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Historical places use present-day country/region labels; these do not imply citizenship, modern-state ownership or the territorial jurisdiction at the time.", "source_url": "https://tile.loc.gov/storage-services/service/gdc/gdcfindingaidpdfs/ms003071/ms003071.pdf", "target": { "record_id": "urn:uuid:ec6fd80b-f6ab-49b7-ae73-4f273c8d646e", "version": 1, "content_hash": "sha256:d92d1cb6eee3a6277069333b35ceaa50d9b2f45460f3906c68bd0356f319516e" }, "basis": "RESEARCH_LOCATION_OR_PAPER_AFFILIATION", "reviewed_on": "2026-09-26", "nationality_inferred": false, "sources": [ { "title": "Attribution source", "url": "https://tile.loc.gov/storage-services/service/gdc/gdcfindingaidpdfs/ms003071/ms003071.pdf" } ], "coverage_status": "COMPLETE", "country_labels": { "US": "United States" } }, "points": "450000", "published_grade": { "id": "shannon", "entry_id": "shannon", "band": "S", "modifier": null, "target": { "record_id": "urn:uuid:ec6fd80b-f6ab-49b7-ae73-4f273c8d646e", "version": 1, "content_hash": "sha256:d92d1cb6eee3a6277069333b35ceaa50d9b2f45460f3906c68bd0356f319516e" }, "what_changed": "Compression and reliable communication could be studied through common mathematical limits.", "scope": "The 1948 framework for information measures, coding and communication limits under stated source/channel models.", "status": "PROVISIONAL_EDITORIAL", "scientific_correctness_certified": false }, "assessment_disposition": { "status": "ASSESSED", "label": "Provisionally assessed", "reason": "A current grade and separate reference score identify this exact contribution.", "target": { "record_id": "urn:uuid:ec6fd80b-f6ab-49b7-ae73-4f273c8d646e", "version": 1, "content_hash": "sha256:d92d1cb6eee3a6277069333b35ceaa50d9b2f45460f3906c68bd0356f319516e" }, "edition": "ratings-e039106f30dec177" } }, "version": { "kind": "PUBLIC_RECORD_VIEW", "previous": null, "published_at": "2026-09-11T00:00:00Z", "publisher": { "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33", "role": "WRS_EDITORIAL_PUBLICATION" }, "record": { "contributors": [ { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Claude Shannon", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "formulator" ], "source": { "citation": "Shannon, A Mathematical Theory of Communication, Bell System Technical Journal 27 (1948)", "url": "https://www.nokia.com/bell-labs/about/history/innovation-stories/bell-labs-technical-journal/" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Shannon, A Mathematical Theory of Communication, Bell System Technical Journal 27 (1948)", "url": "https://www.nokia.com/bell-labs/about/history/innovation-stories/bell-labs-technical-journal/" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "The probability models, asymptotic conditions and channels of each coding theorem must be stated.", "domain": "信息论" }, "sources": [ { "citation": "Shannon, A Mathematical Theory of Communication, Bell System Technical Journal 27 (1948)", "url": "https://www.nokia.com/bell-labs/about/history/innovation-stories/bell-labs-technical-journal/" } ], "statement": "# Shannon: A Mathematical Theory of Communication\n\n## The question\n\nHow much information can a communication system carry, and how can a message survive noise? Shannon's 1948 framework treats sources, channels and coding mathematically. It separates the engineering problem of conveying a selected message from the message's meaning. That distinction makes it possible to study shared limits across very different systems, rather than giving each telephone, alphabet or transmission method a wholly separate theory.\n\n## The idea\n\nA probabilistic source has patterns and uncertainty. Coding can use those patterns when representing messages. A noisy channel has limits on the rate at which information can be conveyed reliably under a stated model. These are mathematical statements about specified sources and channels, with asymptotic and coding assumptions. A word such as information does not automatically give a metric for truth, intelligence, usefulness or scientific importance.\n\n## Why it matters\n\nThe contribution provides a common language for compression and communication limits. It helps distinguish an inefficient implementation from a fundamental constraint of a model. WRS's S grade concerns the 1948 information-theory contribution. Practical error-correcting codes have their own records and contributors; Hamming's constructive coding work is not absorbed into Shannon's authorship. The reference score is WRS's provisional historical estimate, not a measurement in bits reported by the original paper.\n\n## A teaching example\n\nImagine a toy sender choosing A half the time, B a quarter, and C and D one eighth each. A bit is one binary digit: 0 or 1. A fixed code needs two bits per symbol. Could common symbols use fewer? Try A = 0, B = 10, C = 110, D = 111. No complete code begins another, so the receiver can tell where each symbol ends. Over eight example messages with exactly those proportions, four A symbols use four bits; two B symbols use four; C and D use three each. The total is fourteen bits for eight symbols: 1.75 bits per symbol, instead of two. This invented classroom example shows how probability can shorten an average message. It assumes noiseless transmission; it does not protect against flipped bits or measure a message's meaning.\n\n## Scope and a useful next step\n\nRead the source and channel descriptions in the linked institutional reprint, then inspect the hypotheses of any theorem you want to use. A bounded contribution might compare two explicit source distributions or reproduce one small coding example. State the alphabet, probabilities, noise model and quantity being measured. A demonstration on short messages does not establish an asymptotic coding theorem, and reliable transmission of a false statement does not make that statement true.\n\n## Scientific scope\n\nThe probability models, asymptotic conditions and channels of each coding theorem must be stated.\n\n## Sources for this explanation\n\n- [Shannon: A Mathematical Theory of Communication, institutional reprint](https://www.princeton.edu/~wbialek/rome/refs/shannon_48.pdf): Introduction and source/channel models; theorem assumptions distinguish coding limits from semantic meaning.\n", "summary": "Uses entropy and channel capacity to describe limits on compression and reliable communication.", "title": "Shannon: A Mathematical Theory of Communication" }, "record_id": "urn:uuid:ec6fd80b-f6ab-49b7-ae73-4f273c8d646e", "version": 1, "source_ref": { "record_id": "urn:uuid:ec6fd80b-f6ab-49b7-ae73-4f273c8d646e", "version": 1, "content_hash": "sha256:d92d1cb6eee3a6277069333b35ceaa50d9b2f45460f3906c68bd0356f319516e" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:d3a4d747291de2b69a84daf6f02dcce37de549d50b0514bdc733b2d285977aa3" }, "target": { "record_id": "urn:uuid:ec6fd80b-f6ab-49b7-ae73-4f273c8d646e", "version": 1, "content_hash": "sha256:d92d1cb6eee3a6277069333b35ceaa50d9b2f45460f3906c68bd0356f319516e" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "shannon", "entry_id": "shannon", "revision": 1, "title": "Shannon: A Mathematical Theory of Communication", "context": "Mathematical information and communication theory", "track": "Computing theory", "band": "S", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-09-13", "evidence_cutoff": "2026-09-13", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Compression and reliable communication could be studied through common mathematical limits.", "scope": "The 1948 framework for information measures, coding and communication limits under stated source/channel models.", "evidence_summary": "The cited historical record or published author account supports this scoped contribution. The source-reading boundary is stated at each link; a WRS grade is not an independent scientific validation.", "references": [ { "title": "Shannon 1948, corrected reprint", "url": "https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf", "locator": "Introduction and coding/channel framework; selected reading, not a complete proof audit" }, { "title": "Bell Labs original publication record", "url": "https://www.nokia.com/bell-labs/publications-and-media/publications/a-mathematical-theory-of-communication/", "locator": "Original introduction and publication identity" } ], "roles": [ { "who": "Claude Shannon", "credit": "SCIENCE", "did": "Information-theoretic framework." }, { "who": "Harry Nyquist; Ralph Hartley", "credit": "SCIENCE", "did": "Acknowledged foundations of the communication problem." } ], "target": { "record_id": "urn:uuid:ec6fd80b-f6ab-49b7-ae73-4f273c8d646e", "version": 1, "content_hash": "sha256:d92d1cb6eee3a6277069333b35ceaa50d9b2f45460f3906c68bd0356f319516e" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "450000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": { "id": "shannon", "sections": [ { "title": "The question", "text": "How much information can a communication system carry, and how can a message survive noise? Shannon's 1948 framework treats sources, channels and coding mathematically. It separates the engineering problem of conveying a selected message from the message's meaning. That distinction makes it possible to study shared limits across very different systems, rather than giving each telephone, alphabet or transmission method a wholly separate theory." }, { "title": "The idea", "text": "A probabilistic source has patterns and uncertainty. Coding can use those patterns when representing messages. A noisy channel has limits on the rate at which information can be conveyed reliably under a stated model. These are mathematical statements about specified sources and channels, with asymptotic and coding assumptions. A word such as information does not automatically give a metric for truth, intelligence, usefulness or scientific importance." }, { "title": "Why it matters", "text": "The contribution provides a common language for compression and communication limits. It helps distinguish an inefficient implementation from a fundamental constraint of a model. WRS's S grade concerns the 1948 information-theory contribution. Practical error-correcting codes have their own records and contributors; Hamming's constructive coding work is not absorbed into Shannon's authorship. The reference score is WRS's provisional historical estimate, not a measurement in bits reported by the original paper." }, { "title": "A teaching example", "text": "Imagine a toy sender choosing A half the time, B a quarter, and C and D one eighth each. A bit is one binary digit: 0 or 1. A fixed code needs two bits per symbol. Could common symbols use fewer? Try A = 0, B = 10, C = 110, D = 111. No complete code begins another, so the receiver can tell where each symbol ends. Over eight example messages with exactly those proportions, four A symbols use four bits; two B symbols use four; C and D use three each. The total is fourteen bits for eight symbols: 1.75 bits per symbol, instead of two. This invented classroom example shows how probability can shorten an average message. It assumes noiseless transmission; it does not protect against flipped bits or measure a message's meaning." }, { "title": "Scope and a useful next step", "text": "Read the source and channel descriptions in the linked institutional reprint, then inspect the hypotheses of any theorem you want to use. A bounded contribution might compare two explicit source distributions or reproduce one small coding example. State the alphabet, probabilities, noise model and quantity being measured. A demonstration on short messages does not establish an asymptotic coding theorem, and reliable transmission of a false statement does not make that statement true." } ], "references": [ { "title": "Shannon: A Mathematical Theory of Communication, institutional reprint", "url": "https://www.princeton.edu/~wbialek/rome/refs/shannon_48.pdf", "locator": "Introduction and source/channel models; theorem assumptions distinguish coding limits from semantic meaning." } ], "target": { "record_id": "urn:uuid:ec6fd80b-f6ab-49b7-ae73-4f273c8d646e", "version": 1, "content_hash": "sha256:d92d1cb6eee3a6277069333b35ceaa50d9b2f45460f3906c68bd0356f319516e" } }, "reading_status": "EXPLANATORY_GUIDE" }