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Chapter 1 — Bundle Overview & Scope of Use
I. Purpose & Scope
- Define the composition, boundaries, and acceptance rules of the Minimal Reproducible Bundle (MRB), covering environment lock, data snapshot & lineage, weights & parameters, freshness policy, scripts & commands, metrics & intervals, gate mapping, and release manifests.
- Applicable to local/container/cloud modes; for model training/inference reproduction, offline evaluation, and online regression verification.
- For path quantities (arrival time/phase), explicitly show gamma(ell) and d ell in text, and record delta_form ∈ {general, factored} on the data side; parenthesize all expressions; publication requires p_dim = 1.0 with check_dim_report.json.
II. Minimal Bundle
- Environment lock: env_lock.json, container_spec.yaml (HW/OS/driver/framework/image/clock source).
- Data snapshot: data_refs.yaml, split_manifest.json (source/Schema/splits/checksums/license).
- Weights & params: weights_manifest.yaml, param_lock.yaml (signatures/versions/freshness/cov_group).
- Training & inference: train_config.yaml, inference_openapi.yaml, inference.proto, binding_spec.md.
- UQ & error budget: model_uq.yaml, uq_summary.json, budget_breakdown.csv (choose exactly one coverage mode: k/alpha/quantile across the volume).
- Evaluation & scoring: bench_plan.yaml, scorecard.json, eval_report.md.
- Monitoring & rollback: monitoring_rules.yaml, rollback_fsm.yaml, alerts.jsonl.
- Reports & signatures: check_dim_report.json, validate_report.json, audit.jsonl, report_manifest.yaml, SIGNATURE.asc.
Root: PTN_EXPORT/; register sha256 and signatures for all artifacts.
III. Scope of Use & Limits
- In-scope: engineering/research reproduction, comparative evaluation, and release verification aligned with Dataset/Model/Error/Pipeline/Parameter cards.
- Limits: if path blocks (gamma/measure/delta_form) are missing, clock_state!="locked", p_dim < 1, or cross-volume coverage modes disagree, mark [Restricted] and allow qualitative checks only.
IV. Dependency Matrix
Category | Dependency | Version/Anchor | Notes |
|---|---|---|---|
Data | Dataset Card Ch.3/4/6/7/8/10/11/12 | see[] (anchor coverage ≥ 90%) | Provenance/Schema/Splits/QC/UQ/API/Bench |
Model | Model Card Ch.6/7/8/10/11/12 | Training/UQ/Bench/Interfaces/Performance/Monitoring | Align training & deployment |
Error | Error Budget Card Ch.5/6/8/9 | Coverage & covariance | Delta/MC/Bootstrap & threshold mapping |
Pipeline | Pipeline Card Ch.3/4/6/9/12 | DAG/Contracts/Stages/Gates/Release | Runtime & release consistency |
Params | Parameter Card Ch.4/6/8/9/10/11 | Units/Freshness/Cov groups/Interfaces | Versioning & traceability |
V. Versioning & Signatures
- Use SemVer: MAJOR (breaking: schema/units/dimensions/control equations), MINOR (backward-compatible additions), PATCH (fixes).
- Record checksum and signature for each artifact; fix rollback points with *-lock tags; public references v1.* only.
VI. Gate Mapping
- G1 Schema completeness | G2 Citation compliance (anchor coverage ≥ 90%) | G3 Path conventions (gamma/measure/delta_form present; step compliant) | G4 Dimensional closure (p_dim = 1.0) | G5 Freshness (clock_state="locked") | G6 Coverage consistency (k/alpha/quantile) | G7 Covariance consistency (Σ PD) | G8 Uniqueness & acyclicity.
- Trigger S1–S5 (dimension/freshness/path/covariance/citation) → reject acceptance or tag [Restricted].
VII. Normative Path Forms
- Arrival (two equivalent):
T_arr = ( 1 / c_ref ) * ( ( ∫ n_eff d ell ) )
T_arr = ( ( ∫ ( n_eff / c_ref ) d ell ) ) - Phase:
Phi = ( ( 2π / λ_ref ) * ( ∫ n_eff d ell ) )
Before reproduction, align time → path → phase; arrays len(gamma_ell)=len(d_ell)=len(n_eff)≥2; record delta_form on the data side.
VIII. Machine-Readable Index
# repro_manifest.yaml (example)
version: "1.0.0"
entrypoints:
train: "reproduce.sh train"
infer: "reproduce.sh infer"
eval: "reproduce.sh eval"
artifacts:
env: ["env_lock.json","container_spec.yaml"]
data: ["data_refs.yaml","split_manifest.json"]
model: ["train_config.yaml","weights_manifest.yaml","param_lock.yaml"]
api: ["inference_openapi.yaml","inference.proto","binding_spec.md"]
uq: ["model_uq.yaml","uq_summary.json","budget_breakdown.csv"]
bench: ["bench_plan.yaml","scorecard.json","eval_report.md"]
monitor: ["monitoring_rules.yaml","rollback_fsm.yaml","alerts.jsonl"]
reports: ["check_dim_report.json","validate_report.json","audit.jsonl","report_manifest.yaml","SIGNATURE.asc"]
checksums:
sha256: "checksums.txt"
IX. Execution & Acceptance
- Execute: complete train → infer → eval → compare in local/container/cloud modes; log to audit.jsonl.
- Acceptance: metrics/intervals match manifests; /validate passes G1–G8; all artifacts’ checksums/signatures match; non-compliances handled as [Restricted] with diagnostics.
Copyright & License (CC BY 4.0)
Copyright: Unless otherwise noted, the copyright of “Energy Filament Theory” (text, charts, illustrations, symbols, and formulas) belongs to the author “Guanglin Tu”.
License: This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). You may copy, redistribute, excerpt, adapt, and share for commercial or non‑commercial purposes with proper attribution.
Suggested attribution: Author: “Guanglin Tu”; Work: “Energy Filament Theory”; Source: energyfilament.org; License: CC BY 4.0.
First published: 2025-11-11|Current version:v5.1
License link:https://creativecommons.org/licenses/by/4.0/