{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AXZ2BXY25PPZCE4EJYO3YSRMJS","short_pith_number":"pith:AXZ2BXY2","schema_version":"1.0","canonical_sha256":"05f3a0df1aebdf9113844e1dbc4a2c4c8e962e601c4b28bd61919d2b78fcdf78","source":{"kind":"arxiv","id":"1811.10381","version":1},"attestation_state":"computed","paper":{"title":"Machine learning-based Raman amplifier design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.app-ph","authors_text":"A. Carena, A. Ferrari, D. Zibar, V. Curri","submitted_at":"2018-10-31T10:32:58Z","abstract_excerpt":"A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile."},"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":"1811.10381","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.app-ph","submitted_at":"2018-10-31T10:32:58Z","cross_cats_sorted":[],"title_canon_sha256":"86cc9b5479927148d781c665e5af9b748b0a68d47016b4b88b869ef35fdb59a0","abstract_canon_sha256":"32670b34146ce4d80ec3454cef03f1c3596f9b311944e180036004d1695d08ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:56.627380Z","signature_b64":"dP6yvi/8uCmNOyp65nfHCbMcabtLnsuReNydVDic2gjTAa67RJUl/DpD1ygeRA1O71gFAUOS4eTYl9B1jn1xDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05f3a0df1aebdf9113844e1dbc4a2c4c8e962e601c4b28bd61919d2b78fcdf78","last_reissued_at":"2026-05-17T23:59:56.626780Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:56.626780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine learning-based Raman amplifier design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.app-ph","authors_text":"A. Carena, A. Ferrari, D. Zibar, V. Curri","submitted_at":"2018-10-31T10:32:58Z","abstract_excerpt":"A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10381","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":"1811.10381","created_at":"2026-05-17T23:59:56.626871+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.10381v1","created_at":"2026-05-17T23:59:56.626871+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10381","created_at":"2026-05-17T23:59:56.626871+00:00"},{"alias_kind":"pith_short_12","alias_value":"AXZ2BXY25PPZ","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AXZ2BXY25PPZCE4E","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AXZ2BXY2","created_at":"2026-05-18T12:32:13.499390+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/AXZ2BXY25PPZCE4EJYO3YSRMJS","json":"https://pith.science/pith/AXZ2BXY25PPZCE4EJYO3YSRMJS.json","graph_json":"https://pith.science/api/pith-number/AXZ2BXY25PPZCE4EJYO3YSRMJS/graph.json","events_json":"https://pith.science/api/pith-number/AXZ2BXY25PPZCE4EJYO3YSRMJS/events.json","paper":"https://pith.science/paper/AXZ2BXY2"},"agent_actions":{"view_html":"https://pith.science/pith/AXZ2BXY25PPZCE4EJYO3YSRMJS","download_json":"https://pith.science/pith/AXZ2BXY25PPZCE4EJYO3YSRMJS.json","view_paper":"https://pith.science/paper/AXZ2BXY2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.10381&json=true","fetch_graph":"https://pith.science/api/pith-number/AXZ2BXY25PPZCE4EJYO3YSRMJS/graph.json","fetch_events":"https://pith.science/api/pith-number/AXZ2BXY25PPZCE4EJYO3YSRMJS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AXZ2BXY25PPZCE4EJYO3YSRMJS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AXZ2BXY25PPZCE4EJYO3YSRMJS/action/storage_attestation","attest_author":"https://pith.science/pith/AXZ2BXY25PPZCE4EJYO3YSRMJS/action/author_attestation","sign_citation":"https://pith.science/pith/AXZ2BXY25PPZCE4EJYO3YSRMJS/action/citation_signature","submit_replication":"https://pith.science/pith/AXZ2BXY25PPZCE4EJYO3YSRMJS/action/replication_record"}},"created_at":"2026-05-17T23:59:56.626871+00:00","updated_at":"2026-05-17T23:59:56.626871+00:00"}