Editorial: Glycans: molecules at the interface of immunity and disease
Bibliographic record
Abstract
Glycans: molecules at the interface of immunity and diseaseBiomolecules are evolved machines.The chemical properties of proteins, lipids and nucleotides are manifestly suited to their essential functions (1).What, then, is the intrinsic function of glycans?Organisms use sugars in such variety; could there be any one role that optimizes the use of glycans' inherently diverse structures and chemistry?If nucleotide polymers are 'for' information storage and proteins are 'for' catalysis, we suggest that glycans are for context -they extend and modify biochemical capability.Glycans enhance life's patterning systems; their finely regulated and contextdependent functions augment processes that require diversity and precision (2).This Research Topic collects research on immunity and disease, where glycans' diverse structures and functions contextualize and enact responses to illness and infection.The articles in this Research Topic illustrate glycans' many biological functions, fine-tuned localization and regulation, diverse structures and synthesis, and varied evolutionary history.Understanding patterns that glycans enact and how these patterns respond to organismal state is central to a complete and nuanced picture of immunity and to targeted disease interventions.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.022 |
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".