Evaluation of transmission infrared spectroscopy and digital and optical refractometers to identify low immunoglobulin G concentrations in alpaca serum.
Bibliographic record
Abstract
= 0.99]. However, the correlation coefficients between Brix % and STP with serum RID-IgG concentrations were only 0.56 and 0.55, respectively. Twenty-one (12.4%) of 169 alpaca serum samples had IgG concentrations of < 10 g/L. Using receiver operator characteristic curve (ROC) analysis, the optimal cutoff points for the TIR assay, digital Brix, and optical STP refractometers for assessing low IgG (RID < 10 g/L) were 13 g/L, 8.8%, and 50 g/L, respectively. The TIR assay showed higher sensitivity (Se = 95.2%) and specificity (Sp = 96.8%) than either the digital Brix (Se = 90.5% and Sp = 65.5%) or optical STP (Se = 81% and Sp = 73.7%) refractometers for assessing alpacas with low IgG. In conclusion, the Brix and STP refractometers lack accuracy in measuring alpaca IgG concentrations, but may be useful for screening animals for low serum IgG. However, the TIR assay with a cutoff point of 13 g/L was more appropriate for identifying low IgG than either refractometer. Another study that focuses on neonatal crias is recommended in order to evaluate the usefulness of these assays for field diagnosing of failure of transfer of passive immunity (FTPI).
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".