{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:CGS6JDHNXBVIR7HXMQGKW7HTPO","short_pith_number":"pith:CGS6JDHN","schema_version":"1.0","canonical_sha256":"11a5e48cedb86a88fcf7640cab7cf37b9210211ee5d0b90cc8b2fb7b6cb03899","source":{"kind":"arxiv","id":"1808.07895","version":1},"attestation_state":"computed","paper":{"title":"Folding a Small Protein Using Harmonic Linear Discriminant Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Dan Mendels, GiovanniMaria Piccini, Michele Parrinello, Yi I. Yang, Z. Faidon Brotzakis","submitted_at":"2018-08-23T18:18:41Z","abstract_excerpt":"Many processes of scientific importance are characterized by time scales that extend far beyond the reach of standard simulation techniques. To circumvent this impediment a plethora of enhanced sampling methods has been developed. One important class of such methods relies on the application of a bias that is function of a set of collective variables specially designed for the problem under consideration. The design of good collective variables can be challenging and thereby constitutes the main bottle neck in the application of these methods. To address this problem, recently we have introduc"},"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":"1808.07895","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-08-23T18:18:41Z","cross_cats_sorted":[],"title_canon_sha256":"77c82390fca8cc2aaa29f88f9831ece09b10eab0b63157760008711684c7dae1","abstract_canon_sha256":"e23d00c15ab5d674dc680f470592439909bb2b70ae17ea699e0f0526f5a0a1e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:14.575585Z","signature_b64":"q2MqJA4lUvtC/A5u0KfJ8voKk17JLVYLbpczbAQz3q8vATgZQfy+RH7rQDKPZVDhSKlIUn3l1do863IfJlpmAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11a5e48cedb86a88fcf7640cab7cf37b9210211ee5d0b90cc8b2fb7b6cb03899","last_reissued_at":"2026-05-17T23:59:14.575110Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:14.575110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Folding a Small Protein Using Harmonic Linear Discriminant Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Dan Mendels, GiovanniMaria Piccini, Michele Parrinello, Yi I. Yang, Z. Faidon Brotzakis","submitted_at":"2018-08-23T18:18:41Z","abstract_excerpt":"Many processes of scientific importance are characterized by time scales that extend far beyond the reach of standard simulation techniques. To circumvent this impediment a plethora of enhanced sampling methods has been developed. One important class of such methods relies on the application of a bias that is function of a set of collective variables specially designed for the problem under consideration. The design of good collective variables can be challenging and thereby constitutes the main bottle neck in the application of these methods. To address this problem, recently we have introduc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07895","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1808.07895","created_at":"2026-05-17T23:59:14.575192+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.07895v1","created_at":"2026-05-17T23:59:14.575192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07895","created_at":"2026-05-17T23:59:14.575192+00:00"},{"alias_kind":"pith_short_12","alias_value":"CGS6JDHNXBVI","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"CGS6JDHNXBVIR7HX","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"CGS6JDHN","created_at":"2026-05-18T12:32:16.446611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.09495","citing_title":"Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators","ref_index":154,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CGS6JDHNXBVIR7HXMQGKW7HTPO","json":"https://pith.science/pith/CGS6JDHNXBVIR7HXMQGKW7HTPO.json","graph_json":"https://pith.science/api/pith-number/CGS6JDHNXBVIR7HXMQGKW7HTPO/graph.json","events_json":"https://pith.science/api/pith-number/CGS6JDHNXBVIR7HXMQGKW7HTPO/events.json","paper":"https://pith.science/paper/CGS6JDHN"},"agent_actions":{"view_html":"https://pith.science/pith/CGS6JDHNXBVIR7HXMQGKW7HTPO","download_json":"https://pith.science/pith/CGS6JDHNXBVIR7HXMQGKW7HTPO.json","view_paper":"https://pith.science/paper/CGS6JDHN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.07895&json=true","fetch_graph":"https://pith.science/api/pith-number/CGS6JDHNXBVIR7HXMQGKW7HTPO/graph.json","fetch_events":"https://pith.science/api/pith-number/CGS6JDHNXBVIR7HXMQGKW7HTPO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CGS6JDHNXBVIR7HXMQGKW7HTPO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CGS6JDHNXBVIR7HXMQGKW7HTPO/action/storage_attestation","attest_author":"https://pith.science/pith/CGS6JDHNXBVIR7HXMQGKW7HTPO/action/author_attestation","sign_citation":"https://pith.science/pith/CGS6JDHNXBVIR7HXMQGKW7HTPO/action/citation_signature","submit_replication":"https://pith.science/pith/CGS6JDHNXBVIR7HXMQGKW7HTPO/action/replication_record"}},"created_at":"2026-05-17T23:59:14.575192+00:00","updated_at":"2026-05-17T23:59:14.575192+00:00"}