{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2005:52BOOY5I6FMANWGY7UZEC2QS6V","short_pith_number":"pith:52BOOY5I","canonical_record":{"source":{"id":"math/0507421","kind":"arxiv","version":1},"metadata":{"license":"","primary_cat":"math.ST","submitted_at":"2005-07-21T07:46:05Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"154554fd6b54374fba4656de2e9cc042ba78a3494070e384404c415a493ddbdb","abstract_canon_sha256":"965ca4b5fb6e1b18dbef161448f3ddef9cc00451ef952d9341641993c899262b"},"schema_version":"1.0"},"canonical_sha256":"ee82e763a8f15806d8d8fd32416a12f576b0f1ec48d5b66fa550b73b5b48e031","source":{"kind":"arxiv","id":"math/0507421","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"math/0507421","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"arxiv_version","alias_value":"math/0507421v1","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.math/0507421","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"pith_short_12","alias_value":"52BOOY5I6FMA","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"pith_short_16","alias_value":"52BOOY5I6FMANWGY","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"pith_short_8","alias_value":"52BOOY5I","created_at":"2026-07-04T15:01:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2005:52BOOY5I6FMANWGY7UZEC2QS6V","target":"record","payload":{"canonical_record":{"source":{"id":"math/0507421","kind":"arxiv","version":1},"metadata":{"license":"","primary_cat":"math.ST","submitted_at":"2005-07-21T07:46:05Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"154554fd6b54374fba4656de2e9cc042ba78a3494070e384404c415a493ddbdb","abstract_canon_sha256":"965ca4b5fb6e1b18dbef161448f3ddef9cc00451ef952d9341641993c899262b"},"schema_version":"1.0"},"canonical_sha256":"ee82e763a8f15806d8d8fd32416a12f576b0f1ec48d5b66fa550b73b5b48e031","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T15:01:58.043328Z","signature_b64":"ESreibxze7bat/cTJJFOF1wAiHKAPt/3qffgGvmLdOCKbsam6Nfb/92wfY9LtfK35ea7pE1hag8aD8+AKl1zAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee82e763a8f15806d8d8fd32416a12f576b0f1ec48d5b66fa550b73b5b48e031","last_reissued_at":"2026-07-04T15:01:58.042927Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T15:01:58.042927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"math/0507421","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":"2026-07-04T15:01:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x8C3MW0TmpTuL2dczXbFOtXgvG0VwsKjCppBnRF/oL19SnLvbFUJG0xOCXqbXbF8sHdkH490gSwmOrMA3DJCDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:07:17.665942Z"},"content_sha256":"275c3c55fd7162f01dfcaebb7c298954e6c047f3fbf29fcce0c2ea64e2cc8ced","schema_version":"1.0","event_id":"sha256:275c3c55fd7162f01dfcaebb7c298954e6c047f3fbf29fcce0c2ea64e2cc8ced"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2005:52BOOY5I6FMANWGY7UZEC2QS6V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical testing designs for pattern recognition","license":"","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Donald Geman, Gilles Blanchard","submitted_at":"2005-07-21T07:46:05Z","abstract_excerpt":"We explore the theoretical foundations of a ``twenty questions'' approach to pattern recognition. The object of the analysis is the computational process itself rather than probability distributions (Bayesian inference) or decision boundaries (statistical learning). Our formulation is motivated by applications to scene interpretation in which there are a great many possible explanations for the data, one (``background'') is statistically dominant, and it is imperative to restrict intensive computation to genuinely ambiguous regions. The focus here is then on pattern filtering: Given a large se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"math/0507421","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/math/0507421/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":"2026-07-04T15:01:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lwtPXAHlCKeNE598v37Vgpc/APCDJiocKDn+L2Z6k2R5FxzNW9OLfHtcIWCJoE2sXc6WMEW2oINxTsAMJkjeAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:07:17.666435Z"},"content_sha256":"3b7d836d91e3a740e48277755353e1c55392206a421573e0604d9f6ccca0e3f6","schema_version":"1.0","event_id":"sha256:3b7d836d91e3a740e48277755353e1c55392206a421573e0604d9f6ccca0e3f6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/52BOOY5I6FMANWGY7UZEC2QS6V/bundle.json","state_url":"https://pith.science/pith/52BOOY5I6FMANWGY7UZEC2QS6V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/52BOOY5I6FMANWGY7UZEC2QS6V/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":"2026-08-03T17:07:17Z","links":{"resolver":"https://pith.science/pith/52BOOY5I6FMANWGY7UZEC2QS6V","bundle":"https://pith.science/pith/52BOOY5I6FMANWGY7UZEC2QS6V/bundle.json","state":"https://pith.science/pith/52BOOY5I6FMANWGY7UZEC2QS6V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/52BOOY5I6FMANWGY7UZEC2QS6V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2005:52BOOY5I6FMANWGY7UZEC2QS6V","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":"965ca4b5fb6e1b18dbef161448f3ddef9cc00451ef952d9341641993c899262b","cross_cats_sorted":["stat.TH"],"license":"","primary_cat":"math.ST","submitted_at":"2005-07-21T07:46:05Z","title_canon_sha256":"154554fd6b54374fba4656de2e9cc042ba78a3494070e384404c415a493ddbdb"},"schema_version":"1.0","source":{"id":"math/0507421","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"math/0507421","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"arxiv_version","alias_value":"math/0507421v1","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.math/0507421","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"pith_short_12","alias_value":"52BOOY5I6FMA","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"pith_short_16","alias_value":"52BOOY5I6FMANWGY","created_at":"2026-07-04T15:01:58Z"},{"alias_kind":"pith_short_8","alias_value":"52BOOY5I","created_at":"2026-07-04T15:01:58Z"}],"graph_snapshots":[{"event_id":"sha256:3b7d836d91e3a740e48277755353e1c55392206a421573e0604d9f6ccca0e3f6","target":"graph","created_at":"2026-07-04T15:01:58Z","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/math/0507421/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We explore the theoretical foundations of a ``twenty questions'' approach to pattern recognition. The object of the analysis is the computational process itself rather than probability distributions (Bayesian inference) or decision boundaries (statistical learning). Our formulation is motivated by applications to scene interpretation in which there are a great many possible explanations for the data, one (``background'') is statistically dominant, and it is imperative to restrict intensive computation to genuinely ambiguous regions. The focus here is then on pattern filtering: Given a large se","authors_text":"Donald Geman, Gilles Blanchard","cross_cats":["stat.TH"],"headline":"","license":"","primary_cat":"math.ST","submitted_at":"2005-07-21T07:46:05Z","title":"Hierarchical testing designs for pattern recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"math/0507421","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":"sha256:275c3c55fd7162f01dfcaebb7c298954e6c047f3fbf29fcce0c2ea64e2cc8ced","target":"record","created_at":"2026-07-04T15:01:58Z","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":"965ca4b5fb6e1b18dbef161448f3ddef9cc00451ef952d9341641993c899262b","cross_cats_sorted":["stat.TH"],"license":"","primary_cat":"math.ST","submitted_at":"2005-07-21T07:46:05Z","title_canon_sha256":"154554fd6b54374fba4656de2e9cc042ba78a3494070e384404c415a493ddbdb"},"schema_version":"1.0","source":{"id":"math/0507421","kind":"arxiv","version":1}},"canonical_sha256":"ee82e763a8f15806d8d8fd32416a12f576b0f1ec48d5b66fa550b73b5b48e031","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee82e763a8f15806d8d8fd32416a12f576b0f1ec48d5b66fa550b73b5b48e031","first_computed_at":"2026-07-04T15:01:58.042927Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T15:01:58.042927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ESreibxze7bat/cTJJFOF1wAiHKAPt/3qffgGvmLdOCKbsam6Nfb/92wfY9LtfK35ea7pE1hag8aD8+AKl1zAw==","signature_status":"signed_v1","signed_at":"2026-07-04T15:01:58.043328Z","signed_message":"canonical_sha256_bytes"},"source_id":"math/0507421","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:275c3c55fd7162f01dfcaebb7c298954e6c047f3fbf29fcce0c2ea64e2cc8ced","sha256:3b7d836d91e3a740e48277755353e1c55392206a421573e0604d9f6ccca0e3f6"],"state_sha256":"f33918e56b906c71651fe238ce7f5be88bfd8f0b091f051733800eeed79a11aa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TPa9Y0mY1rZ3X4Ddxv4I0vztScIf0F/1ZmFSve0t3SN6lCEtqsNPqxuLMCC+BuBKXXqHBJMxTMCRTmczc9Q0Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T17:07:17.670225Z","bundle_sha256":"6fcd2629f484d219f6e3ec2d7917dd694ea57bf905d9bf0b189933ac7eb434fc"}}