Cannabis and Brain: Disrupting Neural Circuits of Memory
Bibliographic record
Abstract
Cannabis is a federally controlled substance, it’s very familiar to many but its neurobiological substrates are not well-characterized. In the brain, most areas prevalently having cannabinoid receptors have been associated with behavioral control and cognitive effects due to cannabinoids. Study over the last several decades suggested cannabinoids (CBs) exert copious oftentimes opposite effects on countless neuronal receptors and processes. In fact, owing to this plethora of effects, it’s still cryptic how CBs trigger neuronal circuits. Cannabis use has been revealed to cause cognitive deficits from basic motor coordination to more complex executive functions, for example, the aptitude to plan, organize, make choices, solve glitches, remember, and control emotions as well as behavior. Numerous factors like age of onset and duration of cannabis use regulate the severity of the difficulties. People with the cannabis-linked deficiency in executive functions have been found to have trouble learning and applying the skills requisite for fruitful recovery, setting them at amplified risk for deterioration to cannabis use. Exploring the impacts of cannabis on the brain is imperative. Therefore the intention of this study was to analyze the neuropsychological effects and the impact of CBs on the dynamics of neural circuits, and its potential as the drug of addiction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".