{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ZAVTT2ATENFS3HFIRWLBBYAJLO","short_pith_number":"pith:ZAVTT2AT","canonical_record":{"source":{"id":"2411.15592","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-23T15:51:15Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f9d413576c1c0646057ec113148bc5f671ab866c8dec3cc29a12d1ce916ab821","abstract_canon_sha256":"4a7d77d6065a546d0a3ee03529a7e53f2f342d31ef11748e47d12cac63b7933a"},"schema_version":"1.0"},"canonical_sha256":"c82b39e813234b2d9ca88d9610e0095b95f24973d719840627efe55913779621","source":{"kind":"arxiv","id":"2411.15592","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.15592","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"arxiv_version","alias_value":"2411.15592v2","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15592","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"pith_short_12","alias_value":"ZAVTT2ATENFS","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"pith_short_16","alias_value":"ZAVTT2ATENFS3HFI","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"pith_short_8","alias_value":"ZAVTT2AT","created_at":"2026-07-05T09:40:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ZAVTT2ATENFS3HFIRWLBBYAJLO","target":"record","payload":{"canonical_record":{"source":{"id":"2411.15592","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-23T15:51:15Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f9d413576c1c0646057ec113148bc5f671ab866c8dec3cc29a12d1ce916ab821","abstract_canon_sha256":"4a7d77d6065a546d0a3ee03529a7e53f2f342d31ef11748e47d12cac63b7933a"},"schema_version":"1.0"},"canonical_sha256":"c82b39e813234b2d9ca88d9610e0095b95f24973d719840627efe55913779621","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:21.737901Z","signature_b64":"6WDirmncm9Fir166ZdSTF9t+jBhFhWeZIKXIP88vUdAbxl0JN7JB7lNHTLMOiMjNBNf3S9o39Y430eFD0mHiDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c82b39e813234b2d9ca88d9610e0095b95f24973d719840627efe55913779621","last_reissued_at":"2026-07-05T09:40:21.737430Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:21.737430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.15592","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":"2026-07-05T09:40:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WAUYKMQfh9gOUkWQgYc6e6OgsXDmHvl6ZnHjqqJGu9GHW5h4gX4WmO8W9+Lx3Pi0lATOBub/gMn1wTtvcHE9Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T23:24:06.726311Z"},"content_sha256":"cda3c678abb4154fb0c833d93a3d33e85941c5245a5da428da0eacd85d3b9725","schema_version":"1.0","event_id":"sha256:cda3c678abb4154fb0c833d93a3d33e85941c5245a5da428da0eacd85d3b9725"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ZAVTT2ATENFS3HFIRWLBBYAJLO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Adithya B. Somaraj, Buddhadev Goswami, Nirmal Punjabi, Prantar Chakrabarti, Ravindra Gudi","submitted_at":"2024-11-23T15:51:15Z","abstract_excerpt":"Hematological disorders, which involve a variety of malignant conditions and genetic diseases affecting blood formation, present significant diagnostic challenges. One such major challenge in clinical settings is differentiating Erythroblast from WBCs. Our approach evaluates the efficacy of various machine learning (ML) classifiers$\\unicode{x2014}$SVM, XG-Boost, KNN, and Random Forest$\\unicode{x2014}$using the ResNet-50 deep learning model as a backbone in detecting and differentiating erythroblast blood smear images across training splits of different sizes. Our findings indicate that the Res"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15592","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/2411.15592/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":"2026-07-05T09:40:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TDbsA0vxSW0Fz2qRFoH6g1fNUzHeRVM8t2PR7fGZlu45HB9AldRlrVWlRrSP83gleyJB/MgKiK9wClp/NXj6Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T23:24:06.727251Z"},"content_sha256":"7a4f587abd55cb6e91eaf81d493ce3bf736163b0536e09beeb6dc5ca2dfe4754","schema_version":"1.0","event_id":"sha256:7a4f587abd55cb6e91eaf81d493ce3bf736163b0536e09beeb6dc5ca2dfe4754"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZAVTT2ATENFS3HFIRWLBBYAJLO/bundle.json","state_url":"https://pith.science/pith/ZAVTT2ATENFS3HFIRWLBBYAJLO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZAVTT2ATENFS3HFIRWLBBYAJLO/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":"2026-08-12T23:24:06Z","links":{"resolver":"https://pith.science/pith/ZAVTT2ATENFS3HFIRWLBBYAJLO","bundle":"https://pith.science/pith/ZAVTT2ATENFS3HFIRWLBBYAJLO/bundle.json","state":"https://pith.science/pith/ZAVTT2ATENFS3HFIRWLBBYAJLO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZAVTT2ATENFS3HFIRWLBBYAJLO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZAVTT2ATENFS3HFIRWLBBYAJLO","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":"4a7d77d6065a546d0a3ee03529a7e53f2f342d31ef11748e47d12cac63b7933a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-23T15:51:15Z","title_canon_sha256":"f9d413576c1c0646057ec113148bc5f671ab866c8dec3cc29a12d1ce916ab821"},"schema_version":"1.0","source":{"id":"2411.15592","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.15592","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"arxiv_version","alias_value":"2411.15592v2","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15592","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"pith_short_12","alias_value":"ZAVTT2ATENFS","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"pith_short_16","alias_value":"ZAVTT2ATENFS3HFI","created_at":"2026-07-05T09:40:21Z"},{"alias_kind":"pith_short_8","alias_value":"ZAVTT2AT","created_at":"2026-07-05T09:40:21Z"}],"graph_snapshots":[{"event_id":"sha256:7a4f587abd55cb6e91eaf81d493ce3bf736163b0536e09beeb6dc5ca2dfe4754","target":"graph","created_at":"2026-07-05T09:40:21Z","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/2411.15592/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hematological disorders, which involve a variety of malignant conditions and genetic diseases affecting blood formation, present significant diagnostic challenges. One such major challenge in clinical settings is differentiating Erythroblast from WBCs. Our approach evaluates the efficacy of various machine learning (ML) classifiers$\\unicode{x2014}$SVM, XG-Boost, KNN, and Random Forest$\\unicode{x2014}$using the ResNet-50 deep learning model as a backbone in detecting and differentiating erythroblast blood smear images across training splits of different sizes. Our findings indicate that the Res","authors_text":"Adithya B. Somaraj, Buddhadev Goswami, Nirmal Punjabi, Prantar Chakrabarti, Ravindra Gudi","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-23T15:51:15Z","title":"Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15592","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:cda3c678abb4154fb0c833d93a3d33e85941c5245a5da428da0eacd85d3b9725","target":"record","created_at":"2026-07-05T09:40:21Z","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":"4a7d77d6065a546d0a3ee03529a7e53f2f342d31ef11748e47d12cac63b7933a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-23T15:51:15Z","title_canon_sha256":"f9d413576c1c0646057ec113148bc5f671ab866c8dec3cc29a12d1ce916ab821"},"schema_version":"1.0","source":{"id":"2411.15592","kind":"arxiv","version":2}},"canonical_sha256":"c82b39e813234b2d9ca88d9610e0095b95f24973d719840627efe55913779621","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c82b39e813234b2d9ca88d9610e0095b95f24973d719840627efe55913779621","first_computed_at":"2026-07-05T09:40:21.737430Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:40:21.737430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6WDirmncm9Fir166ZdSTF9t+jBhFhWeZIKXIP88vUdAbxl0JN7JB7lNHTLMOiMjNBNf3S9o39Y430eFD0mHiDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:40:21.737901Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.15592","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cda3c678abb4154fb0c833d93a3d33e85941c5245a5da428da0eacd85d3b9725","sha256:7a4f587abd55cb6e91eaf81d493ce3bf736163b0536e09beeb6dc5ca2dfe4754"],"state_sha256":"62e7cabf1e5eb4fde9499cd116343a2e88f6807a715c9b1b94919514fb756bdc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1mBEOe42cyf/gCPTeZkzIuOjhgTt3g8evCvIqGgmuzddKL/+SFsGXlamninFbx5BkW64Z8mQLkz7uuOwBTTkBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T23:24:06.734554Z","bundle_sha256":"f6d150f18d4fd38b1e47c96ce7c4282d2c23fb8e4f5aa71354a1ee9fc2d3ab09"}}