{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SV5R5G527XJNDNKQW2DQDJQTJT","short_pith_number":"pith:SV5R5G52","canonical_record":{"source":{"id":"2410.09068","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T07:48:16Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"18dc77dfff0fc30eefa402b73b2e2e80aa949b8da65aa01095ce800cd7c3225c","abstract_canon_sha256":"597b2150b23f2b37fef81d956714ea0df04dc548a90c63319303267ff8caba19"},"schema_version":"1.0"},"canonical_sha256":"957b1e9bbafdd2d1b550b68701a6134cf224cdcc902fbdff01fd616040bcb5ae","source":{"kind":"arxiv","id":"2410.09068","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.09068","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"arxiv_version","alias_value":"2410.09068v1","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09068","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"pith_short_12","alias_value":"SV5R5G527XJN","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"pith_short_16","alias_value":"SV5R5G527XJNDNKQ","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"pith_short_8","alias_value":"SV5R5G52","created_at":"2026-07-05T09:19:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SV5R5G527XJNDNKQW2DQDJQTJT","target":"record","payload":{"canonical_record":{"source":{"id":"2410.09068","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T07:48:16Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"18dc77dfff0fc30eefa402b73b2e2e80aa949b8da65aa01095ce800cd7c3225c","abstract_canon_sha256":"597b2150b23f2b37fef81d956714ea0df04dc548a90c63319303267ff8caba19"},"schema_version":"1.0"},"canonical_sha256":"957b1e9bbafdd2d1b550b68701a6134cf224cdcc902fbdff01fd616040bcb5ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:19:33.597801Z","signature_b64":"LLFiVmJgY8yHE72jPO0WHq6rlCMm7mgz5CK6YrZ2ks1RCXUITHp8G/M+JNmwGSQigSpNoqeXXg4kB0N7Uoq5Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"957b1e9bbafdd2d1b550b68701a6134cf224cdcc902fbdff01fd616040bcb5ae","last_reissued_at":"2026-07-05T09:19:33.597336Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:19:33.597336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.09068","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:19:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UGrI2MIisH/1wcB98Jki/wEivRUm8vyRMRhDi8Qji4qacXAnFCK1xcBtOl4D07sSgYJ4A3xn/aXd0WCxPN5vCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T20:37:42.057381Z"},"content_sha256":"7ad2b66f5849f775fea21833d7c1d6bd194c0960230edff9297f47414f1a4954","schema_version":"1.0","event_id":"sha256:7ad2b66f5849f775fea21833d7c1d6bd194c0960230edff9297f47414f1a4954"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SV5R5G527XJNDNKQW2DQDJQTJT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling and Prediction of the UEFA EURO 2024 via Combined Statistical Learning Approaches","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.LG","authors_text":"Achim Zeileis, Andreas Groll, Christophe Ley, Gunther Schauberger, Jonas Sternemann, Lars M. Hvattum","submitted_at":"2024-10-01T07:48:16Z","abstract_excerpt":"In this work, three fundamentally different machine learning models are combined to create a new, joint model for forecasting the UEFA EURO 2024. Therefore, a generalized linear model, a random forest model, and a extreme gradient boosting model are used to predict the number of goals a team scores in a match. The three models are trained on the match results of the UEFA EUROs 2004-2020, with additional covariates characterizing the teams for each tournament as well as three enhanced variables derived from different ranking methods for football teams. The first enhanced variable is based on hi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09068","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/2410.09068/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":"2026-07-05T09:19:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dpljs09WryeKWJWsT0/cUsfvmQIjce4pdDb01DJ/7QdlXSYqXD2oO2Or2VA99G+ukG4GZ9YDVi0XnvaMhUzIDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T20:37:42.057888Z"},"content_sha256":"9ced0df392590b6137b1e9e2bd9bec07c896720aebce0b9784f2b6ed35bd0397","schema_version":"1.0","event_id":"sha256:9ced0df392590b6137b1e9e2bd9bec07c896720aebce0b9784f2b6ed35bd0397"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SV5R5G527XJNDNKQW2DQDJQTJT/bundle.json","state_url":"https://pith.science/pith/SV5R5G527XJNDNKQW2DQDJQTJT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SV5R5G527XJNDNKQW2DQDJQTJT/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":"2026-08-23T20:37:42Z","links":{"resolver":"https://pith.science/pith/SV5R5G527XJNDNKQW2DQDJQTJT","bundle":"https://pith.science/pith/SV5R5G527XJNDNKQW2DQDJQTJT/bundle.json","state":"https://pith.science/pith/SV5R5G527XJNDNKQW2DQDJQTJT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SV5R5G527XJNDNKQW2DQDJQTJT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SV5R5G527XJNDNKQW2DQDJQTJT","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":"597b2150b23f2b37fef81d956714ea0df04dc548a90c63319303267ff8caba19","cross_cats_sorted":["stat.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T07:48:16Z","title_canon_sha256":"18dc77dfff0fc30eefa402b73b2e2e80aa949b8da65aa01095ce800cd7c3225c"},"schema_version":"1.0","source":{"id":"2410.09068","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.09068","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"arxiv_version","alias_value":"2410.09068v1","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09068","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"pith_short_12","alias_value":"SV5R5G527XJN","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"pith_short_16","alias_value":"SV5R5G527XJNDNKQ","created_at":"2026-07-05T09:19:33Z"},{"alias_kind":"pith_short_8","alias_value":"SV5R5G52","created_at":"2026-07-05T09:19:33Z"}],"graph_snapshots":[{"event_id":"sha256:9ced0df392590b6137b1e9e2bd9bec07c896720aebce0b9784f2b6ed35bd0397","target":"graph","created_at":"2026-07-05T09:19:33Z","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/2410.09068/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, three fundamentally different machine learning models are combined to create a new, joint model for forecasting the UEFA EURO 2024. Therefore, a generalized linear model, a random forest model, and a extreme gradient boosting model are used to predict the number of goals a team scores in a match. The three models are trained on the match results of the UEFA EUROs 2004-2020, with additional covariates characterizing the teams for each tournament as well as three enhanced variables derived from different ranking methods for football teams. The first enhanced variable is based on hi","authors_text":"Achim Zeileis, Andreas Groll, Christophe Ley, Gunther Schauberger, Jonas Sternemann, Lars M. Hvattum","cross_cats":["stat.AP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T07:48:16Z","title":"Modeling and Prediction of the UEFA EURO 2024 via Combined Statistical Learning Approaches"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09068","kind":"arxiv","version":1},"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":"sha256:7ad2b66f5849f775fea21833d7c1d6bd194c0960230edff9297f47414f1a4954","target":"record","created_at":"2026-07-05T09:19:33Z","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":"597b2150b23f2b37fef81d956714ea0df04dc548a90c63319303267ff8caba19","cross_cats_sorted":["stat.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-01T07:48:16Z","title_canon_sha256":"18dc77dfff0fc30eefa402b73b2e2e80aa949b8da65aa01095ce800cd7c3225c"},"schema_version":"1.0","source":{"id":"2410.09068","kind":"arxiv","version":1}},"canonical_sha256":"957b1e9bbafdd2d1b550b68701a6134cf224cdcc902fbdff01fd616040bcb5ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"957b1e9bbafdd2d1b550b68701a6134cf224cdcc902fbdff01fd616040bcb5ae","first_computed_at":"2026-07-05T09:19:33.597336Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:19:33.597336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LLFiVmJgY8yHE72jPO0WHq6rlCMm7mgz5CK6YrZ2ks1RCXUITHp8G/M+JNmwGSQigSpNoqeXXg4kB0N7Uoq5Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:19:33.597801Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.09068","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7ad2b66f5849f775fea21833d7c1d6bd194c0960230edff9297f47414f1a4954","sha256:9ced0df392590b6137b1e9e2bd9bec07c896720aebce0b9784f2b6ed35bd0397"],"state_sha256":"7e855a721a7cecd84c8420ee669734f16192a1a23ac66f99be44a14c6f39834c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JyS3OG+7Lm2LIUGfCLgnmtVoRlGVxDHva6hMgBdcz+5tNl59KKzVF7VKNuBxF+uD7zjTuT8abaFvDTFA/TqjDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T20:37:42.062586Z","bundle_sha256":"08671560afab6696a683ebb052337462d1750a789c0244b694ed6178e24a1532"}}