Bibliographic record
Abstract
The year 2023 marks 100 years since publication of the first report of a hyperglycemic factor in pancreatic extracts which C P Kimball and John R Murlin named glucagon (from GLUCose AGONist). Glucagon has a range of profound effects on metabolism including, but not limited to, stimulation of hepatic glucose production. Dysregulation of glucagon secretion is a key feature of both major forms of diabetes, leading to the concept that diabetes is a bihormonal disorder. Still, the work to fully understand the production and biological effects of glucagon has proceeded at a slower pace compared to that of insulin. A recent resurgence of interest in the islet alpha (α) cell, the predominant site of glucagon production, has been facilitated in part by technological innovations. This work has led to significant developments in the field, from defining how alpha cells develop and how glucagon secretion from pancreatic alpha cells is regulated to determining the role of glucagon in metabolic homeostasis and the progression of both major forms of diabetes. In addition, glucagon is considered to be a promising target for diabetes therapy, with many new potential applications arising from research in this field. This collection of reviews, led by Guest Editors James Cantley, Vincent Poitout and Rebecca Hull-Meichle, is intended to capture the field's current understanding of glucagon and alpha cell biology, as well stimulate additional interest and research on this important hormone.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.026 |
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".