Bibliographic record
Abstract
Recent advances in molecular biology, genomics, and immunology are revolutionizing our approach to managing infectious diseases of humans, livestock, and poultry. One of the most interesting additions to the armamentarium of research focusing on controlling infectious diseases has been a better understanding of how the host's innate immune system recognizes "danger" signals. Additionally, there has been recognition of the relationship between the innate and the specific arms of the immune system. For example, the recent discovery that CpG motifs can modulate immune responses has been used both as an adjuvant to enhance the responses to vaccines, as well as a direct immunostimulant to prevent infections. Using an Escherichia coli chicken model, we have been able to prevent cellulitis in chickens with CpG alone. Thus, CpG can be used immunoprophylactically to reduce infectious diseases. In addition, we will describe how CpG formulations with various antigens; recombinant proteins, peptides, and conventional vaccines can enhance immune responses to each of these different vaccine combinations. What is even more interesting is that CpG incorporation in vaccines can shift the immune response from a predominant T helper 2 (Th2)-like immune response generally induced by killed or subunit proteins to a much more balanced Th1-Th2 response. These immunomodulatory effects have significant implications for management of infectious diseases of all vertebrates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".