Bibliographic record
Abstract
Psilocybin is a naturally occurring compound present in numerous mushroom species characterised by its hallucinogenic and psychedelic effects. Although it has a negative reputation, psilocybin has demonstrated therapeutic potential for treating mental health disorders by allowing the brain to make new neural connections which help the neural pathways adapt and break out of certain cognitive patterns related to mental illness. In recent studies, psilocybin has shown antidepressant effects, significantly reducing depressive and anxious symptoms in affected individuals. After single or low dosage, mood disorder symptoms remained in remission for 6 to 12 months. In contrast, conventional antidepressants often require multiple doses over long treatment periods to achieve similar effects. Other findings have shown that therapeutic use of psilocybin helps combat substance abuse and addictive disorders and can be applied as a cessation tool. Despite years of controversy surrounding the benefits of psilocybin, recent scientific evidence supports psilocybin’s immense potential in helping those with mental health disorders like Major Depressive Disorder (MDD), Generalised Anxiety Disorder (GAD), Post Traumatic Stress Disorder (PTSD), Obsessive-Compulsive Disorder (OCD), and addiction disorders. With such promising results, psilocybin could be incorporated into clinical use and its therapeutic effects should continue being researched.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".