{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:I32UBEGP3GJ5JNW7BDUAYY7Y5H","short_pith_number":"pith:I32UBEGP","schema_version":"1.0","canonical_sha256":"46f54090cfd993d4b6df08e80c63f8e9c5082a00ba4fac8ad6fcef037254c378","source":{"kind":"arxiv","id":"2111.07176","version":2},"attestation_state":"computed","paper":{"title":"Information geometry for multiparameter models: New perspectives on the origin of simplicity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.soft","physics.comp-ph","physics.data-an"],"primary_cat":"cond-mat.stat-mech","authors_text":"Benjamin B. Machta, James P. Sethna, Katherine N. Quinn, Mark K. Transtrum, Michael C. Abbott","submitted_at":"2021-11-13T19:20:22Z","abstract_excerpt":"Complex models in physics, biology, economics, and engineering are often sloppy, meaning that the model parameters are not well determined by the model predictions for collective behavior. Many parameter combinations can vary over decades without significant changes in the predictions. This review uses information geometry to explore sloppiness and its deep relation to emergent theories. We introduce the model manifold of predictions, whose coordinates are the model parameters. Its hyperribbon structure explains why only a few parameter combinations matter for the behavior. We review recent ri"},"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":"2111.07176","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2021-11-13T19:20:22Z","cross_cats_sorted":["cond-mat.soft","physics.comp-ph","physics.data-an"],"title_canon_sha256":"84f522351d688fa8ad940ad5e7af7187b6acdcf8289a28d89d64c47f5e10ca20","abstract_canon_sha256":"213f51e57756199c23059e6a78e5f5b5405b3d6d47b7e35beb5762cf0909b510"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:00:14.456891Z","signature_b64":"FHfQJw5PJ0/scZBqzhqYTb9ewKvOWzmGwLmzUK0jJHVgi0LQRklpgQTBw9mXoMaTCvUG5HTK83OW/cUC0RynDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46f54090cfd993d4b6df08e80c63f8e9c5082a00ba4fac8ad6fcef037254c378","last_reissued_at":"2026-07-05T05:00:14.456496Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:00:14.456496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Information geometry for multiparameter models: New perspectives on the origin of simplicity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.soft","physics.comp-ph","physics.data-an"],"primary_cat":"cond-mat.stat-mech","authors_text":"Benjamin B. Machta, James P. Sethna, Katherine N. Quinn, Mark K. Transtrum, Michael C. Abbott","submitted_at":"2021-11-13T19:20:22Z","abstract_excerpt":"Complex models in physics, biology, economics, and engineering are often sloppy, meaning that the model parameters are not well determined by the model predictions for collective behavior. Many parameter combinations can vary over decades without significant changes in the predictions. This review uses information geometry to explore sloppiness and its deep relation to emergent theories. We introduce the model manifold of predictions, whose coordinates are the model parameters. Its hyperribbon structure explains why only a few parameter combinations matter for the behavior. We review recent ri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.07176","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/2111.07176/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":"2111.07176","created_at":"2026-07-05T05:00:14.456553+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.07176v2","created_at":"2026-07-05T05:00:14.456553+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.07176","created_at":"2026-07-05T05:00:14.456553+00:00"},{"alias_kind":"pith_short_12","alias_value":"I32UBEGP3GJ5","created_at":"2026-07-05T05:00:14.456553+00:00"},{"alias_kind":"pith_short_16","alias_value":"I32UBEGP3GJ5JNW7","created_at":"2026-07-05T05:00:14.456553+00:00"},{"alias_kind":"pith_short_8","alias_value":"I32UBEGP","created_at":"2026-07-05T05:00:14.456553+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2206.00578","citing_title":"Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling","ref_index":71,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I32UBEGP3GJ5JNW7BDUAYY7Y5H","json":"https://pith.science/pith/I32UBEGP3GJ5JNW7BDUAYY7Y5H.json","graph_json":"https://pith.science/api/pith-number/I32UBEGP3GJ5JNW7BDUAYY7Y5H/graph.json","events_json":"https://pith.science/api/pith-number/I32UBEGP3GJ5JNW7BDUAYY7Y5H/events.json","paper":"https://pith.science/paper/I32UBEGP"},"agent_actions":{"view_html":"https://pith.science/pith/I32UBEGP3GJ5JNW7BDUAYY7Y5H","download_json":"https://pith.science/pith/I32UBEGP3GJ5JNW7BDUAYY7Y5H.json","view_paper":"https://pith.science/paper/I32UBEGP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.07176&json=true","fetch_graph":"https://pith.science/api/pith-number/I32UBEGP3GJ5JNW7BDUAYY7Y5H/graph.json","fetch_events":"https://pith.science/api/pith-number/I32UBEGP3GJ5JNW7BDUAYY7Y5H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I32UBEGP3GJ5JNW7BDUAYY7Y5H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I32UBEGP3GJ5JNW7BDUAYY7Y5H/action/storage_attestation","attest_author":"https://pith.science/pith/I32UBEGP3GJ5JNW7BDUAYY7Y5H/action/author_attestation","sign_citation":"https://pith.science/pith/I32UBEGP3GJ5JNW7BDUAYY7Y5H/action/citation_signature","submit_replication":"https://pith.science/pith/I32UBEGP3GJ5JNW7BDUAYY7Y5H/action/replication_record"}},"created_at":"2026-07-05T05:00:14.456553+00:00","updated_at":"2026-07-05T05:00:14.456553+00:00"}