Bibliographic record
Abstract
Since the discovery of insulin in 1922 (1), the skeletal muscle, adipose tissue, and liver are traditionally regarded as key insulin-sensitive organs (2). Evidence of insulin actions in the brain emerged more recently, approximately 35 years ago, with studies showing that the hypothalamic actions of insulin regulate peripheral energy homeostasis (reviewed in [3]). Interestingly, insulin receptors were also detected in high densities in other brain regions (4), suggesting that insulin’s role in the central nervous system (CNS) extends beyond hypothalamic control of energy homeostasis. The hippocampus, a brain region key to memory and learning, was found to present particularly high levels of insulin receptors, suggesting that insulin could play a role in synaptic plasticity mechanisms and memory formation in rodents and humans. Indeed, studies using in vitro and in vivo experimental models indicated that insulin regulates neuronal survival, acts as a growth factor, and regulates circuit function and plasticity (reviewed in [5]). In cultured hippocampal neurons, insulin receptors present a punctate dendritic distribution that is consistent with the presence of insulin receptors at synaptic compartments (6). In harmony with the proposed role of insulin signaling in cognition, intranasal insulin treatment—a method achieving direct CNS delivery of insulin without invasiveness or major complications (7)—improves memory in healthy adults, without changing blood levels of insulin or glucose (reviewed in [8]). While these recent …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".