Parametric Resonance via Neuronal Microtubules: Filtering Optical Signals by Tryptophan Qubits
Bibliographic record
Abstract
This paper aims to address the possibility of parametric resonance effects in microtubules via tryptophan qubits, using the Hamiltonian of the cavity quantum electrodynamics (QED) model involving photons in a waveguide and the surrounding environment. The time evolution equations for qubits and photons are derived using the input–output formulation. Input signals with a 560 nm wavelength are amplified by Rabi oscillations for tryptophan qubits in excited states. Here, the qubits organized in multiple layers are all in excited states. When an appropriate decay to the environment occurs as internal loss, which is prepared in multiple layers, we find binary patterns of the parametric amplification of input signals and the reduction of output signals. This property might help us to understand the information processing of optical signals by filtering them with the use of tryptophan residues in microtubules and diffused nonlocal processing spreading over the whole brain in the form of holograms.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".