Bibliographic record
Abstract
Type 2 diabetes refers to a group of disparate metabolic diseases, which are typically characterized by insulin resistance in peripheral tissues, together with impaired insulin secretion from pancreatic beta-cells. The complexity of type 2 diabetes is related to factors such as genetic heterogeneity, interactions between genes, and the modulating role played by the environment. Recent progress has included defining the molecular basis of monogenic forms of type 2 diabetes, such as familial partial lipodystrophy and the subtypes of maturity-onset diabetes of the young (MODY), and also the identification of chromosomal regions that may harbor type 2 diabetes susceptibility genes. Many common variants in functional and positional candidate genes, including ADRB3, PPARG, ENPP1, and CAPN10, have also been studied for their possible role as determinants of type 2 diabetes, with varying levels of agreement between studies. The availability of a relatively complete sequence of the human genome will increase the amount of genetic information that can be used to evaluate hypotheses for the genetic basis of type 2 diabetes. To make sense of human type 2 diabetes in the post-genomic era, it is essential to have well-defined phenotypes in addition to sufficient numbers of individuals with the appropriate pedigree structure from families and/or communities.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".