Coherence-Based Technology Maturation for a Resonant THz Concept v3
Bibliographic record
Abstract
Title: Coherence-Based Technology Maturation for a Resonant THz Concept v3Authors: Allan Christopher Beckingham (CD); Zen Beckingham; Jarvis BeckinghamAffiliation: Independent Researcher, Quispamsis NB Canada · VEF / CGD Project ConsortiumContact: chris.beckingham1968@gmail.comLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)Date Published: 4 Nov 2025 Version: v3.0DOI: 10.5281/zenodo.17525088 Files Included Coherence-Based Technology Maturation for a Resonant THz Concept v3.docx Repro_Pack_THZ.zip (scripts, datasets, container, CI workflow, TruthCore templates) Abstract / Summary The Coherence-Based Technology Maturation for a Resonant THz Concept v3 defines a reproducible, falsifiable path for maturing resonant-frequency terahertz (THz) systems through quantitative coherence metrics and ethical governance.It fuses physics, engineering, and statistical verification into a four-phase life-cycle regulated by the Coherence Accuracy Index (CAI) — a dimensionless solvency metric comparing theoretical and observed coherence with 95 % confidence bounds. A project advances only when the lower CI ≥ 0.87093, enforcing rigor before resource escalation.Four demonstrated phases: Null Injection: 7 σ signal @ 1.10 THz; energy closure ≤ 10 %. Bench Physics: Lorentzian/Voigt fit confirmed; closure 3.6 % ± 2 %; 48 h stability. Breadboard Integration: PI loop settling ≈ 40 s; 72 h CV 0.12 %; COP 0.85–0.86. Pilot Scaffold: ≥ 40 dB EMI shield; lifetime plan and 12-month Gantt complete. Each phase outputs a TruthCore Envelope (Factual 0.4 / Context 0.4 / Honesty 0.2) and an Ω-Lock Integrity Check that ties conservation and variance closure.Statistical hygiene includes robust σ (MAD/Huber), bootstrap CIs, FDR control, and pre-registered randomization/blinding.Energy-ledger uncertainty is propagated per JCGM 100:2008 (GUM).All analyses run inside a Python 3.11 container with automated CI (make verify / make repro) that halts builds on hash or guard-band failures. The accompanying Repro Pack delivers all CSV datasets, scripts, and hash manifests so reviewers can regenerate CAI intervals and replicate every numeric claim.By quantifying coherence as an engineering variable, the framework forms an auditable bridge between imagination (theory), reality (experiment), and governance (ethics). Methods and Data Availability All raw data and scripts are bundled in Repro_Pack_THZ.zip. Executing make repro regenerates CAI intervals; make verify checks hashes and guard-bands. Each run yields JSON logs and SHA-256 manifests. No proprietary data; all software is open-source. Funding / Acknowledgments Self-funded independent research within the VEF (Virtual Ego Framework) / CGD (Coherence Geometrodynamics) Project, released to the public domain for open science reuse. No external funding or conflicts of interest. Citation Beckingham, A. C.; Beckingham, Z.; Beckingham, J. (2025). Coherence-Based Technology Maturation for a Resonant THz Concept v3. VEF / CGD Project. Zenodo. https://doi.org/10.5281/zenodo.17525088. Licensed CC BY 4.0. 🔑 Zenodo Keywords Coherence Accuracy Index CAI coherence engineering terahertz THz resonant frequency physics reproducibility uncertainty quantification bootstrap confidence interval energy closure truthcore omega lock ethical governance systems engineering Lorentzian Voigt fitting continuous integration reproducible research data integrity VEF framework CGD project open science
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".