{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AXVTCIUHCLK24OBVDKIFKPDECL","short_pith_number":"pith:AXVTCIUH","schema_version":"1.0","canonical_sha256":"05eb31228712d5ae38351a90553c6412d912aa9adacc0b460542977fed049ac4","source":{"kind":"arxiv","id":"2509.22092","version":1},"attestation_state":"computed","paper":{"title":"Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Raphael Fischer","submitted_at":"2025-09-26T09:12:21Z","abstract_excerpt":"Although machine learning (ML) and artificial intelligence (AI) present fascinating opportunities for innovation, their rapid development is also significantly impacting our environment. In response to growing resource-awareness in the field, quantification tools such as the ML Emissions Calculator and CodeCarbon were developed to estimate the energy consumption and carbon emissions of running AI models. They are easy to incorporate into AI projects, however also make pragmatic assumptions and neglect important factors, raising the question of estimation accuracy. This study systematically eva"},"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":"2509.22092","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-09-26T09:12:21Z","cross_cats_sorted":[],"title_canon_sha256":"796d2e979d1f2a29a92ccab0bb93ad3048e26c14c00f94d10ad234f3abb605e2","abstract_canon_sha256":"dd9748f487f8f3442745120b5db6aa744d5daa997889ca802d3d2a30a1e66a2d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-13T01:17:47.329867Z","signature_b64":"Yujtyrg5/NS3Z6oYxxZmkW/tfktDBOTXJgGzfsAoPPpK3Eqr43A7BTWh9BFtcDZb/+RLmAUy9WQPuQ1YqN++Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05eb31228712d5ae38351a90553c6412d912aa9adacc0b460542977fed049ac4","last_reissued_at":"2026-07-13T01:17:47.328245Z","signature_status":"signed_v1","first_computed_at":"2026-07-13T01:17:47.328245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Raphael Fischer","submitted_at":"2025-09-26T09:12:21Z","abstract_excerpt":"Although machine learning (ML) and artificial intelligence (AI) present fascinating opportunities for innovation, their rapid development is also significantly impacting our environment. In response to growing resource-awareness in the field, quantification tools such as the ML Emissions Calculator and CodeCarbon were developed to estimate the energy consumption and carbon emissions of running AI models. They are easy to incorporate into AI projects, however also make pragmatic assumptions and neglect important factors, raising the question of estimation accuracy. This study systematically eva"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.22092","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/2509.22092/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":"2509.22092","created_at":"2026-07-13T01:17:47.328998+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.22092v1","created_at":"2026-07-13T01:17:47.328998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.22092","created_at":"2026-07-13T01:17:47.328998+00:00"},{"alias_kind":"pith_short_12","alias_value":"AXVTCIUHCLK2","created_at":"2026-07-13T01:17:47.328998+00:00"},{"alias_kind":"pith_short_16","alias_value":"AXVTCIUHCLK24OBV","created_at":"2026-07-13T01:17:47.328998+00:00"},{"alias_kind":"pith_short_8","alias_value":"AXVTCIUH","created_at":"2026-07-13T01:17:47.328998+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2605.11857","citing_title":"Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs","ref_index":22,"is_internal_anchor":true},{"citing_arxiv_id":"2605.05416","citing_title":"From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"2604.07953","citing_title":"Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AXVTCIUHCLK24OBVDKIFKPDECL","json":"https://pith.science/pith/AXVTCIUHCLK24OBVDKIFKPDECL.json","graph_json":"https://pith.science/api/pith-number/AXVTCIUHCLK24OBVDKIFKPDECL/graph.json","events_json":"https://pith.science/api/pith-number/AXVTCIUHCLK24OBVDKIFKPDECL/events.json","paper":"https://pith.science/paper/AXVTCIUH"},"agent_actions":{"view_html":"https://pith.science/pith/AXVTCIUHCLK24OBVDKIFKPDECL","download_json":"https://pith.science/pith/AXVTCIUHCLK24OBVDKIFKPDECL.json","view_paper":"https://pith.science/paper/AXVTCIUH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.22092&json=true","fetch_graph":"https://pith.science/api/pith-number/AXVTCIUHCLK24OBVDKIFKPDECL/graph.json","fetch_events":"https://pith.science/api/pith-number/AXVTCIUHCLK24OBVDKIFKPDECL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AXVTCIUHCLK24OBVDKIFKPDECL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AXVTCIUHCLK24OBVDKIFKPDECL/action/storage_attestation","attest_author":"https://pith.science/pith/AXVTCIUHCLK24OBVDKIFKPDECL/action/author_attestation","sign_citation":"https://pith.science/pith/AXVTCIUHCLK24OBVDKIFKPDECL/action/citation_signature","submit_replication":"https://pith.science/pith/AXVTCIUHCLK24OBVDKIFKPDECL/action/replication_record"}},"created_at":"2026-07-13T01:17:47.328998+00:00","updated_at":"2026-07-13T01:17:47.328998+00:00"}