{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:M64OZEI3DIIFATWWXFBO3MKQXJ","short_pith_number":"pith:M64OZEI3","schema_version":"1.0","canonical_sha256":"67b8ec911b1a10504ed6b942edb150ba588d8dd294963b3628ab8ee14f09370c","source":{"kind":"arxiv","id":"2404.11341","version":2},"attestation_state":"computed","paper":{"title":"The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME","stat.ML"],"primary_cat":"cs.AI","authors_text":"Jonas Peters, Juan L. Gamella, Peter B\\\"uhlmann","submitted_at":"2024-04-17T13:00:52Z","abstract_excerpt":"In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, which yields limited information about the applicability of the proposed methods to real problems. As a step forward, we have constructed two devices that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems. The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and me"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2404.11341","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-04-17T13:00:52Z","cross_cats_sorted":["cs.LG","stat.ME","stat.ML"],"title_canon_sha256":"4ce151be39308a5e771f2ec4d1cb45a320cb91d4b5a9782782b7f66e904ce7bf","abstract_canon_sha256":"08893d770f18d868bc42dda2851ed04914be2c984dfbbd56fffc5a36cb0c0049"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:59:08.464082Z","signature_b64":"ilFS3HPvDCDnjBOrGt3WdUEv/mk16IVWDV6RlrZz71dcLYy7odYD6m4UtIxJ7HNWiLY5wL7GILJH5lGdC1WgAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67b8ec911b1a10504ed6b942edb150ba588d8dd294963b3628ab8ee14f09370c","last_reissued_at":"2026-07-05T08:59:08.463625Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:59:08.463625Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME","stat.ML"],"primary_cat":"cs.AI","authors_text":"Jonas Peters, Juan L. Gamella, Peter B\\\"uhlmann","submitted_at":"2024-04-17T13:00:52Z","abstract_excerpt":"In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, which yields limited information about the applicability of the proposed methods to real problems. As a step forward, we have constructed two devices that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems. The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.11341","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2404.11341/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2404.11341","created_at":"2026-07-05T08:59:08.463684+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.11341v2","created_at":"2026-07-05T08:59:08.463684+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.11341","created_at":"2026-07-05T08:59:08.463684+00:00"},{"alias_kind":"pith_short_12","alias_value":"M64OZEI3DIIF","created_at":"2026-07-05T08:59:08.463684+00:00"},{"alias_kind":"pith_short_16","alias_value":"M64OZEI3DIIFATWW","created_at":"2026-07-05T08:59:08.463684+00:00"},{"alias_kind":"pith_short_8","alias_value":"M64OZEI3","created_at":"2026-07-05T08:59:08.463684+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05404","citing_title":"Harnessing Generalist Agents for Contextualized Time Series","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M64OZEI3DIIFATWWXFBO3MKQXJ","json":"https://pith.science/pith/M64OZEI3DIIFATWWXFBO3MKQXJ.json","graph_json":"https://pith.science/api/pith-number/M64OZEI3DIIFATWWXFBO3MKQXJ/graph.json","events_json":"https://pith.science/api/pith-number/M64OZEI3DIIFATWWXFBO3MKQXJ/events.json","paper":"https://pith.science/paper/M64OZEI3"},"agent_actions":{"view_html":"https://pith.science/pith/M64OZEI3DIIFATWWXFBO3MKQXJ","download_json":"https://pith.science/pith/M64OZEI3DIIFATWWXFBO3MKQXJ.json","view_paper":"https://pith.science/paper/M64OZEI3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.11341&json=true","fetch_graph":"https://pith.science/api/pith-number/M64OZEI3DIIFATWWXFBO3MKQXJ/graph.json","fetch_events":"https://pith.science/api/pith-number/M64OZEI3DIIFATWWXFBO3MKQXJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M64OZEI3DIIFATWWXFBO3MKQXJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M64OZEI3DIIFATWWXFBO3MKQXJ/action/storage_attestation","attest_author":"https://pith.science/pith/M64OZEI3DIIFATWWXFBO3MKQXJ/action/author_attestation","sign_citation":"https://pith.science/pith/M64OZEI3DIIFATWWXFBO3MKQXJ/action/citation_signature","submit_replication":"https://pith.science/pith/M64OZEI3DIIFATWWXFBO3MKQXJ/action/replication_record"}},"created_at":"2026-07-05T08:59:08.463684+00:00","updated_at":"2026-07-05T08:59:08.463684+00:00"}