{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TI53G3ADVMAUHB4JVPAHGMICHP","short_pith_number":"pith:TI53G3AD","schema_version":"1.0","canonical_sha256":"9a3bb36c03ab01438789abc07331023bd8a45d1238592b799748ea4de76dc3aa","source":{"kind":"arxiv","id":"1803.06226","version":2},"attestation_state":"computed","paper":{"title":"Glyph: Symbolic Regression Tools","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","math.OC","physics.data-an"],"primary_cat":"cs.MS","authors_text":"Julien Gout, Markus Abel, Markus Quade","submitted_at":"2018-03-13T21:57:49Z","abstract_excerpt":"We present Glyph - a Python package for genetic programming based symbolic regression. Glyph is designed for usage let by numerical simulations let by real world experiments. For experimentalists, glyph-remote provides a separation of tasks: a ZeroMQ interface splits the genetic programming optimization task from the evaluation of an experimental (or numerical) run. Glyph can be accessed at http://github.com/ambrosys/glyph . Domain experts are be able to employ symbolic regression in their experiments with ease, even if they are not expert programmers. The reuse potential is kept high by a gen"},"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":"1803.06226","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MS","submitted_at":"2018-03-13T21:57:49Z","cross_cats_sorted":["cs.NE","math.OC","physics.data-an"],"title_canon_sha256":"54d0ea1b59d636a2a0d2df93307d301879f403a52641c8830391bf5e06302809","abstract_canon_sha256":"b2fa2803b9ebdece2472026061a38e10b45b482ede74a1d1ef807f854d4aa92d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:29.708846Z","signature_b64":"8e2EqVlWpZdUHUtle4jWz5aFCCi3I97l7pl06ID7FYspI2OQLFZQmzurHyXRhWQmD3Nzj7XPUcjGtRmDY7LqAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a3bb36c03ab01438789abc07331023bd8a45d1238592b799748ea4de76dc3aa","last_reissued_at":"2026-05-18T00:20:29.708136Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:29.708136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Glyph: Symbolic Regression Tools","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","math.OC","physics.data-an"],"primary_cat":"cs.MS","authors_text":"Julien Gout, Markus Abel, Markus Quade","submitted_at":"2018-03-13T21:57:49Z","abstract_excerpt":"We present Glyph - a Python package for genetic programming based symbolic regression. Glyph is designed for usage let by numerical simulations let by real world experiments. For experimentalists, glyph-remote provides a separation of tasks: a ZeroMQ interface splits the genetic programming optimization task from the evaluation of an experimental (or numerical) run. Glyph can be accessed at http://github.com/ambrosys/glyph . Domain experts are be able to employ symbolic regression in their experiments with ease, even if they are not expert programmers. The reuse potential is kept high by a gen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.06226","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":""},"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":"1803.06226","created_at":"2026-05-18T00:20:29.708262+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.06226v2","created_at":"2026-05-18T00:20:29.708262+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.06226","created_at":"2026-05-18T00:20:29.708262+00:00"},{"alias_kind":"pith_short_12","alias_value":"TI53G3ADVMAU","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"TI53G3ADVMAUHB4J","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"TI53G3AD","created_at":"2026-05-18T12:32:53.628368+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TI53G3ADVMAUHB4JVPAHGMICHP","json":"https://pith.science/pith/TI53G3ADVMAUHB4JVPAHGMICHP.json","graph_json":"https://pith.science/api/pith-number/TI53G3ADVMAUHB4JVPAHGMICHP/graph.json","events_json":"https://pith.science/api/pith-number/TI53G3ADVMAUHB4JVPAHGMICHP/events.json","paper":"https://pith.science/paper/TI53G3AD"},"agent_actions":{"view_html":"https://pith.science/pith/TI53G3ADVMAUHB4JVPAHGMICHP","download_json":"https://pith.science/pith/TI53G3ADVMAUHB4JVPAHGMICHP.json","view_paper":"https://pith.science/paper/TI53G3AD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.06226&json=true","fetch_graph":"https://pith.science/api/pith-number/TI53G3ADVMAUHB4JVPAHGMICHP/graph.json","fetch_events":"https://pith.science/api/pith-number/TI53G3ADVMAUHB4JVPAHGMICHP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TI53G3ADVMAUHB4JVPAHGMICHP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TI53G3ADVMAUHB4JVPAHGMICHP/action/storage_attestation","attest_author":"https://pith.science/pith/TI53G3ADVMAUHB4JVPAHGMICHP/action/author_attestation","sign_citation":"https://pith.science/pith/TI53G3ADVMAUHB4JVPAHGMICHP/action/citation_signature","submit_replication":"https://pith.science/pith/TI53G3ADVMAUHB4JVPAHGMICHP/action/replication_record"}},"created_at":"2026-05-18T00:20:29.708262+00:00","updated_at":"2026-05-18T00:20:29.708262+00:00"}