0.95 in calibration and 0.94 in cross-validation). Average (91.5 g/L), standard deviation (37.6g/L), and range, as highest minus lowest values (171.9 g/L) of reference values were 10.1, 4.2, and 19 times the value of the root mean square error of cross-validation (9.03 g/L) respectively. Near-infrared spectroscopy, scanned in folded transmission, is an effective tool to predict the IgG content in liquid colostrum.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".