{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:L7XBXTLLQB4DCJFQEAENBKH5H2","short_pith_number":"pith:L7XBXTLL","canonical_record":{"source":{"id":"2510.24601","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-10-28T16:28:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e958df59ad9a7d76f2b3487c665c0fd4845e433797617e881af9f6f853326582","abstract_canon_sha256":"d62ae07f6505ddb45565474ce08c8f78f6f611f95ce8e92a39cd46314ab2a50f"},"schema_version":"1.0"},"canonical_sha256":"5fee1bcd6b80783124b02008d0a8fd3ebde4e14d341ec9ceb9530cc734092f16","source":{"kind":"arxiv","id":"2510.24601","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.24601","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"arxiv_version","alias_value":"2510.24601v2","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.24601","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"pith_short_12","alias_value":"L7XBXTLLQB4D","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"pith_short_16","alias_value":"L7XBXTLLQB4DCJFQ","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"pith_short_8","alias_value":"L7XBXTLL","created_at":"2026-06-25T01:17:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:L7XBXTLLQB4DCJFQEAENBKH5H2","target":"record","payload":{"canonical_record":{"source":{"id":"2510.24601","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-10-28T16:28:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e958df59ad9a7d76f2b3487c665c0fd4845e433797617e881af9f6f853326582","abstract_canon_sha256":"d62ae07f6505ddb45565474ce08c8f78f6f611f95ce8e92a39cd46314ab2a50f"},"schema_version":"1.0"},"canonical_sha256":"5fee1bcd6b80783124b02008d0a8fd3ebde4e14d341ec9ceb9530cc734092f16","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T01:17:47.311464Z","signature_b64":"PpMifuym2aYREGxSv4U9/wt4xEfHQkaCSJcJLEsp5VUW9Jqw1b+WdOeyyMGTqEC9Y/rVl/GULNsWWItDBNTDBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fee1bcd6b80783124b02008d0a8fd3ebde4e14d341ec9ceb9530cc734092f16","last_reissued_at":"2026-06-25T01:17:47.311044Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T01:17:47.311044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.24601","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-25T01:17:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GZPHz8XatPBupcmN6HPVpKEwrJ/DzPq5EgKHTWJBXABopbDh/l4hPyAAcvpRQJw1HY9QRFTwYriq2PcyDvqpCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T23:59:52.177170Z"},"content_sha256":"20e9dc556c50d9ac8cb326046fcdeedad28c6b8c9b8ec14a0e714d88bce8d683","schema_version":"1.0","event_id":"sha256:20e9dc556c50d9ac8cb326046fcdeedad28c6b8c9b8ec14a0e714d88bce8d683"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:L7XBXTLLQB4DCJFQEAENBKH5H2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Comparison of generalised additive models and neural networks in applications: A systematic review","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jessica Doohan, Kevin Burke, Lucas Kook","submitted_at":"2025-10-28T16:28:42Z","abstract_excerpt":"Neural networks have become a popular tool in predictive modelling, more commonly associated with machine learning and artificial intelligence than with statistics. Generalised Additive Models (GAMs) are flexible non-linear statistical models that retain interpretability. Both are state-of-the-art in their own right, with their respective advantages and disadvantages. This paper analyses how these two model classes have performed on real-world tabular data. Following PRISMA guidelines, we conducted a systematic review of papers that performed empirical comparisons of GAMs and neural networks. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.24601","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.24601/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-25T01:17:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fgRxCirF5B2b2FUNcPzvYYEWiC8FrRF7SUr4fy0586QcHdK3gn24v78DP4laJMiuFf0ZuvIw4w1GuL1qxELjCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T23:59:52.177562Z"},"content_sha256":"35f05f39a90817f43ea8eb89f1ffe4a12bd288932e4cca1d34998b58cfdb5cb3","schema_version":"1.0","event_id":"sha256:35f05f39a90817f43ea8eb89f1ffe4a12bd288932e4cca1d34998b58cfdb5cb3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L7XBXTLLQB4DCJFQEAENBKH5H2/bundle.json","state_url":"https://pith.science/pith/L7XBXTLLQB4DCJFQEAENBKH5H2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L7XBXTLLQB4DCJFQEAENBKH5H2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-25T23:59:52Z","links":{"resolver":"https://pith.science/pith/L7XBXTLLQB4DCJFQEAENBKH5H2","bundle":"https://pith.science/pith/L7XBXTLLQB4DCJFQEAENBKH5H2/bundle.json","state":"https://pith.science/pith/L7XBXTLLQB4DCJFQEAENBKH5H2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L7XBXTLLQB4DCJFQEAENBKH5H2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:L7XBXTLLQB4DCJFQEAENBKH5H2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d62ae07f6505ddb45565474ce08c8f78f6f611f95ce8e92a39cd46314ab2a50f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-10-28T16:28:42Z","title_canon_sha256":"e958df59ad9a7d76f2b3487c665c0fd4845e433797617e881af9f6f853326582"},"schema_version":"1.0","source":{"id":"2510.24601","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.24601","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"arxiv_version","alias_value":"2510.24601v2","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.24601","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"pith_short_12","alias_value":"L7XBXTLLQB4D","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"pith_short_16","alias_value":"L7XBXTLLQB4DCJFQ","created_at":"2026-06-25T01:17:47Z"},{"alias_kind":"pith_short_8","alias_value":"L7XBXTLL","created_at":"2026-06-25T01:17:47Z"}],"graph_snapshots":[{"event_id":"sha256:35f05f39a90817f43ea8eb89f1ffe4a12bd288932e4cca1d34998b58cfdb5cb3","target":"graph","created_at":"2026-06-25T01:17:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2510.24601/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural networks have become a popular tool in predictive modelling, more commonly associated with machine learning and artificial intelligence than with statistics. Generalised Additive Models (GAMs) are flexible non-linear statistical models that retain interpretability. Both are state-of-the-art in their own right, with their respective advantages and disadvantages. This paper analyses how these two model classes have performed on real-world tabular data. Following PRISMA guidelines, we conducted a systematic review of papers that performed empirical comparisons of GAMs and neural networks. ","authors_text":"Jessica Doohan, Kevin Burke, Lucas Kook","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-10-28T16:28:42Z","title":"Comparison of generalised additive models and neural networks in applications: A systematic review"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.24601","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:20e9dc556c50d9ac8cb326046fcdeedad28c6b8c9b8ec14a0e714d88bce8d683","target":"record","created_at":"2026-06-25T01:17:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d62ae07f6505ddb45565474ce08c8f78f6f611f95ce8e92a39cd46314ab2a50f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-10-28T16:28:42Z","title_canon_sha256":"e958df59ad9a7d76f2b3487c665c0fd4845e433797617e881af9f6f853326582"},"schema_version":"1.0","source":{"id":"2510.24601","kind":"arxiv","version":2}},"canonical_sha256":"5fee1bcd6b80783124b02008d0a8fd3ebde4e14d341ec9ceb9530cc734092f16","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5fee1bcd6b80783124b02008d0a8fd3ebde4e14d341ec9ceb9530cc734092f16","first_computed_at":"2026-06-25T01:17:47.311044Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-25T01:17:47.311044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PpMifuym2aYREGxSv4U9/wt4xEfHQkaCSJcJLEsp5VUW9Jqw1b+WdOeyyMGTqEC9Y/rVl/GULNsWWItDBNTDBA==","signature_status":"signed_v1","signed_at":"2026-06-25T01:17:47.311464Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.24601","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:20e9dc556c50d9ac8cb326046fcdeedad28c6b8c9b8ec14a0e714d88bce8d683","sha256:35f05f39a90817f43ea8eb89f1ffe4a12bd288932e4cca1d34998b58cfdb5cb3"],"state_sha256":"9770e71ccab4add6963b74ad0f15958f58582cb7923362eb135e2a90389a5f28"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X+7ayuZglj63VSKCjkVHFFtw5hgttynBHpdp6Twza7lIsbVJBAc1JIxOSud7NDa1pFaDJgcqQdMyn6XUgBPQDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-25T23:59:52.179555Z","bundle_sha256":"f5e068df5736075004f446f7214b8ccb3a76cadf46c345833706136f2af1186d"}}