{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TQLWIRFOKNTOGMSGTGPR4NMMCQ","short_pith_number":"pith:TQLWIRFO","schema_version":"1.0","canonical_sha256":"9c176444ae5366e33246999f1e358c1419c48eb1348338b73049743d6ff7f383","source":{"kind":"arxiv","id":"2607.07863","version":1},"attestation_state":"computed","paper":{"title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"J\\\"org Frochte, Sarah Grewe","submitted_at":"2026-07-08T18:54:09Z","abstract_excerpt":"In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of physics integration can matter as much as the learning algorithm. Using an abrasive waterjet milling dataset ($n{=}155$, Inconel\\,718), we make three methodological contributions. First, we separate physics-based data \\emph{cleaning} from statistical \\emph{curation} and treat the latter as competing modelling hypotheses rather than silent preprocessing. Second, we find that model rankings from a 15-point hold-out set c"},"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":"2607.07863","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-08T18:54:09Z","cross_cats_sorted":[],"title_canon_sha256":"d129279915a1c17090fde86d90aef5d779fa9b15df82523ad1e863f63d30f0d5","abstract_canon_sha256":"9e90497366569087740465911c07383bfe6a2346594934107e5c7b053ba08308"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-10T00:19:03.287309Z","signature_b64":"C7XZKggYq5tWuUcFsR0k8BH3dDb1WcHNhm54wzYXQMe68AAgg4E2rR3jHS2vOq9SRlXEbOgBLZxxee8oJlidDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c176444ae5366e33246999f1e358c1419c48eb1348338b73049743d6ff7f383","last_reissued_at":"2026-07-10T00:19:03.286962Z","signature_status":"signed_v1","first_computed_at":"2026-07-10T00:19:03.286962Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"J\\\"org Frochte, Sarah Grewe","submitted_at":"2026-07-08T18:54:09Z","abstract_excerpt":"In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of physics integration can matter as much as the learning algorithm. Using an abrasive waterjet milling dataset ($n{=}155$, Inconel\\,718), we make three methodological contributions. First, we separate physics-based data \\emph{cleaning} from statistical \\emph{curation} and treat the latter as competing modelling hypotheses rather than silent preprocessing. Second, we find that model rankings from a 15-point hold-out set c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.07863","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.07863/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":"2607.07863","created_at":"2026-07-10T00:19:03.287028+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.07863v1","created_at":"2026-07-10T00:19:03.287028+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.07863","created_at":"2026-07-10T00:19:03.287028+00:00"},{"alias_kind":"pith_short_12","alias_value":"TQLWIRFOKNTO","created_at":"2026-07-10T00:19:03.287028+00:00"},{"alias_kind":"pith_short_16","alias_value":"TQLWIRFOKNTOGMSG","created_at":"2026-07-10T00:19:03.287028+00:00"},{"alias_kind":"pith_short_8","alias_value":"TQLWIRFO","created_at":"2026-07-10T00:19:03.287028+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/TQLWIRFOKNTOGMSGTGPR4NMMCQ","json":"https://pith.science/pith/TQLWIRFOKNTOGMSGTGPR4NMMCQ.json","graph_json":"https://pith.science/api/pith-number/TQLWIRFOKNTOGMSGTGPR4NMMCQ/graph.json","events_json":"https://pith.science/api/pith-number/TQLWIRFOKNTOGMSGTGPR4NMMCQ/events.json","paper":"https://pith.science/paper/TQLWIRFO"},"agent_actions":{"view_html":"https://pith.science/pith/TQLWIRFOKNTOGMSGTGPR4NMMCQ","download_json":"https://pith.science/pith/TQLWIRFOKNTOGMSGTGPR4NMMCQ.json","view_paper":"https://pith.science/paper/TQLWIRFO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.07863&json=true","fetch_graph":"https://pith.science/api/pith-number/TQLWIRFOKNTOGMSGTGPR4NMMCQ/graph.json","fetch_events":"https://pith.science/api/pith-number/TQLWIRFOKNTOGMSGTGPR4NMMCQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TQLWIRFOKNTOGMSGTGPR4NMMCQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TQLWIRFOKNTOGMSGTGPR4NMMCQ/action/storage_attestation","attest_author":"https://pith.science/pith/TQLWIRFOKNTOGMSGTGPR4NMMCQ/action/author_attestation","sign_citation":"https://pith.science/pith/TQLWIRFOKNTOGMSGTGPR4NMMCQ/action/citation_signature","submit_replication":"https://pith.science/pith/TQLWIRFOKNTOGMSGTGPR4NMMCQ/action/replication_record"}},"created_at":"2026-07-10T00:19:03.287028+00:00","updated_at":"2026-07-10T00:19:03.287028+00:00"}