CB1 Receptor Signaling in the Brain: Extracting Specificity from Ubiquity
Bibliographic record
Abstract
Endocannabinoids (eCBs) are amongst the most ubiquitous signaling molecules in the nervous system. Over the past few decades, observations based on a large volume of work, first examining the pharmacological effects of exogenous cannabinoids, and then the physiological functions of eCBs, have directly challenged long-held and dogmatic views about communication, plasticity and behavior in the central nervous system (CNS). The eCBs and their cognate cannabinoid receptors exhibit a number of unique properties that distinguish them from the widely studied classical amino-acid transmitters, neuropeptides, and catecholamines. Although we now have a loose set of mechanistic rules based on experimental findings, new studies continue to reveal that our understanding of the eCB system (ECS) is continuously evolving and challenging long-held conventions. Here we will briefly summarize findings on the current canonical view of the 'ECS' and will address novel aspects that reveal how a nearly ubiquitous system can determine highly specific functions in the brain. In particular, we will focus on findings that push for an expansion of our ideas around long-held beliefs about eCB signaling that, while clearly true, may be contributing to an oversimplified perspective on how cannabinoid signaling at the microscopic level impacts behavior at the macroscopic level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".