{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:L3PNEVFRT2OW2JUOGWMCFSY2LA","short_pith_number":"pith:L3PNEVFR","canonical_record":{"source":{"id":"1910.09700","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2019-10-21T23:57:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"53c718faef53841f47f977dc8641503cb9d4fc1a9e2602604343414e85ae24bc","abstract_canon_sha256":"d4ce882e1d4e646bf26fc5ac7996efe579ab08f03782965320b110c07728eadd"},"schema_version":"1.0"},"canonical_sha256":"5eded254b19e9d6d268e359822cb1a581fefa8b1b081c67341970e00dcc3ed9b","source":{"kind":"arxiv","id":"1910.09700","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.09700","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"arxiv_version","alias_value":"1910.09700v2","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.09700","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"pith_short_12","alias_value":"L3PNEVFRT2OW","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"L3PNEVFRT2OW2JUO","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"L3PNEVFR","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:L3PNEVFRT2OW2JUOGWMCFSY2LA","target":"record","payload":{"canonical_record":{"source":{"id":"1910.09700","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2019-10-21T23:57:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"53c718faef53841f47f977dc8641503cb9d4fc1a9e2602604343414e85ae24bc","abstract_canon_sha256":"d4ce882e1d4e646bf26fc5ac7996efe579ab08f03782965320b110c07728eadd"},"schema_version":"1.0"},"canonical_sha256":"5eded254b19e9d6d268e359822cb1a581fefa8b1b081c67341970e00dcc3ed9b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:53.339536Z","signature_b64":"QILvlcBCkPI1w9QBbixIcxekDUIFFLXHWRWm6KOv7x/RPgRz/+VvrdHMSVJjuDuBxAM2p8RVYeunwkSFlbplCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5eded254b19e9d6d268e359822cb1a581fefa8b1b081c67341970e00dcc3ed9b","last_reissued_at":"2026-05-17T23:38:53.338922Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:53.338922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.09700","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":"2026-05-17T23:38:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GKQpOERSwGccp3rvTtBX2KRA6MOLSkur6SSlQAr3Fd86Tn7RpLOIGHhdA5BuGZXIZxvxV1Wjk0ZqDrN2x/XzCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T12:55:20.060121Z"},"content_sha256":"e4775f43539ae706c22e8aca3b8311bd25d6b2235607d107203d2f7c9bc38481","schema_version":"1.0","event_id":"sha256:e4775f43539ae706c22e8aca3b8311bd25d6b2235607d107203d2f7c9bc38481"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:L3PNEVFRT2OW2JUOGWMCFSY2LA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantifying the Carbon Emissions of Machine Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"A tool called the Machine Learning Emissions Calculator approximates the carbon emissions of training neural networks based on server location, energy grid, training duration, and hardware.","cross_cats":["cs.LG"],"primary_cat":"cs.CY","authors_text":"Alexandra Luccioni, Alexandre Lacoste, Thomas Dandres, Victor Schmidt","submitted_at":"2019-10-21T23:57:32Z","abstract_excerpt":"From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an expl"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The listed factors (server location, energy grid, training length, hardware) are sufficient to accurately approximate emissions and that the tool's estimates will be reliable enough to guide mitigation decisions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Presents a calculator tool for estimating carbon emissions from ML model training along with mitigation actions.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A tool called the Machine Learning Emissions Calculator approximates the carbon emissions of training neural networks based on server location, energy grid, training duration, and hardware.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c2958f2972083cbf7e70fa9f5432665589dd39acaa2d66bf5216f7daf89b6e1f"},"source":{"id":"1910.09700","kind":"arxiv","version":2},"verdict":{"id":"c9340fbc-6a49-48f0-b80c-0d6996b91fde","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T05:47:52.538039Z","strongest_claim":"we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models","one_line_summary":"Presents a calculator tool for estimating carbon emissions from ML model training along with mitigation actions.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The listed factors (server location, energy grid, training length, hardware) are sufficient to accurately approximate emissions and that the tool's estimates will be reliable enough to guide mitigation decisions.","pith_extraction_headline":"A tool called the Machine Learning Emissions Calculator approximates the carbon emissions of training neural networks based on server location, energy grid, training duration, and hardware."},"references":{"count":25,"sample":[{"doi":"","year":1906,"title":"Energy and policy considerations for deep learning in nlp","work_id":"33ac678f-b75d-4caf-b8a6-9a4d65b1748c","ref_index":1,"cited_arxiv_id":"1906.02243","is_internal_anchor":true},{"doi":"","year":1907,"title":"Emma Strubell, Ananya Ganesh, and Andrew McCallum","work_id":"2f2ecc7f-a9df-4003-a924-900d790909d9","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2006,"title":"Institute for Global Environmental Strategies Hayama, Japan, 2006","work_id":"cdc5d24e-6cb6-4209-968e-bd6036fa39c8","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2002,"title":"Ghg emissions from electricity consumption: A case study of hong kong from 2002 to 2015 and trends to 2030","work_id":"19c7fb3a-bb23-477b-82b7-b6fc85cb31a7","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2011,"title":"Electricity- specific emission factors for grid electricity.Ecometrica, Emissionfactors","work_id":"a2d83cb4-911a-40f6-a569-85381c48b0f5","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":25,"snapshot_sha256":"d699f907ffce6da5e415eb0c900a3077e07088ebcd840e41a04f1db644cae7f2","internal_anchors":8},"formal_canon":{"evidence_count":2,"snapshot_sha256":"4047b89aadfcf197b412157147df3a98871e4082651894db2a7213dc3f5d8493"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"c9340fbc-6a49-48f0-b80c-0d6996b91fde"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VQ414YU1MIUF104n7r068kjb8NTvx7ZnXzmNt0deszw0FQqxX8hRXNzPFBc2WjLnM//7azx61NODGb/d70PsDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T12:55:20.060776Z"},"content_sha256":"11f473cf8a102e41495da2c56cff8064108a83e137173639ce16f6f014897f76","schema_version":"1.0","event_id":"sha256:11f473cf8a102e41495da2c56cff8064108a83e137173639ce16f6f014897f76"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L3PNEVFRT2OW2JUOGWMCFSY2LA/bundle.json","state_url":"https://pith.science/pith/L3PNEVFRT2OW2JUOGWMCFSY2LA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L3PNEVFRT2OW2JUOGWMCFSY2LA/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":"2026-05-26T12:55:20Z","links":{"resolver":"https://pith.science/pith/L3PNEVFRT2OW2JUOGWMCFSY2LA","bundle":"https://pith.science/pith/L3PNEVFRT2OW2JUOGWMCFSY2LA/bundle.json","state":"https://pith.science/pith/L3PNEVFRT2OW2JUOGWMCFSY2LA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L3PNEVFRT2OW2JUOGWMCFSY2LA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:L3PNEVFRT2OW2JUOGWMCFSY2LA","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":"d4ce882e1d4e646bf26fc5ac7996efe579ab08f03782965320b110c07728eadd","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2019-10-21T23:57:32Z","title_canon_sha256":"53c718faef53841f47f977dc8641503cb9d4fc1a9e2602604343414e85ae24bc"},"schema_version":"1.0","source":{"id":"1910.09700","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.09700","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"arxiv_version","alias_value":"1910.09700v2","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.09700","created_at":"2026-05-17T23:38:53Z"},{"alias_kind":"pith_short_12","alias_value":"L3PNEVFRT2OW","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"L3PNEVFRT2OW2JUO","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"L3PNEVFR","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:11f473cf8a102e41495da2c56cff8064108a83e137173639ce16f6f014897f76","target":"graph","created_at":"2026-05-17T23:38:53Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models"},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The listed factors (server location, energy grid, training length, hardware) are sufficient to accurately approximate emissions and that the tool's estimates will be reliable enough to guide mitigation decisions."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Presents a calculator tool for estimating carbon emissions from ML model training along with mitigation actions."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A tool called the Machine Learning Emissions Calculator approximates the carbon emissions of training neural networks based on server location, energy grid, training duration, and hardware."}],"snapshot_sha256":"c2958f2972083cbf7e70fa9f5432665589dd39acaa2d66bf5216f7daf89b6e1f"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"4047b89aadfcf197b412157147df3a98871e4082651894db2a7213dc3f5d8493"},"paper":{"abstract_excerpt":"From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an expl","authors_text":"Alexandra Luccioni, Alexandre Lacoste, Thomas Dandres, Victor Schmidt","cross_cats":["cs.LG"],"headline":"A tool called the Machine Learning Emissions Calculator approximates the carbon emissions of training neural networks based on server location, energy grid, training duration, and hardware.","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2019-10-21T23:57:32Z","title":"Quantifying the Carbon Emissions of Machine Learning"},"references":{"count":25,"internal_anchors":8,"resolved_work":25,"sample":[{"cited_arxiv_id":"1906.02243","doi":"","is_internal_anchor":true,"ref_index":1,"title":"Energy and policy considerations for deep learning in nlp","work_id":"33ac678f-b75d-4caf-b8a6-9a4d65b1748c","year":1906},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Emma Strubell, Ananya Ganesh, and Andrew McCallum","work_id":"2f2ecc7f-a9df-4003-a924-900d790909d9","year":1907},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Institute for Global Environmental Strategies Hayama, Japan, 2006","work_id":"cdc5d24e-6cb6-4209-968e-bd6036fa39c8","year":2006},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Ghg emissions from electricity consumption: A case study of hong kong from 2002 to 2015 and trends to 2030","work_id":"19c7fb3a-bb23-477b-82b7-b6fc85cb31a7","year":2002},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Electricity- specific emission factors for grid electricity.Ecometrica, Emissionfactors","work_id":"a2d83cb4-911a-40f6-a569-85381c48b0f5","year":2011}],"snapshot_sha256":"d699f907ffce6da5e415eb0c900a3077e07088ebcd840e41a04f1db644cae7f2"},"source":{"id":"1910.09700","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-15T05:47:52.538039Z","id":"c9340fbc-6a49-48f0-b80c-0d6996b91fde","model_set":{"reader":"grok-4.3"},"one_line_summary":"Presents a calculator tool for estimating carbon emissions from ML model training along with mitigation actions.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A tool called the Machine Learning Emissions Calculator approximates the carbon emissions of training neural networks based on server location, energy grid, training duration, and hardware.","strongest_claim":"we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models","weakest_assumption":"The listed factors (server location, energy grid, training length, hardware) are sufficient to accurately approximate emissions and that the tool's estimates will be reliable enough to guide mitigation decisions."}},"verdict_id":"c9340fbc-6a49-48f0-b80c-0d6996b91fde"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e4775f43539ae706c22e8aca3b8311bd25d6b2235607d107203d2f7c9bc38481","target":"record","created_at":"2026-05-17T23:38:53Z","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":"d4ce882e1d4e646bf26fc5ac7996efe579ab08f03782965320b110c07728eadd","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2019-10-21T23:57:32Z","title_canon_sha256":"53c718faef53841f47f977dc8641503cb9d4fc1a9e2602604343414e85ae24bc"},"schema_version":"1.0","source":{"id":"1910.09700","kind":"arxiv","version":2}},"canonical_sha256":"5eded254b19e9d6d268e359822cb1a581fefa8b1b081c67341970e00dcc3ed9b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5eded254b19e9d6d268e359822cb1a581fefa8b1b081c67341970e00dcc3ed9b","first_computed_at":"2026-05-17T23:38:53.338922Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:53.338922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QILvlcBCkPI1w9QBbixIcxekDUIFFLXHWRWm6KOv7x/RPgRz/+VvrdHMSVJjuDuBxAM2p8RVYeunwkSFlbplCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:53.339536Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.09700","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e4775f43539ae706c22e8aca3b8311bd25d6b2235607d107203d2f7c9bc38481","sha256:11f473cf8a102e41495da2c56cff8064108a83e137173639ce16f6f014897f76"],"state_sha256":"f852134c54841e750ee33382637102cb7f947de7a3bfa2dbd08f8949efe39dd1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7tNUs/ghAuSkB9fhFiHH+oXD/xVfAm2QsHhGkiGnN31ooxEeffEirxlZVkTOZgkBUWL/gh1PnFp/fdZZ7ApwBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T12:55:20.064109Z","bundle_sha256":"59a24eb13a8b6760dfd4529db5ee106c3994eb72ad99849f88d0c1101539762f"}}