{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:CTX23BB327KVORJTXI7ZRLPWFC","short_pith_number":"pith:CTX23BB3","canonical_record":{"source":{"id":"1908.07805","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2019-08-21T11:47:38Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"291299fe086829fea177666f6c294f8c4e90930ff36f1ddbb054a1a817b2598e","abstract_canon_sha256":"362dea569286c87d57e7f610cb1bd1ba3c63d603a915cda6360340233ddc9267"},"schema_version":"1.0"},"canonical_sha256":"14efad843bd7d5574533ba3f98adf6289a4b2bed1926403b20fa66ea390b2845","source":{"kind":"arxiv","id":"1908.07805","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.07805","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"arxiv_version","alias_value":"1908.07805v1","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07805","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_12","alias_value":"CTX23BB327KV","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_16","alias_value":"CTX23BB327KVORJT","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_8","alias_value":"CTX23BB3","created_at":"2026-07-05T00:24:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:CTX23BB327KVORJTXI7ZRLPWFC","target":"record","payload":{"canonical_record":{"source":{"id":"1908.07805","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2019-08-21T11:47:38Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"291299fe086829fea177666f6c294f8c4e90930ff36f1ddbb054a1a817b2598e","abstract_canon_sha256":"362dea569286c87d57e7f610cb1bd1ba3c63d603a915cda6360340233ddc9267"},"schema_version":"1.0"},"canonical_sha256":"14efad843bd7d5574533ba3f98adf6289a4b2bed1926403b20fa66ea390b2845","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:24:55.680924Z","signature_b64":"uD/qad5WOA1C/0n/t88WiVFZOc2pLSfFBSbb9Uv9pyFiNNRjUrvP5ioUk8taJwKIffuvlJ/4JnlW45D2L328Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14efad843bd7d5574533ba3f98adf6289a4b2bed1926403b20fa66ea390b2845","last_reissued_at":"2026-07-05T00:24:55.680560Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:24:55.680560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.07805","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:24:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xluWJ2NCOCYQczNnMwXSSQdfMeGiIDfxKaWCtXUpFazs5aORb4F8E7srhAy6SGHW0tou/J3j7my/9dcLnXw4CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T09:05:20.269171Z"},"content_sha256":"73f5bfffa5b9c5c38137b800fe0fa796ea89ee8212654248f1e41f2fec0e18a9","schema_version":"1.0","event_id":"sha256:73f5bfffa5b9c5c38137b800fe0fa796ea89ee8212654248f1e41f2fec0e18a9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:CTX23BB327KVORJTXI7ZRLPWFC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"stat.AP","authors_text":"Christoph Reudenbach, Hanna Meyer, Stephan W\\\"ollauer, Thomas Nauss","submitted_at":"2019-08-21T11:47:38Z","abstract_excerpt":"Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data but are unable to make spatial predictions beyond the locations of the training samples. We assume that not only spatial validation strategies but also spatial variable selection is essential for reliable spatial predictions. We introduce two case studies that use remote sensing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07805","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/1908.07805/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":"2026-07-05T00:24:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PvMiUE5emo5J9OOh1okum9ZhURFRGu5gQGY2ktnysyMbw+7WqZsrC0vYoZamAw8+3yKqJpXVGdBrCgu9hf68AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T09:05:20.269743Z"},"content_sha256":"c368010fea4e947f81473150b94ee3e7fdf8df45b2941810467caf4287f2a858","schema_version":"1.0","event_id":"sha256:c368010fea4e947f81473150b94ee3e7fdf8df45b2941810467caf4287f2a858"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CTX23BB327KVORJTXI7ZRLPWFC/bundle.json","state_url":"https://pith.science/pith/CTX23BB327KVORJTXI7ZRLPWFC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CTX23BB327KVORJTXI7ZRLPWFC/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":"2026-08-16T09:05:20Z","links":{"resolver":"https://pith.science/pith/CTX23BB327KVORJTXI7ZRLPWFC","bundle":"https://pith.science/pith/CTX23BB327KVORJTXI7ZRLPWFC/bundle.json","state":"https://pith.science/pith/CTX23BB327KVORJTXI7ZRLPWFC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CTX23BB327KVORJTXI7ZRLPWFC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:CTX23BB327KVORJTXI7ZRLPWFC","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":"362dea569286c87d57e7f610cb1bd1ba3c63d603a915cda6360340233ddc9267","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2019-08-21T11:47:38Z","title_canon_sha256":"291299fe086829fea177666f6c294f8c4e90930ff36f1ddbb054a1a817b2598e"},"schema_version":"1.0","source":{"id":"1908.07805","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.07805","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"arxiv_version","alias_value":"1908.07805v1","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07805","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_12","alias_value":"CTX23BB327KV","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_16","alias_value":"CTX23BB327KVORJT","created_at":"2026-07-05T00:24:55Z"},{"alias_kind":"pith_short_8","alias_value":"CTX23BB3","created_at":"2026-07-05T00:24:55Z"}],"graph_snapshots":[{"event_id":"sha256:c368010fea4e947f81473150b94ee3e7fdf8df45b2941810467caf4287f2a858","target":"graph","created_at":"2026-07-05T00:24:55Z","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/1908.07805/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data but are unable to make spatial predictions beyond the locations of the training samples. We assume that not only spatial validation strategies but also spatial variable selection is essential for reliable spatial predictions. We introduce two case studies that use remote sensing ","authors_text":"Christoph Reudenbach, Hanna Meyer, Stephan W\\\"ollauer, Thomas Nauss","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2019-08-21T11:47:38Z","title":"Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07805","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":"sha256:73f5bfffa5b9c5c38137b800fe0fa796ea89ee8212654248f1e41f2fec0e18a9","target":"record","created_at":"2026-07-05T00:24:55Z","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":"362dea569286c87d57e7f610cb1bd1ba3c63d603a915cda6360340233ddc9267","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2019-08-21T11:47:38Z","title_canon_sha256":"291299fe086829fea177666f6c294f8c4e90930ff36f1ddbb054a1a817b2598e"},"schema_version":"1.0","source":{"id":"1908.07805","kind":"arxiv","version":1}},"canonical_sha256":"14efad843bd7d5574533ba3f98adf6289a4b2bed1926403b20fa66ea390b2845","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"14efad843bd7d5574533ba3f98adf6289a4b2bed1926403b20fa66ea390b2845","first_computed_at":"2026-07-05T00:24:55.680560Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:24:55.680560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uD/qad5WOA1C/0n/t88WiVFZOc2pLSfFBSbb9Uv9pyFiNNRjUrvP5ioUk8taJwKIffuvlJ/4JnlW45D2L328Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:24:55.680924Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.07805","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:73f5bfffa5b9c5c38137b800fe0fa796ea89ee8212654248f1e41f2fec0e18a9","sha256:c368010fea4e947f81473150b94ee3e7fdf8df45b2941810467caf4287f2a858"],"state_sha256":"2ebc28fd289d37b94cc4390b6ae9f58f266747451edc360205c8cc72ba990720"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nuZXSDjghor/WEK+dUwgOOBORIraP9rK3dvs1X7mFMirWUPJDe24fqrcf3oD0bvt2p7s44FEEIVra6tmDbAnDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T09:05:20.275531Z","bundle_sha256":"a004ab998d52bd8a14cfb76135f32e91c353b80b09208f16033ddd6745f2825f"}}