Bibliographic record
Abstract
G protein-linked receptor function and regulation have received considerable attention owing to the pivotal role of these receptors in mediating cellular responses to chemical transmitters. Typically, alterations in neurotransmitter status can either result in reduced receptor activity, also known as receptor desensitization, or increased receptor activity, referred to as receptor supersensitivity. Receptor desensitization has perhaps been best characterized in the (3-adrenergic receptor system, where prolonged treatment with β-receptor agonists results in the decreased number and sensitivity of β-adrenergic receptors ( Hadcock and Malbon, 1993 ). Although the role of G proteins and other signal transduction components in regulating receptor desensitization has long been appreciated, the study of receptor supersensitivity has generally focused more narrowly on changes in receptor levels and affinity. Receptor supersensitivity can be brought about in a number of ways, most commonly by receptor antagonist treatment or through denervation ( Srivastava and Mishra, 1994 ). In both cases, the dopamine receptor system has received a great deal of attention and is generally accepted as the prototype for the study of receptor supersensitivity. The purpose of this chapter is to discuss the potential role of G proteins in mediating dopamine receptor supersensitivity. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.016 | 0.007 |
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".