{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:T6ZYH3ZOEX5FLIDSNRI7IOLKYO","short_pith_number":"pith:T6ZYH3ZO","schema_version":"1.0","canonical_sha256":"9fb383ef2e25fa55a0726c51f4396ac38e910b85918a21b9dc0d2f4273d88344","source":{"kind":"arxiv","id":"1911.08354","version":2},"attestation_state":"computed","paper":{"title":"Energy Usage Reports: Environmental awareness as part of algorithmic accountability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jonathan P. Wilson, Kadan Lottick, Silvia Susai, Sorelle A. Friedler","submitted_at":"2019-11-19T15:34:28Z","abstract_excerpt":"The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an easy-to-use Python package. Localizing to the energy mixture of the electrical power grid, we make the conversion from energy usage to CO2 emissions, in addition to contextualizing these results with more human-understandable benchmarks such as automobile miles driven. We also inc"},"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":"1911.08354","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-19T15:34:28Z","cross_cats_sorted":["cs.CY","stat.ML"],"title_canon_sha256":"09d7b7cac549f68132cc49d4d5987d7bc75518cc44220868ec6b51457c7003fa","abstract_canon_sha256":"92052d6542796750e3e03b03384b7e55a8cbbb432c7bc4dcd139680c5f142678"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:26:11.804222Z","signature_b64":"L1Xz+q00MdE1e8rB4i7EbFRCeADvpwQ2zV3dpoOoF5CGuirjP8/52OLwcarFmrLr8uMrZoleDTnghcz8ZcnyCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fb383ef2e25fa55a0726c51f4396ac38e910b85918a21b9dc0d2f4273d88344","last_reissued_at":"2026-07-05T00:26:11.803736Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:26:11.803736Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Energy Usage Reports: Environmental awareness as part of algorithmic accountability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jonathan P. Wilson, Kadan Lottick, Silvia Susai, Sorelle A. Friedler","submitted_at":"2019-11-19T15:34:28Z","abstract_excerpt":"The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an easy-to-use Python package. Localizing to the energy mixture of the electrical power grid, we make the conversion from energy usage to CO2 emissions, in addition to contextualizing these results with more human-understandable benchmarks such as automobile miles driven. We also inc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.08354","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/1911.08354/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":"1911.08354","created_at":"2026-07-05T00:26:11.803794+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.08354v2","created_at":"2026-07-05T00:26:11.803794+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.08354","created_at":"2026-07-05T00:26:11.803794+00:00"},{"alias_kind":"pith_short_12","alias_value":"T6ZYH3ZOEX5F","created_at":"2026-07-05T00:26:11.803794+00:00"},{"alias_kind":"pith_short_16","alias_value":"T6ZYH3ZOEX5FLIDS","created_at":"2026-07-05T00:26:11.803794+00:00"},{"alias_kind":"pith_short_8","alias_value":"T6ZYH3ZO","created_at":"2026-07-05T00:26:11.803794+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07327","citing_title":"Six Open Questions in Machine-Learned Interatomic Potential Foundation Models","ref_index":269,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11215","citing_title":"The Environmental Cost of LLMs in AIED: Reporting and Practices","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24561","citing_title":"CARINA: Carbon-Aware Execution of Recurrent Industrial Analytics","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2511.19633","citing_title":"An NLO-Matched Initial and Final State Parton Shower on a GPU","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2511.19633","citing_title":"An NLO-Matched Initial and Final State Parton Shower on a GPU","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T6ZYH3ZOEX5FLIDSNRI7IOLKYO","json":"https://pith.science/pith/T6ZYH3ZOEX5FLIDSNRI7IOLKYO.json","graph_json":"https://pith.science/api/pith-number/T6ZYH3ZOEX5FLIDSNRI7IOLKYO/graph.json","events_json":"https://pith.science/api/pith-number/T6ZYH3ZOEX5FLIDSNRI7IOLKYO/events.json","paper":"https://pith.science/paper/T6ZYH3ZO"},"agent_actions":{"view_html":"https://pith.science/pith/T6ZYH3ZOEX5FLIDSNRI7IOLKYO","download_json":"https://pith.science/pith/T6ZYH3ZOEX5FLIDSNRI7IOLKYO.json","view_paper":"https://pith.science/paper/T6ZYH3ZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.08354&json=true","fetch_graph":"https://pith.science/api/pith-number/T6ZYH3ZOEX5FLIDSNRI7IOLKYO/graph.json","fetch_events":"https://pith.science/api/pith-number/T6ZYH3ZOEX5FLIDSNRI7IOLKYO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T6ZYH3ZOEX5FLIDSNRI7IOLKYO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T6ZYH3ZOEX5FLIDSNRI7IOLKYO/action/storage_attestation","attest_author":"https://pith.science/pith/T6ZYH3ZOEX5FLIDSNRI7IOLKYO/action/author_attestation","sign_citation":"https://pith.science/pith/T6ZYH3ZOEX5FLIDSNRI7IOLKYO/action/citation_signature","submit_replication":"https://pith.science/pith/T6ZYH3ZOEX5FLIDSNRI7IOLKYO/action/replication_record"}},"created_at":"2026-07-05T00:26:11.803794+00:00","updated_at":"2026-07-05T00:26:11.803794+00:00"}