Paper Citation Record · LEDGER

Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2403.03344.

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

pith.paper-citation-record.v1
2403.03344 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:18:48.982183Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T18:50:16.662597Z

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 7c950a4c-1b43-410a-a8e0-c64fe1f03cc0 · inbound

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code cites this paper.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:48.982183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 732b2bae-3c20-4f77-8dea-773d953afa85 · inbound

Evaluating the Energy-Efficiency of the Code Generated by LLMs cites this paper.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:51.077826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f76ee759-d283-458f-a84c-4a440ff483a5 · inbound

From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation cites this paper.

From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T17:14:12.866105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fcfbcda-a5f0-47a8-a145-6c1129be27a3 · inbound

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming cites this paper.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:40.473741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f5f58f1-420b-4067-8af6-17afe9c4d06e · inbound

Sustainable Code Generation Using Large Language Models: A Systematic Literature Review cites this paper.

Sustainable Code Generation Using Large Language Models: A Systematic Literature Review Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.666598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f58aab24-3acb-4ccc-8849-4c2bef79c031 · inbound

An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code cites this paper.

An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:40:10.523117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5181e15d-de18-4a87-8ca2-1310f8048dac · inbound

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation cites this paper.

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:13:13.143364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation af557cc0-c0a7-452f-9fd6-23c36cf4a2c0 · inbound

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning cites this paper.

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-11T17:00:48.664985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.