Assessing adaptive immune response phenotypes in Australian Holstein-Friesian heifers in a pasture-based production system1
Bibliographic record
Abstract
The objective of this study was to determine the repeatability of ranking Holstein-Friesian heifers reared in an Australian pasture-based production system for immune responses (IR) when ranking was based on secondary versus tertiary IR. Further objectives were to investigate associations between IR and stress responsiveness, ADG and resistance to internal parasites. A total of 100 heifers were IR phenotyped at 5 to 6 mo of age and again at 12 to 13 mo of age using commercial vaccine antigens to induce measurable IR. Antibody production to tetanus toxoid (TT) was used to assess antibody-mediated IR (AMIR), and delayed-type hypersensitivity reactions to vaccine antigens were used to assess cell-mediated IR (CMIR). Changes in serum cortisol and haptoglobin were used to assess stress responsiveness and fecal egg counts used as a measure of resistance to internal parasites. Based on testing, animals were categorized as either average to above-average (High) or low responders for IR. Secondary and tertiary AMIR were well correlated (r = 0.651, adjusted R(2) = 0.418, P < 0.0001), whereas correlations between secondary and tertiary CMIR were poor (r = 0.078, R(2) = –0.004, P = 0.450). A Cohen kappa (κ) test of agreement was used to test the consistency of ranking of individual animal for IR and, therefore, the ability to consistently identify low immune responder animals within the herd across test periods. The consistency of ranking (High versus low) was moderately high for AMIR (κ = 0.445), poor for CMIR (κ = –0.055), and fair to moderate for combined IR (κ = 0.395). High AMIR phenotype animals had significantly higher serum cortisol concentrations than their low immune responder counterparts (P = 0.045). A similar relationship was observed in heifers categorized for CMIR, with High CMIR responders having higher serum cortisol concentrations than their low responder counterparts (P = 0.008). High AMIR calves had a higher ADG compared with low AMIR calves (0.72 ± 0.02 versus 0.66 ± 0.06 kg/d; P = 0.009). Serum haptoglobin concentrations and worm egg counts were very low and could not be used to investigate associations with immune responsiveness. It is concluded that secondary and tertiary antibody responses to TT were well correlated in the Holstein-Friesian heifers in this study and that by using the testing procedure described here, low antibody responders were able to be consistently identified in the herd.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".