Comparative Approaches to the Investigation of Responses to Stress and Viral Infection in Cattle
Bibliographic record
Abstract
Fatal bovine respiratory disease (BRD) is a major cause of financial losses in the cattle industry. A variety of stressors have been implicated as contributing to disease severity. However, it has proven difficult to determine the role these individual factors may play in the final outcome of this disease complex. The objective of the present investigation was to obtain proteomic, metabonomic, and elemental profiles of bovine serum samples from stressed and control animals before and after a primary viral infection to determine if these profiles could distinguish between responses to stressors and viral infection. Multivariate analysis revealed distinct differential trends in the distribution profile of proteins, metabolites, and elements following a stress response both before and after primary viral infection. A group of acute phase proteins, metabolites, and elements could be specifically linked to either a stress response (decreased serum amyloid A and Cu, increased apolipoprotein CIII, amino acids, LDL, P, and Mo) or a primary viral respiratory infection (increased apolipoprotein A1, haptoglobin, glucose, amino acids, LDL and Cu, decreased Lipid, and P). Thus, combined OMICS analysis of serum samples revealed that multimethod analysis could be used to discriminate between the complex biological responses to stress and viral infection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".