{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XJPYVG7D5CWVHCNE7MDUA267TF","short_pith_number":"pith:XJPYVG7D","schema_version":"1.0","canonical_sha256":"ba5f8a9be3e8ad5389a4fb07406bdf99454bbce7b7a521b2ac00dce79e07d9d6","source":{"kind":"arxiv","id":"2502.16778","version":1},"attestation_state":"computed","paper":{"title":"The Robustness of Structural Features in Species Interaction Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SI"],"primary_cat":"cs.LG","authors_text":"Emily Dolson, Sanaz Hasanzadeh Fard","submitted_at":"2025-02-24T02:14:17Z","abstract_excerpt":"Species interaction networks are a powerful tool for describing ecological communities; they typically contain nodes representing species, and edges representing interactions between those species. For the purposes of drawing abstract inferences about groups of similar networks, ecologists often use graph topology metrics to summarize structural features. However, gathering the data that underlies these networks is challenging, which can lead to some interactions being missed. Thus, it is important to understand how much different structural metrics are affected by missing data. To address thi"},"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":"2502.16778","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-24T02:14:17Z","cross_cats_sorted":["cs.AI","cs.SI"],"title_canon_sha256":"e4918d746568401be34cd0d0738abbc6ca6f9ccfd0f76e532d4c94e34d236811","abstract_canon_sha256":"abe4f9b381b73203441813129c6dc4275db73a0c98f8af05ea58a5dd2883e2a4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:18:59.461405Z","signature_b64":"lc3lvhb3j1VP5HWqO/ZBp4auxkg49JJF5QrtE70+2Ageh8BTejuVztwV8TWOtlrhS2AVxjdGL6gHRNetMclUDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba5f8a9be3e8ad5389a4fb07406bdf99454bbce7b7a521b2ac00dce79e07d9d6","last_reissued_at":"2026-07-05T10:18:59.460885Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:18:59.460885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Robustness of Structural Features in Species Interaction Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SI"],"primary_cat":"cs.LG","authors_text":"Emily Dolson, Sanaz Hasanzadeh Fard","submitted_at":"2025-02-24T02:14:17Z","abstract_excerpt":"Species interaction networks are a powerful tool for describing ecological communities; they typically contain nodes representing species, and edges representing interactions between those species. For the purposes of drawing abstract inferences about groups of similar networks, ecologists often use graph topology metrics to summarize structural features. However, gathering the data that underlies these networks is challenging, which can lead to some interactions being missed. Thus, it is important to understand how much different structural metrics are affected by missing data. To address thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.16778","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/2502.16778/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.16778","created_at":"2026-07-05T10:18:59.460947+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.16778v1","created_at":"2026-07-05T10:18:59.460947+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.16778","created_at":"2026-07-05T10:18:59.460947+00:00"},{"alias_kind":"pith_short_12","alias_value":"XJPYVG7D5CWV","created_at":"2026-07-05T10:18:59.460947+00:00"},{"alias_kind":"pith_short_16","alias_value":"XJPYVG7D5CWVHCNE","created_at":"2026-07-05T10:18:59.460947+00:00"},{"alias_kind":"pith_short_8","alias_value":"XJPYVG7D","created_at":"2026-07-05T10:18:59.460947+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.17219","citing_title":"A Low-Cost Machine Learning Approach for Timber Diameter Estimation","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XJPYVG7D5CWVHCNE7MDUA267TF","json":"https://pith.science/pith/XJPYVG7D5CWVHCNE7MDUA267TF.json","graph_json":"https://pith.science/api/pith-number/XJPYVG7D5CWVHCNE7MDUA267TF/graph.json","events_json":"https://pith.science/api/pith-number/XJPYVG7D5CWVHCNE7MDUA267TF/events.json","paper":"https://pith.science/paper/XJPYVG7D"},"agent_actions":{"view_html":"https://pith.science/pith/XJPYVG7D5CWVHCNE7MDUA267TF","download_json":"https://pith.science/pith/XJPYVG7D5CWVHCNE7MDUA267TF.json","view_paper":"https://pith.science/paper/XJPYVG7D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.16778&json=true","fetch_graph":"https://pith.science/api/pith-number/XJPYVG7D5CWVHCNE7MDUA267TF/graph.json","fetch_events":"https://pith.science/api/pith-number/XJPYVG7D5CWVHCNE7MDUA267TF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XJPYVG7D5CWVHCNE7MDUA267TF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XJPYVG7D5CWVHCNE7MDUA267TF/action/storage_attestation","attest_author":"https://pith.science/pith/XJPYVG7D5CWVHCNE7MDUA267TF/action/author_attestation","sign_citation":"https://pith.science/pith/XJPYVG7D5CWVHCNE7MDUA267TF/action/citation_signature","submit_replication":"https://pith.science/pith/XJPYVG7D5CWVHCNE7MDUA267TF/action/replication_record"}},"created_at":"2026-07-05T10:18:59.460947+00:00","updated_at":"2026-07-05T10:18:59.460947+00:00"}