Bibliographic record
Abstract
The complexity of the immune system is now being interrogated using methodologies that generate extensive multi-dimensional data.Effective collection, integration and interpretation of these data remain difficult, but overcoming these important challenges will provide new insights into immune function and opportunities for the rational design of new immune interventions. Immunogenomics is an information scienceJust by counting, it becomes clear that the adaptive immune system is the biggest source of human genetic variation.Each of us carries four to five million single nucleotide polymorphisms, and the HLA locus, the chromosomal region most dedicated to distinguishing self from non-self, contributes more to this total than any other part of our genome [1].Adding, for each of us, the millions of uniquely randomized T-and B-cell receptor genes that encode our immune repertoires, it becomes apparent that at the level of DNA, immunogenomic profiles are what make us most unique.This diversity is the source of the genetic plasticity that allows us to thrive as individuals and as a species in an environment of persistent yet unpredictable immune challenge.Immunogenomics, however, is not actuarial scienceit is an information science.It is a broad and diversified field that has a long history.With advancing technology, we continue to build on the hard work and remarkable insights that established the fundamental principles and mechanistic underpinnings of the immune system, such as somatic recombination, clonal selection and selftoleranceideas that when first described must have
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".