{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PINYGDQIRAITQTE6UB2PDDYR2U","short_pith_number":"pith:PINYGDQI","schema_version":"1.0","canonical_sha256":"7a1b830e088811384c9ea074f18f11d52908d93a4a6ab85dab1c73966024d1ec","source":{"kind":"arxiv","id":"1802.08894","version":1},"attestation_state":"computed","paper":{"title":"Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"q-bio.QM","authors_text":"(2) Politecnico di Torino, Carolina W\\\"ahlby ((1) Centre for Image Analysis, Gabriele Partel (1), Giorgia Milli (2), Italy), Sweden, Uppsala University","submitted_at":"2018-02-24T18:53:56Z","abstract_excerpt":"Image-based sequencing of mRNA makes it possible to see where in a tissue sample a given gene is active, and thus discern large numbers of different cell types in parallel. This is crucial for gaining a better understanding of tissue development and disease such as cancer. Signals are collected over multiple staining and imaging cycles, and signal density together with noise makes signal decoding challenging. Previous approaches have led to low signal recall in efforts to maintain high sensitivity. We propose an approach where signal candidates are generously included, and true-signal probabil"},"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":"1802.08894","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2018-02-24T18:53:56Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"b1b5d6891daba7b57103d25b27664205b88214b69e59a383751a3d3fc61fc4db","abstract_canon_sha256":"3caf5451ae43afdf843d2f03e552a992582e2c42505c41a1153290c351676379"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:36.129353Z","signature_b64":"q6LF9Kn3HPIatEvzMbRQ2Jh2FRdDvr+beoz8ti7raap1LCAXhTqE8oEMMd5z/CxdCjSfpXOopN3TRc/G8FDeDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a1b830e088811384c9ea074f18f11d52908d93a4a6ab85dab1c73966024d1ec","last_reissued_at":"2026-05-18T00:22:36.128609Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:36.128609Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"q-bio.QM","authors_text":"(2) Politecnico di Torino, Carolina W\\\"ahlby ((1) Centre for Image Analysis, Gabriele Partel (1), Giorgia Milli (2), Italy), Sweden, Uppsala University","submitted_at":"2018-02-24T18:53:56Z","abstract_excerpt":"Image-based sequencing of mRNA makes it possible to see where in a tissue sample a given gene is active, and thus discern large numbers of different cell types in parallel. This is crucial for gaining a better understanding of tissue development and disease such as cancer. Signals are collected over multiple staining and imaging cycles, and signal density together with noise makes signal decoding challenging. Previous approaches have led to low signal recall in efforts to maintain high sensitivity. We propose an approach where signal candidates are generously included, and true-signal probabil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08894","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":"1802.08894","created_at":"2026-05-18T00:22:36.128720+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.08894v1","created_at":"2026-05-18T00:22:36.128720+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08894","created_at":"2026-05-18T00:22:36.128720+00:00"},{"alias_kind":"pith_short_12","alias_value":"PINYGDQIRAIT","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"PINYGDQIRAITQTE6","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"PINYGDQI","created_at":"2026-05-18T12:32:43.782077+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/PINYGDQIRAITQTE6UB2PDDYR2U","json":"https://pith.science/pith/PINYGDQIRAITQTE6UB2PDDYR2U.json","graph_json":"https://pith.science/api/pith-number/PINYGDQIRAITQTE6UB2PDDYR2U/graph.json","events_json":"https://pith.science/api/pith-number/PINYGDQIRAITQTE6UB2PDDYR2U/events.json","paper":"https://pith.science/paper/PINYGDQI"},"agent_actions":{"view_html":"https://pith.science/pith/PINYGDQIRAITQTE6UB2PDDYR2U","download_json":"https://pith.science/pith/PINYGDQIRAITQTE6UB2PDDYR2U.json","view_paper":"https://pith.science/paper/PINYGDQI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.08894&json=true","fetch_graph":"https://pith.science/api/pith-number/PINYGDQIRAITQTE6UB2PDDYR2U/graph.json","fetch_events":"https://pith.science/api/pith-number/PINYGDQIRAITQTE6UB2PDDYR2U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PINYGDQIRAITQTE6UB2PDDYR2U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PINYGDQIRAITQTE6UB2PDDYR2U/action/storage_attestation","attest_author":"https://pith.science/pith/PINYGDQIRAITQTE6UB2PDDYR2U/action/author_attestation","sign_citation":"https://pith.science/pith/PINYGDQIRAITQTE6UB2PDDYR2U/action/citation_signature","submit_replication":"https://pith.science/pith/PINYGDQIRAITQTE6UB2PDDYR2U/action/replication_record"}},"created_at":"2026-05-18T00:22:36.128720+00:00","updated_at":"2026-05-18T00:22:36.128720+00:00"}