{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ","short_pith_number":"pith:Y2BRV4KX","canonical_record":{"source":{"id":"2011.05369","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-10T19:44:52Z","cross_cats_sorted":[],"title_canon_sha256":"714a6e27be05b40ce684159eeb87e529c835e96f12939b946759f28f93a8f833","abstract_canon_sha256":"932955638cd8e823a97498fdd03ddc1744655ece51da44c23ced93b0445a92c8"},"schema_version":"1.0"},"canonical_sha256":"c6831af157327d4db45387175d23f4743fbf0ec3dd985e3cdeee3ebcdcee6e9d","source":{"kind":"arxiv","id":"2011.05369","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.05369","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"arxiv_version","alias_value":"2011.05369v2","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.05369","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"pith_short_12","alias_value":"Y2BRV4KXGJ6U","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"pith_short_16","alias_value":"Y2BRV4KXGJ6U3NCT","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"pith_short_8","alias_value":"Y2BRV4KX","created_at":"2026-07-05T03:04:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ","target":"record","payload":{"canonical_record":{"source":{"id":"2011.05369","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-10T19:44:52Z","cross_cats_sorted":[],"title_canon_sha256":"714a6e27be05b40ce684159eeb87e529c835e96f12939b946759f28f93a8f833","abstract_canon_sha256":"932955638cd8e823a97498fdd03ddc1744655ece51da44c23ced93b0445a92c8"},"schema_version":"1.0"},"canonical_sha256":"c6831af157327d4db45387175d23f4743fbf0ec3dd985e3cdeee3ebcdcee6e9d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:04:18.470025Z","signature_b64":"Tst4DsO5d0+IOUuva0V1a/KIm4w0AwMwG7Zlotfcx5J3EVi6AeQC54X4106S+aPSgTJVzTS0sLyGskJ9abT0AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6831af157327d4db45387175d23f4743fbf0ec3dd985e3cdeee3ebcdcee6e9d","last_reissued_at":"2026-07-05T03:04:18.469575Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:04:18.469575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.05369","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":"2026-07-05T03:04:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bFcDABSBFfZxBITZqFiDr0UzsuxPrG2vScKYH6aunNUrOgEDClh1V2Hvb+JwXQHsUo5HLmauaj+BUQvBOScXAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T22:26:17.047351Z"},"content_sha256":"d641e63ac32396a97ee57180133c020df5946dcef8d354f0b3c96e6b39e74299","schema_version":"1.0","event_id":"sha256:d641e63ac32396a97ee57180133c020df5946dcef8d354f0b3c96e6b39e74299"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alison P. Appling, Jared D. Willard, Jordan S. Read, Samantha K. Oliver, Vipin Kumar, Xiaowei Jia","submitted_at":"2020-11-10T19:44:52Z","abstract_excerpt":"Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, Meta Transfer Learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.05369","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/2011.05369/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":"2026-07-05T03:04:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6zoJeJcEqGPr6YDG7xNeThcrVR37glrRdNsVZ2gAzt2zXwLn0hO/62B4cySHYjFhiKDgrqLuxaxEt/+6jvcqBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T22:26:17.047844Z"},"content_sha256":"d4f6e891f8646b8f64c6089ce4b823b02ea2c210a985145847b969908e12d827","schema_version":"1.0","event_id":"sha256:d4f6e891f8646b8f64c6089ce4b823b02ea2c210a985145847b969908e12d827"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ/bundle.json","state_url":"https://pith.science/pith/Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ/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":"2026-08-20T22:26:17Z","links":{"resolver":"https://pith.science/pith/Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ","bundle":"https://pith.science/pith/Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ/bundle.json","state":"https://pith.science/pith/Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:Y2BRV4KXGJ6U3NCTQ4LV2I7UOQ","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":"932955638cd8e823a97498fdd03ddc1744655ece51da44c23ced93b0445a92c8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-10T19:44:52Z","title_canon_sha256":"714a6e27be05b40ce684159eeb87e529c835e96f12939b946759f28f93a8f833"},"schema_version":"1.0","source":{"id":"2011.05369","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.05369","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"arxiv_version","alias_value":"2011.05369v2","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.05369","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"pith_short_12","alias_value":"Y2BRV4KXGJ6U","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"pith_short_16","alias_value":"Y2BRV4KXGJ6U3NCT","created_at":"2026-07-05T03:04:18Z"},{"alias_kind":"pith_short_8","alias_value":"Y2BRV4KX","created_at":"2026-07-05T03:04:18Z"}],"graph_snapshots":[{"event_id":"sha256:d4f6e891f8646b8f64c6089ce4b823b02ea2c210a985145847b969908e12d827","target":"graph","created_at":"2026-07-05T03:04:18Z","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/2011.05369/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, Meta Transfer Learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructe","authors_text":"Alison P. Appling, Jared D. Willard, Jordan S. Read, Samantha K. Oliver, Vipin Kumar, Xiaowei Jia","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-10T19:44:52Z","title":"Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.05369","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:d641e63ac32396a97ee57180133c020df5946dcef8d354f0b3c96e6b39e74299","target":"record","created_at":"2026-07-05T03:04:18Z","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":"932955638cd8e823a97498fdd03ddc1744655ece51da44c23ced93b0445a92c8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-10T19:44:52Z","title_canon_sha256":"714a6e27be05b40ce684159eeb87e529c835e96f12939b946759f28f93a8f833"},"schema_version":"1.0","source":{"id":"2011.05369","kind":"arxiv","version":2}},"canonical_sha256":"c6831af157327d4db45387175d23f4743fbf0ec3dd985e3cdeee3ebcdcee6e9d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c6831af157327d4db45387175d23f4743fbf0ec3dd985e3cdeee3ebcdcee6e9d","first_computed_at":"2026-07-05T03:04:18.469575Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:04:18.469575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Tst4DsO5d0+IOUuva0V1a/KIm4w0AwMwG7Zlotfcx5J3EVi6AeQC54X4106S+aPSgTJVzTS0sLyGskJ9abT0AA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:04:18.470025Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.05369","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d641e63ac32396a97ee57180133c020df5946dcef8d354f0b3c96e6b39e74299","sha256:d4f6e891f8646b8f64c6089ce4b823b02ea2c210a985145847b969908e12d827"],"state_sha256":"e5f82636f172a996880ef064eacf2be9f115c9b9fbffa420ad8fed32a321482d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JmEmBAvavuJ+XJZHyuwXVPjQScHbVZLvlM3wvEGnromRQCLtAzUjXsCIw5+K0+sPgpuWAwAFTrNE09ugTQcrCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T22:26:17.052918Z","bundle_sha256":"59df9f231b74fbe9fbf66f7e710479fcf17987c7665d6e151f836480c8ac3be9"}}