Fresh Ideas, Foundational Experiments (FIFE): Immunology and Diabetes 2016 FIFE Symposium
Bibliographic record
Abstract
The first Fresh Ideas, Foundational Experiments (FIFE): Immunology and Diabetes symposia workshop took place in 2016 and exemplified the active interest of a number of several investigators interested the global rise in the incidence of type 1 diabetes (T1D). This increase does not correlate with genetic drift and indicates that environmental exposures are playing an increasingly significant role. Despite major biomedical and technological advances in diagnosis and treatment, treatments are frequently insufficient as they do not inhibit the progression of the underlying autoimmune response and often fail to prevent life-threatening complications. T1D is the result of autoimmune destruction of the insulin-producing beta cells of the pancreas, and the precise, mechanistic contribution of the immune system to disease pathogenesis and progression remains to be fully characterized. Ultimately, the combinatorial effect of concurrent factors, including beta cell fragility, exogenous stressors, and genetic priming of the innate and adaptive immune system, work together to induce T1D autoimmunity. Thus, T1D is the result of immunological defects and environmental pathogens, requiring the sustained attention of collaborative research teams such as FIFE: I & D with varied perspectives, unified by the universally held goal of finding a sustainable, life-long cure. Herein, the authors provide perspective on various fields in T1D research highlighted by speakers participating in the inaugural FIFE symposium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".