Bibliographic record
Abstract
Innate lymphoid cells (ILCs) are increasingly being recognized for their ability to impact both innate and adaptive immune cells in diverse contexts. ILCs have been observed in all secondary lymphoid tissues, in addition to being tissue-resident innate lymphocytes. In these locations, ILCs are poised to interact with various immune cells at different stages of an immune response. While the heterogeneity and plasticity of ILCs has complicated their study, their association with immune dysregulation in a wide range of pathologies highlights their importance to human health and disease. Notably, in addition to promoting inflammatory immune responses, populations of ILCs have been shown to inhibit immune responses through a variety of mechanisms. The reports of ILC-mediated regulation of immune responses have differed in terms of the phenotype of the regulatory ILC populations, and their mechanism of action. Yet the ability to modulate immune responses appears to be an important function of ILCs. As our understanding of this family of lymphocytes evolves, delineating the factors that dictate whether ILCs orchestrate inflammatory immune responses or suppresses these responses will be important for understanding various disease mechanisms. Here we focus on recent reports that examine how ILCs regulate immunity in different contexts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".