{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:4IDWVPPXL3MMMNRQFAB2FYKUVH","short_pith_number":"pith:4IDWVPPX","canonical_record":{"source":{"id":"2203.14814","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-28T14:51:42Z","cross_cats_sorted":["physics.ao-ph"],"title_canon_sha256":"a8fe0e8fb68f7d985f3ea596fc0eb4d1fe2b9f70515e616cfb5e6c5038841a96","abstract_canon_sha256":"037608c21d5c93f9edcd68892ffc82ef573d347d2ba6f8464dee672a28d093cf"},"schema_version":"1.0"},"canonical_sha256":"e2076abdf75ed8c636302803a2e154a9da9982d2785d1de1cfa3045acac97d80","source":{"kind":"arxiv","id":"2203.14814","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14814","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14814v4","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14814","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"pith_short_12","alias_value":"4IDWVPPXL3MM","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"pith_short_16","alias_value":"4IDWVPPXL3MMMNRQ","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"pith_short_8","alias_value":"4IDWVPPX","created_at":"2026-07-05T04:56:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:4IDWVPPXL3MMMNRQFAB2FYKUVH","target":"record","payload":{"canonical_record":{"source":{"id":"2203.14814","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-28T14:51:42Z","cross_cats_sorted":["physics.ao-ph"],"title_canon_sha256":"a8fe0e8fb68f7d985f3ea596fc0eb4d1fe2b9f70515e616cfb5e6c5038841a96","abstract_canon_sha256":"037608c21d5c93f9edcd68892ffc82ef573d347d2ba6f8464dee672a28d093cf"},"schema_version":"1.0"},"canonical_sha256":"e2076abdf75ed8c636302803a2e154a9da9982d2785d1de1cfa3045acac97d80","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:56:16.119362Z","signature_b64":"lrEEOaA1iQpL1NWG3DbHZEv+xQ0VdivF9JAQQasCm11TDD79sMbe8LC/DzXu0QNeUdCrNirXPnywDaBsW3YtAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2076abdf75ed8c636302803a2e154a9da9982d2785d1de1cfa3045acac97d80","last_reissued_at":"2026-07-05T04:56:16.118671Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:56:16.118671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.14814","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:56:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nQn1X+xIQF9F4uQChbvSmMxQ5nibArwW9wnbmcuXs2J8f/L8e21h6ZVAAACvewHV5xKTxcwlyQ21wdaEtK+WDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T13:46:41.847835Z"},"content_sha256":"12551356c7c81ba81ddf91b900d4769c7ddf95f64fc3f2a7b16e4ee947f2b06b","schema_version":"1.0","event_id":"sha256:12551356c7c81ba81ddf91b900d4769c7ddf95f64fc3f2a7b16e4ee947f2b06b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:4IDWVPPXL3MMMNRQFAB2FYKUVH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.ao-ph"],"primary_cat":"cs.LG","authors_text":"Damon J. Wischik, Hannah M. Christensen, J. Scott Hosking, Raghul Parthipan","submitted_at":"2022-03-28T14:51:42Z","abstract_excerpt":"The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Red noise is essential to many operational parameterization schemes, helping model temporal correlations. We show how to build on the successes of red noise by combining the known benefits of stochasticity with machine learning. This is done using a physically-informed recurrent neural network within a probabilistic framework. Our model is competitive and often superior to both a bespoke baseline and an ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14814","kind":"arxiv","version":4},"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/2203.14814/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":"2026-07-05T04:56:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PJ4rfYA1bop6f0g3Myn1wq0J+oYnEk1eSUzveT2hdSM4UXA2fy2WB3egyT+yYOfBe7DrYHS99EWDu9xvqFYPBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T13:46:41.848920Z"},"content_sha256":"5c613895dd63d3ec2bb36ae421763720c5ecd968c68f00954644bdd3c6ff36a2","schema_version":"1.0","event_id":"sha256:5c613895dd63d3ec2bb36ae421763720c5ecd968c68f00954644bdd3c6ff36a2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4IDWVPPXL3MMMNRQFAB2FYKUVH/bundle.json","state_url":"https://pith.science/pith/4IDWVPPXL3MMMNRQFAB2FYKUVH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4IDWVPPXL3MMMNRQFAB2FYKUVH/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":"2026-08-17T13:46:41Z","links":{"resolver":"https://pith.science/pith/4IDWVPPXL3MMMNRQFAB2FYKUVH","bundle":"https://pith.science/pith/4IDWVPPXL3MMMNRQFAB2FYKUVH/bundle.json","state":"https://pith.science/pith/4IDWVPPXL3MMMNRQFAB2FYKUVH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4IDWVPPXL3MMMNRQFAB2FYKUVH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:4IDWVPPXL3MMMNRQFAB2FYKUVH","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":"037608c21d5c93f9edcd68892ffc82ef573d347d2ba6f8464dee672a28d093cf","cross_cats_sorted":["physics.ao-ph"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-28T14:51:42Z","title_canon_sha256":"a8fe0e8fb68f7d985f3ea596fc0eb4d1fe2b9f70515e616cfb5e6c5038841a96"},"schema_version":"1.0","source":{"id":"2203.14814","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14814","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14814v4","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14814","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"pith_short_12","alias_value":"4IDWVPPXL3MM","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"pith_short_16","alias_value":"4IDWVPPXL3MMMNRQ","created_at":"2026-07-05T04:56:16Z"},{"alias_kind":"pith_short_8","alias_value":"4IDWVPPX","created_at":"2026-07-05T04:56:16Z"}],"graph_snapshots":[{"event_id":"sha256:5c613895dd63d3ec2bb36ae421763720c5ecd968c68f00954644bdd3c6ff36a2","target":"graph","created_at":"2026-07-05T04:56:16Z","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/2203.14814/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Red noise is essential to many operational parameterization schemes, helping model temporal correlations. We show how to build on the successes of red noise by combining the known benefits of stochasticity with machine learning. This is done using a physically-informed recurrent neural network within a probabilistic framework. Our model is competitive and often superior to both a bespoke baseline and an ex","authors_text":"Damon J. Wischik, Hannah M. Christensen, J. Scott Hosking, Raghul Parthipan","cross_cats":["physics.ao-ph"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-28T14:51:42Z","title":"Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14814","kind":"arxiv","version":4},"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":"sha256:12551356c7c81ba81ddf91b900d4769c7ddf95f64fc3f2a7b16e4ee947f2b06b","target":"record","created_at":"2026-07-05T04:56:16Z","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":"037608c21d5c93f9edcd68892ffc82ef573d347d2ba6f8464dee672a28d093cf","cross_cats_sorted":["physics.ao-ph"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-28T14:51:42Z","title_canon_sha256":"a8fe0e8fb68f7d985f3ea596fc0eb4d1fe2b9f70515e616cfb5e6c5038841a96"},"schema_version":"1.0","source":{"id":"2203.14814","kind":"arxiv","version":4}},"canonical_sha256":"e2076abdf75ed8c636302803a2e154a9da9982d2785d1de1cfa3045acac97d80","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2076abdf75ed8c636302803a2e154a9da9982d2785d1de1cfa3045acac97d80","first_computed_at":"2026-07-05T04:56:16.118671Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:56:16.118671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lrEEOaA1iQpL1NWG3DbHZEv+xQ0VdivF9JAQQasCm11TDD79sMbe8LC/DzXu0QNeUdCrNirXPnywDaBsW3YtAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:56:16.119362Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.14814","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:12551356c7c81ba81ddf91b900d4769c7ddf95f64fc3f2a7b16e4ee947f2b06b","sha256:5c613895dd63d3ec2bb36ae421763720c5ecd968c68f00954644bdd3c6ff36a2"],"state_sha256":"bb695addf20504dd8e8379b1bfe023cf8dedaf4cc1fa2ea969b395908087c283"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NqMlPW6KS8XldZihUDuvrFX/lO71oO/SRBeGp5Ne0DVrej4SRXKUQayhNxxmHTvDx/yi7hKC1yVh70Osm5b7Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T13:46:41.864997Z","bundle_sha256":"826d1794c2cd58d86fd7def398618401a17cd1df93f88d97d59a8d3f523e364e"}}