{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HE5OLCSY3ICV237V4HDVF5WZSX","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":"271b0fb0715d7f4fc1e6843d870a3a00e37a54834e910d12a349a0980a3641b0","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-30T16:17:46Z","title_canon_sha256":"a2e7d976db352dd22fc01652d980350073d47f030e4f8d1a6b6f9fbc1158c78d"},"schema_version":"1.0","source":{"id":"2112.15115","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.15115","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"arxiv_version","alias_value":"2112.15115v1","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.15115","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"pith_short_12","alias_value":"HE5OLCSY3ICV","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"pith_short_16","alias_value":"HE5OLCSY3ICV237V","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"pith_short_8","alias_value":"HE5OLCSY","created_at":"2026-07-05T03:44:36Z"}],"graph_snapshots":[{"event_id":"sha256:01cb6d4a2b0fff1a51d46d7ba7f110dc93d41210a4f32b9c5dc170f41ed3ec20","target":"graph","created_at":"2026-07-05T03:44:36Z","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/2112.15115/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citizens exposed to it. In this chapter we provide a review of the methods available to model air pollution, focusing on the application of machine-learning methods. In fact, machine-learning methods have proved to importantly increase the accuracy of traditional air-pollution approaches while limiting the development cost of the models. Machine-learning tools have opened new approaches to study air pollution, such as fl","authors_text":"Beril Sirmacek, Pablo Torres, Ricardo Vinuesa, Sergio Hoyas","cross_cats":["cs.AI","cs.CY"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-30T16:17:46Z","title":"Aim in Climate Change and City Pollution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.15115","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":"sha256:1e166f2d7ff3a7892c78fe6ba5c1b37750a4e8af9a68cda99669095271b99128","target":"record","created_at":"2026-07-05T03:44:36Z","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":"271b0fb0715d7f4fc1e6843d870a3a00e37a54834e910d12a349a0980a3641b0","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-12-30T16:17:46Z","title_canon_sha256":"a2e7d976db352dd22fc01652d980350073d47f030e4f8d1a6b6f9fbc1158c78d"},"schema_version":"1.0","source":{"id":"2112.15115","kind":"arxiv","version":1}},"canonical_sha256":"393ae58a58da055d6ff5e1c752f6d995eea65e4874a6778e3f7cd5c2bcea8d64","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"393ae58a58da055d6ff5e1c752f6d995eea65e4874a6778e3f7cd5c2bcea8d64","first_computed_at":"2026-07-05T03:44:36.867209Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:44:36.867209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A8whBMEis2NgoCF0gpm9nVUsjE+3zHG2JBVAPnqJsWmOa8x6pAYaawuI2QuFhMfzSYPbWOOfV8Fzw23ifTtHDw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:44:36.867686Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.15115","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1e166f2d7ff3a7892c78fe6ba5c1b37750a4e8af9a68cda99669095271b99128","sha256:01cb6d4a2b0fff1a51d46d7ba7f110dc93d41210a4f32b9c5dc170f41ed3ec20"],"state_sha256":"b31df04a81ffb6989d7ebcfd3df17399a3563bbdcb565bcd7281f2f13489e625"}