Paper Citation Record · LEDGER

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements

As of 16 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 3 inbound Pith citation observations for arXiv:2509.22092.

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

pith.paper-citation-record.v1
2509.22092 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T06:27:43.534185Z

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

64 of 64 outbound references displayed

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External citation measurements

1
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Outbound references

Observation 5fa54e71-7f10-41cb-b463-25cdeeddfcef · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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This paper cites Phi-4 Technical Report.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Phi-4 Technical Report

Reference 2

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Unresolved cited work

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This paper cites Tradi- tional Vs Smart Electricity Metering Systems: A Brief Overview.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Tradi- tional Vs Smart Electricity Metering Systems: A Brief Overview

Reference 4

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This paper cites The Values Encoded in Ma- chine Learning Research.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements The Values Encoded in Ma- chine Learning Research

Reference 5

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This paper cites The OpenCV Library.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements The OpenCV Library

Reference 6

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This paper cites Accessed: 2025-09-12.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Accessed: 2025-09-12

Reference 7

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This paper cites Accessed: 2025-09-12.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Accessed: 2025-09-12

Reference 8

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This paper cites Xception: Deep Learning with Depthwise Separable Convolutions.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Xception: Deep Learning with Depthwise Separable Convolutions

Reference 9

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This paper cites May 2024.DOI:10.5281/zenodo.11171501.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements May 2024.DOI:10.5281/zenodo.11171501

Reference 10

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 11

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Imagenet: A Large-Scale Hierarchical Image Database

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements 2025.DOI:10.48550/arXiv

Reference 13

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This paper cites Advancing the Sustainability of Machine Learning and Artificial Intelligence via La- beling and Meta-Learning.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Advancing the Sustainability of Machine Learning and Artificial Intelligence via La- beling and Meta-Learning

Reference 14

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This paper cites Towards More Sustainable and Trustworthy Reporting in Machine Learning.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Towards More Sustainable and Trustworthy Reporting in Machine Learning

Reference 15

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This paper cites AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting

Reference 16

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This paper cites A Unified Framework for As- sessing Energy Efficiency of Machine Learning.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements A Unified Framework for As- sessing Energy Efficiency of Machine Learning

Reference 17

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements MetaQuRe: Meta-learning from Model Quality and Resource Consumption

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Estimation of Energy Con- sumption in Machine Learning

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Gemma 3 Technical Report

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements The Llama 3 Herd of Models

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Deep Residual Learning for Image Recognition

Reference 22

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Identity Mappings in Deep Resid- ual Networks

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Towards Power Efficiency in Deep Learning on Data Center Hardware

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements 2017.DOI:10

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Searching for MobileNetV3

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Densely Connected Convolutional Networks

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Accessed: 2025-09-08

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Mistral 7B

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements How Can Artificial Intelligence Impact Sustainability: A Systematic Literature Re- view

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements 2025.DOI:10

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Artificial Intelligence as a Ser- vice

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements A ConvNet for the 2020s

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Power Hungry Processing: Watts Driving the Cost of AI Deployment?

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Quantifying the Carbon Emis- sions of Machine Learning

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements 2023.URL:https : / / huggingface

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Magistral

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Accessed: 2025-09-08

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements gpt-oss-120b & gpt-oss-20b Model Card

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Scikit-learn: Machine Learn- ing in Python

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Accessed: 2025-08-13

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements MobileNetV2: Inverted Residu- als and Linear Bottlenecks

Reference 45

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Green AI

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Compute Trends Across Three Eras of Machine Learning

Reference 47

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Very Deep Convolutional Networks for Large-Scale Image Recognition

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Stress-Testing USB Accelerators for Efficient Edge Inference

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Energy and Policy Considerations for Modern Deep Learning Research

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Inception-v4, Inception- ResNet and the Impact of Residual Connections on Learning

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Rethinking the Inception Ar- chitecture for Computer Vision

Reference 52

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements EfficientNet: Rethink- ing Model Scaling for Convolutional Neural Net- works

Reference 53

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements EfficientNetV2: Smaller Models and Faster Training

Reference 54

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Observation 7d123e4f-fa86-44b4-955e-a10e0ca0d043 · outbound

This paper cites 2 OLMo 2 Furious.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements 2 OLMo 2 Furious

Reference 55

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Unresolved cited work

Reference 56

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This paper cites Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI

Reference 57

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Sustainable AI: Environmental Implications, Challenges and Opportunities

Reference 58

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Sustainable AI: AI for Sus- tainability and the Sustainability of AI

Reference 59

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Qwen3 Technical Report

Reference 60

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Accelerating the Machine Learning Lifecycle with MLflow

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Accessed: 2025-09-12

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:47:24.096463Z

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This paper cites TinyLlama: An Open-Source Small Language Model.

Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements TinyLlama: An Open-Source Small Language Model

Reference 63

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Energy-efficient LLM Training in GPU datacenters with Immersion Cooling Systems

Reference 64

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Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements Learning Transferable Architec- tures for Scalable Image Recognition

Reference 65

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Pith citing papers

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Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification cites this paper.

Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements

Reference 17

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From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-13T02:17:11.579270Z

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Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs cites this paper.

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Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-13T02:17:11.579270Z

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