Paper Citation Record · LEDGER

Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2007.03051.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2007.03051 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:36:25.849010Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

115
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 162a961f-d281-4ffb-a66a-738a60c019b0 · inbound

Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference cites this paper.

Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-23T00:07:17.268780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2982508b-91e6-4876-a6ea-44c65eedc84f · inbound

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices cites this paper.

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:25.849010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbf4285b-be7a-44bb-8b69-b26ef55b331a · inbound

Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead cites this paper.

Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:07.311697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00627497-0b08-4d0f-be99-26d9563cc3ab · inbound

Towards Decentralized and Sustainable Foundation Model Training with the Edge cites this paper.

Towards Decentralized and Sustainable Foundation Model Training with the Edge Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:02.026851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46d8866b-546e-445f-b2ce-aa3684ee87a9 · inbound

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations cites this paper.

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T17:14:19.291983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da894f6e-5cb3-4a71-ae82-64a53ba9a196 · inbound

Energy and Quality of Surrogate-Assisted Search Algorithms: a First Analysis cites this paper.

Energy and Quality of Surrogate-Assisted Search Algorithms: a First Analysis Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T21:58:20.986218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8eab23a-e5ad-4f9c-b52a-26203639530b · inbound

Performance is not All You Need: Sustainability Considerations for Algorithms cites this paper.

Performance is not All You Need: Sustainability Considerations for Algorithms Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T17:03:25.114160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5827fe0a-d5b0-4024-9ee8-8d819b957d88 · inbound

A Discrepancy-Based Perspective on Dataset Condensation cites this paper.

A Discrepancy-Based Perspective on Dataset Condensation Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T17:57:53.510466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a1da7ef-c593-49eb-8aa8-1a11e6d294ab · inbound

Physics Priors Offer Useful Accuracy-Carbon Trade-Offs in Spatio-Temporal Forecasting cites this paper.

Physics Priors Offer Useful Accuracy-Carbon Trade-Offs in Spatio-Temporal Forecasting Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:04:52.559027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fd8ecf3f-ac87-4a03-94a4-21675fd88a2c · inbound

Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid cites this paper.

Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T00:35:32.524414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 093e1e4e-eda4-4757-abc2-8fcb2d8bb9ea · inbound

Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid cites this paper.

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T18:55:29.769193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 905a8d4b-a03c-41d1-a4e4-c78d5aec1513 · inbound

Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation cites this paper.

Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-17T21:10:16.427683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5efaf128-a4f4-4928-bb6b-5ce7cc02e99b · inbound

Watt Counts: Energy-Aware Benchmark for Sustainable LLM Inference on Heterogeneous GPU Architectures cites this paper.

Watt Counts: Energy-Aware Benchmark for Sustainable LLM Inference on Heterogeneous GPU Architectures Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:36:03.469662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3fc48dbf-f94b-4d68-8af8-65629ca34838 · inbound

HAFM: Hierarchical Autoregressive Foundation Model for Music Accompaniment Generation cites this paper.

HAFM: Hierarchical Autoregressive Foundation Model for Music Accompaniment Generation Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T23:32:59.467653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad2f62d0-e859-48e2-b291-5fedd0349eb8 · inbound

Transparent Screening for LLM Inference and Training Impacts cites this paper.

Transparent Screening for LLM Inference and Training Impacts Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T01:03:25.335544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1360e7e-94a7-4199-aea4-9630e11cc37f · inbound

Analytic Framework for Estimating Memory Cost cites this paper.

Analytic Framework for Estimating Memory Cost Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T16:36:08.294867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0b7b2df9-d49f-4dab-95a4-69acc9eea716 · inbound

From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint cites this paper.

From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:31:12.887791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0aed9a9-d615-4fde-9f85-985a25b912f2 · inbound

Nf-PEAK: Process-Based Energy Attribution for Nextflow Workflows on Kubernetes Clusters cites this paper.

Nf-PEAK: Process-Based Energy Attribution for Nextflow Workflows on Kubernetes Clusters Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T04:21:03.359209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 10b801a8-f7e6-4ff7-b23b-2374a205d7b0 · inbound

CARINA: Carbon-Aware Execution of Recurrent Industrial Analytics cites this paper.

CARINA: Carbon-Aware Execution of Recurrent Industrial Analytics Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:34:38.791982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d6c0a21a-3de3-4339-8242-572403001e75 · inbound

MedicalRec: Medical recommender system for image classification without retraining cites this paper.

MedicalRec: Medical recommender system for image classification without retraining Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:24:44.741383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f356880-115d-4658-8818-1c2c4be764ec · inbound

Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference cites this paper.

Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:47:28.113144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3786667-94e2-49be-998e-0bf426b6482e · inbound

Domain Adaptation Under Wireless Network Constraints: When Does It Become Green? cites this paper.

Domain Adaptation Under Wireless Network Constraints: When Does It Become Green? Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T12:29:52.137169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 178af1ac-c2a3-42f9-96c5-b38c5b027e9a · inbound

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting cites this paper.

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-02T12:37:16.293093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.