Paper Citation Record · LEDGER

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

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 39 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

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Source: paper_references, paper_reference_links

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 39 of 39 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:58:38.455307Z

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

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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 f4d312b5-7646-47dc-ae67-29cdfe82ed16 · inbound

Is Locational Marginal Price All You Need for Locational Marginal Emission? cites this paper.

Is Locational Marginal Price All You Need for Locational Marginal Emission? Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 8

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Observation ea50bf7c-7abf-431f-a643-5b6f60c489ca · inbound

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy cites this paper.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 25

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Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting cites this paper.

Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 42

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Spend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization cites this paper.

Spend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 1

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Observation f1833bbf-65bb-41b6-bd0d-26e3ab758e2e · inbound

RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability cites this paper.

RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 6

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Observation d6b78dc8-ff1f-40c0-abcf-8628f65511dd · inbound

How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts cites this paper.

How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 4

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Observation d39be8a1-37ac-4f76-9df3-7f2da7c55323 · inbound

Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent cites this paper.

Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 4

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Observation e54d76d6-fe98-409a-9b5b-e31e68daa704 · inbound

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models cites this paper.

Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 1

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YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing Multi-Objective Optimization based DPO for Text-to-Image Alignment cites this paper.

YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing Multi-Objective Optimization based DPO for Text-to-Image Alignment Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 2019

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Observation 2cd3fa8d-fd05-46fa-8d26-4ba391a18cd5 · inbound

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models cites this paper.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 3

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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

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Observation c9e10156-24c1-40af-900d-51faa0b2d8ae · inbound

Non-Markovianity and memory enhancement in Quantum Reservoir Computing cites this paper.

Non-Markovianity and memory enhancement in Quantum Reservoir Computing Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 6

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Observation a0eb88be-afc9-458f-955e-4d2c5bf49262 · inbound

Diffused Responsibility: Analyzing the Energy Consumption of Generative Text-to-Audio Diffusion Models cites this paper.

Diffused Responsibility: Analyzing the Energy Consumption of Generative Text-to-Audio Diffusion Models Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 26

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Observation c43beb56-ce64-4602-8e2f-86b6665676cf · inbound

Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers cites this paper.

Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:26.786114Z

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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

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Source-reported events for the cited work

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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

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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

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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

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Observation b555bbab-1635-4e32-879c-637f512a678a · inbound

Towards Sustainability Model Cards cites this paper.

Towards Sustainability Model Cards Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 23

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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

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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

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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

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Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting cites this paper.

Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow 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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Observation 2eb12531-de93-4f94-9129-3de79bef579a · inbound

Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint cites this paper.

Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 52

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Observation a1d077ea-a433-405c-8ca2-ac253b0248ed · inbound

Robustness of transferability estimation metrics for medical imaging cites this paper.

Robustness of transferability estimation metrics for medical imaging Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 3

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Unavailable: canonical work link unavailable.