Paper Citation Record · LEDGER

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2002.05651.

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

pith.paper-citation-record.v1
2002.05651 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:20:32.442462Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T23:14:01.455483Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 45771c1b-98c5-4fd8-be4d-701929844485 · inbound

Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts cites this paper.

Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T15:20:32.442462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d761ae19-9b94-42c4-b4e4-59f53f4fd98e · 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 Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:04:52.555259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6ef82afa-7c87-4ee1-89eb-d00b24442621 · inbound

EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization cites this paper.

EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:48:33.246073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2d024a33-43d2-43a2-a514-1466341b69e0 · inbound

Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search cites this paper.

Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T05:43:58.927750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 270cd1d6-81cc-4684-bd8b-72b4fc57e310 · inbound

Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems cites this paper.

Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:55:24.693448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1fd5264d-9d8e-475b-909d-2f62d85291e8 · inbound

Mapping the Schedule x Bit-Width Boundary in Sub-100M Quantisation-Aware Training cites this paper.

Mapping the Schedule x Bit-Width Boundary in Sub-100M Quantisation-Aware Training Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:14:01.457631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.