Combined Spectral Resonances of Signaling Proteins’ Amino Acids in the ERK-MAP Pathway Reflect Unique Patterns that Predict Peak Photon Emissions and Universal Energies
Bibliographic record
Abstract
The duality of matter-energy as particle-waves was applied to the classic ERK-MAP signaling pathways between the plasma cell membrane and the nucleus and was tested with Cosic’s Resonance Recognition Method. Spectral analyses of sequences of pseudopotentials that reflect de-localized electrons of amino acids for the 11 proteins in the pathway were computed. The spectral power density of the terminal protein (cFOS) was shown to be the average of the profiles of the precursor proteins. The results demonstrated that in addition to minute successive alterations in molecular structure wave-functions and resonant patterns can also describe complex molecular signaling pathways in cells. Different pathways may be defined by a single resonance profile. The separations between the peaks of wavelengths from Cosic’s predictions for photon emissions in the visible spectrum that define the ERK-MAP pathway were within the range of 10-20 J. This quantity has been shown to be a fundamental unit of energy within the universe. The involvement of photon patterns indicates that non-local effects could accompany the serial causality (locality) assumed to connect molecular pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".