{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BLHE6P2MRZWDI7LHFSYO5KNHXU","short_pith_number":"pith:BLHE6P2M","schema_version":"1.0","canonical_sha256":"0ace4f3f4c8e6c347d672cb0eea9a7bd39de4c0dab20a255cdbdc484d256e690","source":{"kind":"arxiv","id":"1804.01557","version":1},"attestation_state":"computed","paper":{"title":"Qualit\\\"atsma{\\ss}e bin\\\"arer Klassifikationen im Bereich kriminalprognostischer Instrumente der vierten Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CY","authors_text":"Tobias D. Krafft","submitted_at":"2018-04-04T18:27:45Z","abstract_excerpt":"This master's thesis discusses an important issue regarding how algorithmic decision making (ADM) is used in crime forecasting. In America forecasting tools are widely used by judiciary systems for making decisions about risk offenders based on criminal justice for risk offenders. By making use of such tools, the judiciary relies on ADM in order to make error free judgement on offenders. For this purpose, one of the quality measures for machine learning techniques which is widly used, the $AUC$ (area under curve), is compared to and contrasted for results with the $PPV_k$ (positive predictive "},"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":"1804.01557","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2018-04-04T18:27:45Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"7b69126bf0e0021dff41f78365062be18f4f55499708e51fd7dac734a049c595","abstract_canon_sha256":"39e66e11adb029b1fef8c60e68a01f0903716b54f4440b8f687a7c67cb080289"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:14.624212Z","signature_b64":"kpAluFA5mjoRagi7pcoSaxaBOxwJpD3Ol2yQgj4Uy9rtNzp1KD7WCSZDfWCSk/rnp0XcZXNdiKmpiBFEz6HwAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ace4f3f4c8e6c347d672cb0eea9a7bd39de4c0dab20a255cdbdc484d256e690","last_reissued_at":"2026-05-18T00:19:14.623513Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:14.623513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Qualit\\\"atsma{\\ss}e bin\\\"arer Klassifikationen im Bereich kriminalprognostischer Instrumente der vierten Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CY","authors_text":"Tobias D. Krafft","submitted_at":"2018-04-04T18:27:45Z","abstract_excerpt":"This master's thesis discusses an important issue regarding how algorithmic decision making (ADM) is used in crime forecasting. In America forecasting tools are widely used by judiciary systems for making decisions about risk offenders based on criminal justice for risk offenders. By making use of such tools, the judiciary relies on ADM in order to make error free judgement on offenders. For this purpose, one of the quality measures for machine learning techniques which is widly used, the $AUC$ (area under curve), is compared to and contrasted for results with the $PPV_k$ (positive predictive "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.01557","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":"1804.01557","created_at":"2026-05-18T00:19:14.623619+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.01557v1","created_at":"2026-05-18T00:19:14.623619+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.01557","created_at":"2026-05-18T00:19:14.623619+00:00"},{"alias_kind":"pith_short_12","alias_value":"BLHE6P2MRZWD","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"BLHE6P2MRZWDI7LH","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"BLHE6P2M","created_at":"2026-05-18T12:32:16.446611+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/BLHE6P2MRZWDI7LHFSYO5KNHXU","json":"https://pith.science/pith/BLHE6P2MRZWDI7LHFSYO5KNHXU.json","graph_json":"https://pith.science/api/pith-number/BLHE6P2MRZWDI7LHFSYO5KNHXU/graph.json","events_json":"https://pith.science/api/pith-number/BLHE6P2MRZWDI7LHFSYO5KNHXU/events.json","paper":"https://pith.science/paper/BLHE6P2M"},"agent_actions":{"view_html":"https://pith.science/pith/BLHE6P2MRZWDI7LHFSYO5KNHXU","download_json":"https://pith.science/pith/BLHE6P2MRZWDI7LHFSYO5KNHXU.json","view_paper":"https://pith.science/paper/BLHE6P2M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.01557&json=true","fetch_graph":"https://pith.science/api/pith-number/BLHE6P2MRZWDI7LHFSYO5KNHXU/graph.json","fetch_events":"https://pith.science/api/pith-number/BLHE6P2MRZWDI7LHFSYO5KNHXU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BLHE6P2MRZWDI7LHFSYO5KNHXU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BLHE6P2MRZWDI7LHFSYO5KNHXU/action/storage_attestation","attest_author":"https://pith.science/pith/BLHE6P2MRZWDI7LHFSYO5KNHXU/action/author_attestation","sign_citation":"https://pith.science/pith/BLHE6P2MRZWDI7LHFSYO5KNHXU/action/citation_signature","submit_replication":"https://pith.science/pith/BLHE6P2MRZWDI7LHFSYO5KNHXU/action/replication_record"}},"created_at":"2026-05-18T00:19:14.623619+00:00","updated_at":"2026-05-18T00:19:14.623619+00:00"}