From Distinction to Information: An Exact, Parameter-Free and Machine-Closed Derivation of Information Science from Smithian Fold Theory
Information Science Branch Paper 001, version 1.3.0, preserves the complete twelve-law and 77-obligation scientific record while publishing the full-field roadmap and front-loading the exact distinction, entropy, capacity, coding and deterministic-probability results. From the separately admitted Foundation and Mathematics receipts, twelve exact claim grammars execute 11,776 generated candidates and derive symbols and distinguishability; encoding and decoding; information quantity; entropy and uncertainty; compression and redundancy; finite channels and capacity; noise and error; coding; mutual and conditional information; conservation, loss and transformation; classical and probabilistic information; and finite quantum-information support correspondence. Every candidate is decided, each grammar has one all-preserving survivor, and every law passes minimality, uniqueness, depth-independent closure, four adverse-control classes, cryptographic sealing and implementation-distinct reproduction. The headline result is a provenance-retaining information science compatible with superdeterminism. Information is complete deterministic alternative support plus exact retained and closed distinction ledgers. Entropy preserves every observation class, exact positive support part and unresolved pair rather than importing a logarithmic proof value. Probability reports observation-relative unresolved support rather than a stochastic cause. Version 1.3 registers continuing complete-field work across channels, codes, compression, signals, inference, privacy and cross-science information handoffs while preserving every admitted receipt. Paywalls, inaccessible data and black-box oracles close distinctions required for independent falsification; the paper therefore integrates Maria Smith's authorship and the Ernos Labs open-science standard with the technical record.
Institutions
- Fano Labs (China) (CN)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-07-27
- DOI
- https://doi.org/10.5281/zenodo.21627717
- Primary Topic
- Benford’s Law and Fraud Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00