[EN] This deposit summarizes two frozen workflows in one methodological bundle. (1) Bell / CHSH (real public data): Binary CHSH on the NIST complete-blind CSV. Pairing uses tolerances in NIST grid-index units on the exported t axis (not calibrated seconds in this repository). Frozen snapshot: battle_results/nist_completeblind_2015-09-19/battle_result.json (S_strict = 2.336276 at tolerance 0.0 vs S_standard = 2.839387 at 15.0; pair counts 136632 vs 148670). Reproduce the window scan via python scripts/explore/chsh_denominator_audit.py. (2) GHZ-style statistic (simulation, V10.4 only): The GHZ leg is not tied to ghz_loop_explosion_v19.py. It is anchored to scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py, which emits a computed_curve (type in V10_4_REAL_COST_CURVE.meta.json): a fixed RNG phase sample (medium_phase_states), soft amplitude gating with parameter gate_k, reported F_gated, mean simultaneous-hit retention R_gated, and a matched-retention random control (F_random_mean, F_random_std). Canonical outputs live under artifacts/ghz_medium_v10/ (V10_4_REAL_COST_CURVE.png, .csv, .meta.json, V10_4_REAL_COST_CURVE_REPORT.md). Scope statement: This is methodological reproducibility documentation — not a claim that either pipeline certifies a specific hardware Bell or GHZ experiment. [中] 本归档将两条冻结工作流合并为一条方法论叙述。 (1)贝尔 / CHSH(真实公开数据): 在 NIST complete-blind CSV 上做二元 CHSH。配对容差取导出列 t 上的 NIST 网格指数单位(本仓库不标定为秒)。冻结快照:battle_results/nist_completeblind_2015-09-19/battle_result.json(容差 0.0 时 S_strict = 2.336276,15.0 时 S_standard = 2.839387;配对数 136632 / 148670)。窗口扫描复现:python scripts/explore/chsh_denominator_audit.py。 (2)GHZ 型统计量(仿真,仅 V10.4): GHZ 侧不再绑定 ghz_loop_explosion_v19.py。唯一锚点为 scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py,产出 computed_curve(见 V10_4_REAL_COST_CURVE.meta.json 中 type):固定 RNG 相位样本(medium_phase_states)、参数为 gate_k 的软振幅门控、报告 F_gated、同时触中平均保留 R_gated,以及同保留率随机对照(F_random_mean、F_random_std)。规范产物位于 artifacts/ghz_medium_v10/(V10_4_REAL_COST_CURVE.png、.csv、.meta.json、V10_4_REAL_COST_CURVE_REPORT.md)。 范围声明: 本文为方法论与可复现性文档——不声称任一脚本对某一具体硬件贝尔或 GHZ 实验作出鉴定。 Reproducibility (repository): Bell: python scripts/explore/chsh_denominator_audit.py (needs data/nist_completeblind_side_streams.csv). GHZ V10.4: python scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py. 可复现(配套仓库): Bell:python scripts/explore/chsh_denominator_audit.py(需 data/nist_completeblind_side_streams.csv)。GHZ V10.4:python scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py。
[EN] This deposit summarizes two frozen workflows in one methodological bundle. (1) Bell / CHSH (real public data): Binary CHSH on the NIST complete-blind CSV. Pairing uses tolerances in NIST grid-index units on the exported t axis (not calibrated seconds in this repository). Frozen snapshot: battle_results/nist_completeblind_2015-09-19/battle_result.json (S_strict = 2.336276 at tolerance 0.0 vs S_standard = 2.839387 at 15.0; pair counts 136632 vs 148670). Reproduce the window scan via python scripts/explore/chsh_denominator_audit.py. (2) GHZ-style statistic (simulation, V10.4 only): The GHZ leg is not tied to ghz_loop_explosion_v19.py. It is anchored to scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py, which emits a computed_curve (type in V10_4_REAL_COST_CURVE.meta.json): a fixed RNG phase sample (medium_phase_states), soft amplitude gating with parameter gate_k, reported F_gated, mean simultaneous-hit retention R_gated, and a matched-retention random control (F_random_mean, F_random_std). Canonical outputs live under artifacts/ghz_medium_v10/ (V10_4_REAL_COST_CURVE.png, .csv, .meta.json, V10_4_REAL_COST_CURVE_REPORT.md). Scope statement: This is methodological reproducibility documentation — not a claim that either pipeline certifies a specific hardware Bell or GHZ experiment. [中] 本归档将两条冻结工作流合并为一条方法论叙述。 (1)贝尔 / CHSH(真实公开数据): 在 NIST complete-blind CSV 上做二元 CHSH。配对容差取导出列 t 上的 NIST 网格指数单位(本仓库不标定为秒)。冻结快照:battle_results/nist_completeblind_2015-09-19/battle_result.json(容差 0.0 时 S_strict = 2.336276,15.0 时 S_standard = 2.839387;配对数 136632 / 148670)。窗口扫描复现:python scripts/explore/chsh_denominator_audit.py。 (2)GHZ 型统计量(仿真,仅 V10.4): GHZ 侧不再绑定 ghz_loop_explosion_v19.py。唯一锚点为 scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py,产出 computed_curve(见 V10_4_REAL_COST_CURVE.meta.json 中 type):固定 RNG 相位样本(medium_phase_states)、参数为 gate_k 的软振幅门控、报告 F_gated、同时触中平均保留 R_gated,以及同保留率随机对照(F_random_mean、F_random_std)。规范产物位于 artifacts/ghz_medium_v10/(V10_4_REAL_COST_CURVE.png、.csv、.meta.json、V10_4_REAL_COST_CURVE_REPORT.md)。 范围声明: 本文为方法论与可复现性文档——不声称任一脚本对某一具体硬件贝尔或 GHZ 实验作出鉴定。 Reproducibility (repository): Bell: python scripts/explore/chsh_denominator_audit.py (needs data/nist_completeblind_side_streams.csv). GHZ V10.4: python scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py. 可复现(配套仓库): Bell:python scripts/explore/chsh_denominator_audit.py(需 data/nist_completeblind_side_streams.csv)。GHZ V10.4:python scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py。
Archive Notice (Deprecated): This record is kept for archival purposes and is no longer the current version. Please use the latest Bell Audit v5 record: https://zenodo.org/records/19904639 (DOI: https://doi.org/10.5281/zenodo.19904639). 留档提示(已过期): 本记录仅用于历史留档,已非当前版本。请优先访问最新版 Bell Audit v5:https://zenodo.org/records/19904639(DOI:https://doi.org/10.5281/zenodo.19904639)。 [EN] This paper presents a "Denominator Audit" of Bell/CHSH violations using NIST experimental data. Historically, violations of S > 2.0 are cited as proof of non-locality. Our audit reveals that these results are not intrinsic to the physical system but are "manufactured" through the selective manipulation of coincidence windows (Accounting Fraud). On the same NIST complete-blind stream, we compare a strict pairing tolerance (0.0) versus the Round-1 standard tolerance (15.0) in NIST grid-index units on the exported CSV t axis (not calibrated seconds in this deposit). The frozen binary-CHSH snapshot gives S_strict = 2.336276 vs S_standard = 2.839387 (Δ = +0.503111). A bootstrap CI for the standard branch includes 2\sqrt{2} (artifacts/reports/chsh_bootstrap_ci_standard15.json, ci_contains_tsirelson: true); we therefore discuss protocol sensitivity, not Tsirelson violation. Separately, the “30 cm truth” is a laboratory heuristic (about 1 ns ↔ ~30 cm in vacuum); it motivates coincidence bookkeeping sensitivity and must not be read as “parameter 15.0 equals 15 ns” here. Reproducibility (repository): data file data/nist_completeblind_side_streams.csv; run from repo root python scripts/explore/chsh_denominator_audit.py (writes artifacts/bell_window_scan_v1/). [中] 在同一组 NIST complete-blind 事件流上,我们在导出 CSV 的 t 网格上对比严苛配对容差(0.0)与第一轮标准容差(15.0),单位为 NIST 网格指数(本归档不将其标定为秒)。冻结的二元 CHSH 快照为 S_strict = 2.336276 与 S_standard = 2.839387(Δ = +0.503111)。标准分支的 bootstrap 置信区间覆盖 2\sqrt{2}(artifacts/reports/chsh_bootstrap_ci_standard15.json,ci_contains_tsirelson: true);因此本文强调协议敏感性,而非 Tsirelson 违背。另:“30 厘米的真相”是实验室启发(真空中约 1 纳秒 ↔ 约 30 厘米),用于提示符合会计的敏感性,不应被读作“参数 15.0 等于 15 纳秒”。 可复现(配套仓库): 数据 data/nist_completeblind_side_streams.csv;在仓库根目录运行 python scripts/explore/chsh_denominator_audit.py(输出写入 artifacts/bell_window_scan_v1/)。
This deposit provides a professional Bell audit rewrite based on the Apr-29 v4 evidence package and is designed for reviewer-level traceability. Key quantitative findings on a fixed event stream: Binarization shift: S_raw(continuous) = 1.117683 vs S_raw(quant_2) = 2.297779. Semantic premium staircase: same_index = 2.329417, external_bucket_all = 2.775687 (+0.446270), event_anchor_nearest = 2.834670 (+0.058983). Closure checks: same_index_not_near_2p82 = True, pure_bucket_in_2p8_zone = True, anchor_asymmetry_small = True (delta_abs = 0.001356), edge_sensitivity_bounded = True. Experimental principle and method: Principle: CHSH headline values can be altered by representation and inclusion rules even when the underlying row/event stream is unchanged. Method chain: same data -> quantization choice -> pairing semantics -> denominator inclusion policy -> CHSH report. Scope: methodological reproducibility and protocol sensitivity audit. Not claimed: ontology-final conclusion or direct falsification of all competing theories. Primary manuscripts: papers_final/05_Bell_Audit/version_01/【0】_Bell_Audit/files/01_Bell_Audit_Professional_CN.md papers_final/05_Bell_Audit/version_01/【0】_Bell_Audit/files/02_Bell_Audit_Professional_EN.md Reproducibility Run from repository root: python scripts/explore/nist_same_index_quantization_sweep_v4.py python scripts/explore/nist_unified_semantics_audit_v4.py python scripts/explore/nist_revival_20pct_closure_v4.py
[EN] This deposit documents a rigorous in-silico post-selection audit of a GHZ-type Mermin statistic in the repository's medium-v10 wave model (three local phase-locked sources), motivated by textbook/laboratory GHZ narratives—it is not claimed to replay one specific hardware run. Within that simulator class, we show that chasing high F couples to amplitude gating and low retention, and matched-retention random controls fail to reproduce the gated lift—supporting a selection-driven reading of the reported metric inside the model. Using the V10.4 real cost curve and matched-retention random controls, we show that elevated F values in this simulator class are tightly coupled to selection rules and retention trade-offs. We frame this as a method-level sensitivity result inside the model class, not as a hardware-level claim. [中] 本归档记录对 medium-v10 波动模型中 GHZ 型 Mermin 统计量的严格仿真后选择审计(三个局域相位锁定源),动机来自教科书/实验室叙事,不声称复刻某一具体硬件实验。在该模拟器类内,我们展示:追逐高 F 与振幅门控及低保留率绑定,而同保留率随机对照无法复现门控抬升——从而在模型内支持对报告指标的筛选驱动解读。 通过 V10.4 真实代价曲线和同保留率随机对照,我们展示了:在该模拟器类内,较高 F 与筛选规则及保留率权衡紧密耦合。本文将其定位为模型内的方法学敏感性结果,而非硬件层结论。 Reproducibility (repository): from repo root run python scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py (see script header for output paths). Companion figure in this deposit: figures/V10_4_REAL_COST_CURVE.png. 可复现(配套仓库): 在仓库根目录运行 python scripts/explore/ghz_medium_v10/v10_4_real_cost_curve.py(输出路径见脚本头部)。本归档插图:figures/V10_4_REAL_COST_CURVE.png。
This paper synthesizes two published audit lines into a single reproducible framework for Bell and GHZ interpretation under analysis-rule sensitivity. Using public records and artifact-linked evidence, we show that Bell/CHSH on the same NIST event stream shifts from S_strict=2.336276 to S_standard=2.839387 (Delta=+0.503111) under protocol change, while GHZ search under the current audited pipeline remains far from F=4 (best fine-stage F=0.085396). We formalize a ledger-style audit protocol that treats pairing windows, denominator policy, post-selection, and uncertainty as first-class reported variables. We further introduce a singles-aware diagnostics layer (C_norm = coincidences / sqrt(singles_A * singles_B)) as a testable bridge for normalization-path analysis, while explicitly avoiding ontology-level promotion from current evidence. The contribution is a publication-ready, boundary-aware audit standard with claim-to-artifact traceability across both lines. Related records: Bell DOI https://doi.org/10.5281/zenodo.19763028 ; GHZ DOI https://doi.org/10.5281/zenodo.19763473 ; Code repository: https://github.com/tomnattle/chain-explosion-model
We present a denominator-audit framework for Bell/CHSH statistics to disentangle mechanism effects from protocol-bookkeeping effects under a shared data context. The protocol compares strict and standard definitions in parallel and separates evaluation into an engineering gate (computability and basic data sufficiency) and a thesis gate (claim-level preregistered criteria). On the same NIST completeblind event stream, binary CHSH yields S_strict=2.336276 (window 0.0) and S_standard=2.839387 (window 15.0), i.e., Delta=+0.503111 from pairing-rule change alone. Bootstrap (n=2000) gives CI95_strict=[2.295151, 2.378669] and CI95_standard=[2.820420, 2.857413]; because 2sqrt(2)=2.828427 lies inside the standard CI, we do not claim Tsirelson violation. A simulated fallback run also shows that S_standard does not necessarily exceed S_strict, reinforcing protocol sensitivity. We report a method-level, boundary-aware conclusion: Bell/CHSH claims should be interpreted through protocol definitions, uncertainty intervals, and retained negative results, rather than peak statistics alone. Figures may be added in a subsequent version; current evidence is provided via tables and machine-readable result files.
[EN] This deposit reports a merged v6+v7+v8 audit under one locked parameter triplet (mu, rho, eta)=(1.5495, 2.35, 0.08). v6 layer: four-benchmark baseline joint consistency (laser threshold, semiconductor cutoff, MRI Larmor, atomic-clock modes). v7 layer: three-benchmark consistency (double-slit, tunneling, spin-cos2), rigidity scans (27/27 local, 18/20 random-global), counterfactual expected-fail suite, and 7-seed stability. v8 layer: radial levels, decoherence, and Compton-style shift at the same locked point, plus anti-cheat closure (eta counterfactual fail, negative controls fail, multi-round hardening 100/100). The claim is methodological and reproducibility-centered: auditable model-class consistency under declared gates. Not claimed: completed SI mapping, ontology uniqueness, or direct falsification of standard quantum mechanics. Primary manuscript files: papers_final/04_Ripple_Rigidity_Audit/version_01/【0】_Ripple_Rigidity_Audit/files/07_Manuscript_Merged_v7_v8_Bilingual.md papers_final/04_Ripple_Rigidity_Audit/version_01/【0】_Ripple_Rigidity_Audit/files/08_Manuscript_Merged_v7_v8_Bilingual.html [中] 本归档给出 v6+v7+v8 合并审计,在单一锁定参数三元组 (mu, rho, eta)=(1.5495, 2.35, 0.08) 下完成证据闭环。 v6 层:四项基线联合一致性(激光阈值、半导体截止、MRI Larmor、原子钟模态)。 v7 层:三项基准(双缝、隧穿、自旋 cos2)联合一致性,刚性扫描(局部 27/27、全局随机 18/20)、反事实按预期失败、7 种子稳定性。 v8 层:同锁定点下的径向能级、退相干、类 Compton 位移,并带反作弊闭环(eta 反事实失败、负对照失败、多轮加固 100/100)。 本文主张属于方法学与可复现性主张:在声明门槛下具备可审计一致性。 本文不主张:SI 映射已完成、唯一本体论成立、或可据此直接否定标准量子力学。 主要稿件文件: papers_final/04_Ripple_Rigidity_Audit/version_01/【0】_Ripple_Rigidity_Audit/files/07_Manuscript_Merged_v7_v8_Bilingual.md papers_final/04_Ripple_Rigidity_Audit/version_01/【0】_Ripple_Rigidity_Audit/files/08_Manuscript_Merged_v7_v8_Bilingual.html Reproducibility / 可复现 Run from repository root: python scripts/explore/ripple_quantum_tests/generate_v7_paper_figures.py python scripts/explore/ripple_quantum_tests/ripple_quantum_tests_v6_joint.py python scripts/explore/ripple_quantum_tests/ripple_quantum_tests_v8_unify.py --rounds 100 --eta-probe 0.001 python scripts/explore/ripple_quantum_tests/generate_v8_paper_figures.py python scripts/explore/ripple_quantum_tests/build_merged_v7_v8_html.py