{
 "@context": "https://schema.org",
 "@type": "Claim",
 "@id": "https://wulfkaal.github.io/positions/2026-08-08-329",
 "identifier": "kaal:position:2026-08-08-329",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Production Systems Require Evidence Beyond Academic Design",
 "text": "Paleyes, Urma, and Lawrence distinguish academic machine learning from deployment in a real-world production system. Their survey identifies challenges at every stage of the deployment workflow. This supports treating a proposed production system as a separate evidentiary object. It does not validate Kaal's registered forward program, the Agentic Reputation Substrate, or any production implementation.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "datePublished": "2026-08-08",
 "dateModified": "2026-08-08",
 "creativeWorkStatus": "Affirmed",
 "responseType": "qualification",
 "keywords": [
  "research-methods",
  "institutional-design",
  "risk-and-incentives",
  "scholarly-growth-coverage",
  "scholarly-literature",
  "ml-deployment",
  "production-systems",
  "evidence-provenance",
  "reproducibility"
 ],
 "scope_conditions": [
  "The response is limited to the exact arXiv author-manuscript passages and the one mapped Kaal claim.",
  "External evidence level: complete peer-reviewed author manuscript from arXiv with a concordant Crossref journal record and exact page 1 passages.",
  "Mapping review tier: independent substantive scholarly-growth qualification.",
  "The survey does not examine Kaal's registered forward program, Agentic Reputation Substrate, or any production implementation.",
  "It does not establish that Kaal's specific forward program is properly registered or that any proposed production design will work.",
  "The relationship is limited to the production-system evidence boundary.",
  "Crossref Spanish, Semantic Scholar search, and four mapped Semantic Scholar records returned HTTP 429. No rate-limited response was promoted."
 ],
 "currentDebate": {
  "name": "Challenges in Deploying Machine Learning: A Survey of Case Studies",
  "url": "https://doi.org/10.1145/3533378"
 },
 "extends": {
  "identifier": "kaal:claim:7261018-033",
  "url": "https://wulfkaal.github.io/claims/7261018-033",
  "citation": "Wulf A. Kaal, Empirical Evaluation of the Agentic Reputation Substrate: Deliberation, the Composition of Error, and the Registered Measurement of Agency Costs in a Controlled Multi-Model Cohort (2026). SSRN: https://ssrn.com/abstract=7261018",
  "paper": "Wulf A. Kaal, Empirical Evaluation of the Agentic Reputation Substrate: Deliberation, the Composition of Error, and the Registered Measurement of Agency Costs in a Controlled Multi-Model Cohort",
  "authors": [
   "Wulf A. Kaal"
  ],
  "year": "2026",
  "ssrn": "https://ssrn.com/abstract=7261018",
  "source_pdf_sha256": "1d6cbe544bd0055133f7cf8ff308be4fde955867bd8dc764992b8d516af15fa8"
 },
 "isBasedOn": [
  {
   "@id": "https://wulfkaal.github.io/claims/7261018-033"
  },
  {
   "@type": "CreativeWork",
   "name": "Challenges in Deploying Machine Learning: A Survey of Case Studies",
   "url": "https://doi.org/10.1145/3533378"
  }
 ],
 "batch_id": "kaal-review:2026-08-12:scholarly-growth-7261018-033-reviewed-v1",
 "review_provenance": "https://wulfkaal.github.io/positions/by-claim/7261018-033.html",
 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
 "isPartOf": {
  "@id": "https://wulfkaal.github.io/positions/index.json"
 },
 "version": "1.0",
 "canonical_url": "https://wulfkaal.github.io/positions/2026-08-08-329",
 "canonicalForm": "https://wulfkaal.github.io/positions/2026-08-08-329.md",
 "candidateId": "kaal:response-candidate:2026-08-12:scholarly-growth-7261018-033-paleyes-urma-lawrence-01",
 "evidenceLevel": "complete peer-reviewed author manuscript from arXiv with a concordant Crossref journal record and exact page 1 passages",
 "reviewTier": "independent substantive scholarly-growth qualification",
 "mappingConfidence": 0.97,
 "mappingAmbiguous": false,
 "mappingMethod": "independent substantive scholarly-growth one-to-one review",
 "mappingWhyRelevant": "The survey directly distinguishes academic machine-learning work from the requirements and evidence of a real-world production system.",
 "sourceProvenance": {
  "source": "peer-reviewed ACM Computing Surveys article with complete arXiv author manuscript",
  "sourceRecordId": "doi:10.1145/3533378",
  "canonicalUrl": "https://doi.org/10.1145/3533378",
  "fullTextUrl": "https://arxiv.org/pdf/2011.09926",
  "retrievedAt": "2026-08-13T03:09:53.621Z",
  "crossrefRecordSha256": "ebc5ff083efb0824b16830d6e9768d708f67fd49e2e4e0e7eb3a3af2cf1f3675",
  "arxivMetadataSha256": "df3f11915bbe16e47f26a7df448e42566b5ece390a229c3cf7a62f4d6dc9747a",
  "authorManuscriptSha256": "d943c38c9a145e7db7455d3915e7ccf36fd81727228fd23c3d4125b58caebc68",
  "extractedTextSha256": "3820076686f9d445658d695bf904f61960054057ceabb284801c9b6dc965b328",
  "textExtraction": {
   "tool": "pdftotext -layout",
   "quality": "complete readable 29-page author manuscript with exact proposition-bearing passages"
  },
  "sourceProposition": "Paleyes, Urma, and Lawrence survey reports of deploying machine learning across production use cases. They distinguish what works in an academic setting from what a real-world system requires. They also identify challenges at every stage of the deployment process. The survey supports treating production deployment as an evidentiary object distinct from a prospective academic design.",
  "sourcePropositionSha256": "8511237b7e427141bd70923805742ef7c02b70a9ec8874a96d5e425c78e514de",
  "sourceEvidenceSetSha256": "b510dcdb9087bb4665d528a19653af83d124c8472b1b23944784d4fa03cc256e",
  "sourceEvidencePassages": [
   {
    "text": "Just as with any other field, there are significant differences between what works in an academic setting and what is required by a real world system.",
    "locator": {
     "source": "arXiv author manuscript v3",
     "arxiv": "2011.09926",
     "pdfPage": 1,
     "section": "Introduction",
     "fullTextSha256": "d943c38c9a145e7db7455d3915e7ccf36fd81727228fd23c3d4125b58caebc68",
     "extractedTextSha256": "3820076686f9d445658d695bf904f61960054057ceabb284801c9b6dc965b328"
    },
    "sha256": "76968d6e300d1b3fe696dcd61c3b3f11df9a42717f56180814262da68a1e1de7"
   },
   {
    "text": "By mapping found challenges to the steps of the machine learning deployment workflow we show that practitioners face issues at each stage of the deployment process.",
    "locator": {
     "source": "arXiv author manuscript v3",
     "arxiv": "2011.09926",
     "pdfPage": 1,
     "section": "Abstract",
     "fullTextSha256": "d943c38c9a145e7db7455d3915e7ccf36fd81727228fd23c3d4125b58caebc68",
     "extractedTextSha256": "3820076686f9d445658d695bf904f61960054057ceabb284801c9b6dc965b328"
    },
    "sha256": "207af6a155201fd1c70298159d46b81470e2c222deab4b770a5cf1fed228c39e"
   }
  ],
  "workId": "work:doi:10.1145/3533378",
  "workAuthors": [
   "Andrei Paleyes",
   "Raoul-Gabriel Urma",
   "Neil D. Lawrence"
  ],
  "workPublishedAt": "2022-12-07",
  "identityKeys": [
   "doi:10.1145/3533378",
   "arxiv:2011.09926",
   "crossref:ebc5ff083efb0824b16830d6e9768d708f67fd49e2e4e0e7eb3a3af2cf1f3675",
   "proposition:8511237b7e427141bd70923805742ef7c02b70a9ec8874a96d5e425c78e514de"
  ],
  "claimMappings": [
   {
    "claimId": "kaal:claim:7261018-033",
    "claimUrl": "https://wulfkaal.github.io/claims/7261018-033",
    "rank": 1,
    "confidence": 0.97,
    "method": "independent substantive scholarly-growth one-to-one review",
    "whyRelevant": "The survey directly distinguishes academic machine-learning work from the requirements and evidence of a real-world production system.",
    "ambiguous": false
   }
  ],
  "substantiveReview": {
   "reviewedAt": "2026-08-13T03:15:09.334Z",
   "sourceIdentityVerified": true,
   "authorIndependenceVerified": true,
   "canonicalPublicStatusVerified": true,
   "retractionOrSupersessionFound": false,
   "propositionFidelityVerified": true,
   "mechanismCorrespondence": "academic model work does not supply the requirements, integration, monitoring, and validation evidence of a real-world production system",
   "compatibleScope": "external methodological qualification limited to machine-learning deployment and production systems",
   "responseWordingDefensible": true,
   "oneToOneExtendsMapping": true,
   "sameSourceDistinctionVerified": true,
   "exactSupportingQuotesVerified": true,
   "limitations": [
    "The survey does not examine Kaal's registered forward program, Agentic Reputation Substrate, or any production implementation.",
    "It does not establish that Kaal's specific forward program is properly registered or that any proposed production design will work.",
    "The relationship is limited to the production-system evidence boundary.",
    "Crossref Spanish, Semantic Scholar search, and four mapped Semantic Scholar records returned HTTP 429. No rate-limited response was promoted."
   ]
  },
  "liveVerification": {
   "checkedAt": "2026-08-13T03:16:17.223Z",
   "crossrefHttpStatus": 200,
   "crossrefResponseSha256": "ebc5ff083efb0824b16830d6e9768d708f67fd49e2e4e0e7eb3a3af2cf1f3675",
   "arxivHttpStatus": 200,
   "authorManuscriptSha256": "d943c38c9a145e7db7455d3915e7ccf36fd81727228fd23c3d4125b58caebc68",
   "frozenAuthorManuscriptSha256": "d943c38c9a145e7db7455d3915e7ccf36fd81727228fd23c3d4125b58caebc68"
  }
 },
 "userAffirmation": "Automatically authorized under standing authority receipt kaal-standing-publication-authorization:2026-08-01:hourly-reviewed-batches, SHA-256 e2126054b58bb4e88db65c334ef4fc8ae78dcaadc63543c408133c4eefceb9b1, limited to this substantively reviewed scholarly-growth qualification and the protected scholarly claims matching the current bridge checkpoint under the current owner instruction.",
 "sha256": "7f883bdee594f2c6852aefbb2fc9a793a803edf89516e9e94cd50ccd0b442bb9"
}
