Evaluation of Digital and Optical Refractometers for Assessing Failure of Transfer of Passive Immunity in Dairy Calves
Bibliographic record
Abstract
BACKGROUND: Failure of transfer of passive immunity (FTPI) is the underlying predisposing risk factor for most early losses in dairy calves. Refractometers, either optical or digital, can be used to assess FTPI as a part of calf health monitoring program on dairy operations. OBJECTIVES: To evaluate the performance of and differences between digital Brix and optical refractometers for assessing FTPI in dairy calves. ANIMALS: Two hundred Holstein calves from 1 to 11 days of age. METHODS: A cross-sectional study was designed to measure serum IgG concentration by radial immunodiffusion (RID) assay, digital Brix and optical refractometers. The correlation coefficients (r) between the 2 refractometers were plotted against each other and against the measured IgG concentration from RID. The Se, Sp, and accuracy of digital Brix and optical refractometers for assessing FTPI using previously recommended cut-offs were calculated. A receiver operating characteristic curve was created and used to identify the optimal cut-off for this dataset. RESULTS: The RID IgG concentration was positively correlated with digital Brix (r = 0.79) and optical (r = 0.74) refractometers. The best combination of Se (85.5%), Sp (82.8%), and accuracy (83.5%) of digital Brix refractometer was at 8.3%Brix. For optical refractometer, the best combination of Se (80%), Sp (80.7%), and accuracy (80.5%) was at 5.5 g/dL. CONCLUSIONS AND CLINICAL IMPORTANCE: Both refractometers exhibited utility in assessing FTPI in dairy calves.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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".