Neural Correlation of Faradarmani Consciousness Field Mind Mediation: A Comparative Functional Connectivity and Graph analysis
Bibliographic record
Abstract
The study of brain networks using analysis of electroencephalography (EEG) data based on statistical dependencies (functional connectivity) and mathematical graph theory concepts are common in neuroscience and cognitive sciences for examinations of patients and healthy individuals. Taheri Consciousness Fields and their applications in the optimization of the systems under study have been investigated in various studies. In this study, we examined the results of applying the Faradarmani Consciousness Field (CF) in the Faradarmangars’ brains (a certified and trained individual who has been entrusted with the TCFs). According to Taheri, the effects of Faradarmani CF are initiated through Faradarmangars’ minds. For this purpose, the functional and effective connectivity, and the corresponding brain graphs of EEG from the brains of a group of Faradarmangar are compared with that of non-Faradarmangar groups during Faradarmani CF Connection. According to the results, the brain of the Faradarmangars showed a significantly decreased activity in delta (BA8), beta2 (BA4/6/8/9/10/11/32/44/47), and beta3 (in 34 of 52 BA) frequency bands, mainly in the frontal lobe and after that in parietal and temporal lobes in comparison with the non-Faradarmangars. Moreover, the frontal network’s functional and effective connectivity analysis showed dominant multiple decreased connectivity, mainly in the case of the beta3 frequency band in all parts of the frontal network. On the other hand, the graph theory analysis of the Faradarmangar brain indicated an increase in the activity of the O2-T5-F4-F3-FP2-F8 areas and a significant decrease in the characteristic path length and increases in global efficiency, clustering coefficient and transitivity. In conclusion, the unique higher graph function efficiency and the reduction in the brain activity and connectivity during the Faradarmani CF mind mediation showed the human brain's passive and detector-like function in this task.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".