Fiber Optic Network Technology of Communication of Specialists via Mental Neurointerfaces
Bibliographic record
Abstract
Resonance communication of specialists can be carried out at a distance in real time in dialogue mode through a mental neurointerface with two-way communication. Mental neurointerface on the principles of magnetic resonance tomography captures a hologram of brain activity of the internal speech of the inductor specialist and transmits it to the mental neurointerface of the recipient's interlocutor through ultra-sensitive multi section nano resonators waveguides. The mental neurointerface of the recipient's interlocutor perceives the transmitted hologram of the brain activity of internal speech and resonates its internal speech to it. An interlocutor of the recipient with equivalent semantic memory in a resonant way makes sense of the internal speech of the inductor specialist. He forms the response with internal speech and transmits it to the interlocutor with his mental neurointerface through nano resonators in the form of holograms of the brain activity of internal speech. Interlocutors, as specialists in one subject area, have a similar semantic memory. Semantic memories are considered similar if they correspond to the principle of gold section according to a professional thesaurus. Specialists and interlocutors must learn a professional thesaurus before starting a dialogue through mental neurointerfaces. Thus, the problem of transmitting and reading thoughts at a distance using high technology is solved, taking into account the psychological aspects of the interlocutors. The development of mental neurointerfaces and ultra-sensitive multi section nano waveguide resonators for transmitting holograms of internal speech brain activity is just beginning. The use of resonant communication by mental neurointerfaces through nano waveguides resonators waveguides is currently very relevant in many areas of life activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".