Paper Citation Record · LEDGER

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling

As of 13 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2411.19124.

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

pith.paper-citation-record.v1
2411.19124 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:33:44.747161Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 11fd217a-d9a9-476a-ae6e-df4c948a25b5 · outbound

This paper cites Experimental analysis of R-450A and R-513A as replacements of R-134a and R-507A in a medium temperature commercial refrigeration system,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Experimental analysis of R-450A and R-513A as replacements of R-134a and R-507A in a medium temperature commercial refrigeration system,

Reference 1

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.969788Z

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Observation 7b73decf-367d-453d-8fa9-2d851a9ea8eb · outbound

This paper cites A review of bottom-up and top-down emission estimates of hydrofluorocarbons (HFCs) in different parts of the world,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling A review of bottom-up and top-down emission estimates of hydrofluorocarbons (HFCs) in different parts of the world,

Reference 2

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Observation 99b631c7-efc8-48ef-8b8e-b427ff26dbaf · outbound

This paper cites Electricity savings and greenhouse gas emission reductions from global phase-down of hydrofluorocarbons,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Electricity savings and greenhouse gas emission reductions from global phase-down of hydrofluorocarbons,

Reference 3

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.958472Z

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Observation aa3d894f-4fe8-4a1f-bb82-cf7a23ac5f61 · outbound

This paper cites Future atmospheric abundances and climate forcings from scenarios of global and regional hydrofluorocarbon (HFC) emissions,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Future atmospheric abundances and climate forcings from scenarios of global and regional hydrofluorocarbon (HFC) emissions,

Reference 4

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.947477Z

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Observation 6b24cad4-1214-426c-81e1-77f45f4d4abc · outbound

This paper cites Machine learning for sustainable development: leveraging technology for a greener future,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Machine learning for sustainable development: leveraging technology for a greener future,

Reference 5

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.936407Z

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Observation 3e0f30b5-52ac-467c-b117-7d77715754ad · outbound

This paper cites Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning,

Reference 6

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.925868Z

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Observation aa9862b7-ac7c-49ba-b7b8-2a79ffb6a6cd · outbound

This paper cites Prediction of global warming potentials of refrigerants and related compounds from their molecular structure – An artificial neural network with group contribution method,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Prediction of global warming potentials of refrigerants and related compounds from their molecular structure – An artificial neural network with group contribution method,

Reference 7

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.915288Z

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Observation 0a92b776-cd13-49a5-9b5f-344381d06ced · outbound

This paper cites Group contribution-based property estimation methods: advances and perspectives,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Group contribution-based property estimation methods: advances and perspectives,

Reference 8

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.902944Z

Source-reported events for the cited work

correction dated 2021-12-10. Source: crossref record 10.1016/j.coche.2021.100775->10.1016/j.coche.2019.04.007:correction, observed 2026-07-11T03:14:22.341159+00:00. This notice travels one citation hop only.

Observation f250a9d3-905a-47b5-a589-8bc614ea6945 · outbound

This paper cites Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations,

Reference 9

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.891146Z

Source-reported events for the cited work

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Observation 787a0704-68ad-4108-971e-c8ed7fda3310 · outbound

This paper cites Improved Machine Learning Models by Data Processing for Predicting Life-Cycle Environmental Impacts of Chemicals,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Improved Machine Learning Models by Data Processing for Predicting Life-Cycle Environmental Impacts of Chemicals,

Reference 10

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.878646Z

Source-reported events for the cited work

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Observation 8fd97121-abc9-49de-bb76-91b63c25458e · outbound

This paper cites Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents,

Reference 11

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.867080Z

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Observation ee804bbc-71cf-4bef-8a97-e08d82666a67 · outbound

This paper cites Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF 6 Replacement Gases,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF 6 Replacement Gases,

Reference 12

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.854903Z

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Observation 3dcbda4f-440e-414a-ad17-f24619404fda · outbound

This paper cites Cambridge: Cambridge University Press, 2023.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Cambridge: Cambridge University Press, 2023

Reference 13

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Observation e0b39064-94fa-4c18-9ad2-982f7cc7f8f8 · outbound

This paper cites Scikit-learn: Machine Learning in Python,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Scikit-learn: Machine Learning in Python,

Reference 14

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Observation 0af6d22e-7e3d-4518-ae8b-297c9cb0fb4d · outbound

This paper cites Using principal component analysis for neural network high-dimensional potential energy surface,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Using principal component analysis for neural network high-dimensional potential energy surface,

Reference 15

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.835687Z

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Observation ecb863ee-658b-4adf-8666-36a7666a144c · outbound

This paper cites Ensemble Learning Models for Food Safety Risk Prediction,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Ensemble Learning Models for Food Safety Risk Prediction,

Reference 16

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.822461Z

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This paper cites Forecasting Corn Yield With Machine Learning Ensembles,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Forecasting Corn Yield With Machine Learning Ensembles,

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-12T10:33:45.140309Z

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Observation d5a4b9df-4ff0-4dab-9d68-d652e4933726 · outbound

This paper cites A new approach for the vanishing gradient problem on sigmoid activation,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling A new approach for the vanishing gradient problem on sigmoid activation,

Reference 18

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Observation c46dccf7-3a8c-4f24-97b5-ce8157094e8f · outbound

This paper cites Infrared band intensities and global warming potentials of CF4 , C2 F6 , C3 F8 , C4 F10 , C5 F12 , and C6 F14,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Infrared band intensities and global warming potentials of CF4 , C2 F6 , C3 F8 , C4 F10 , C5 F12 , and C6 F14,

Reference 19

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.799612Z

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This paper cites The stability of nitrogen-centered radicals,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling The stability of nitrogen-centered radicals,

Reference 20

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.786642Z

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This paper cites Chemistry of Volatile Organic Compounds in the Atmosphere,.

Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling Chemistry of Volatile Organic Compounds in the Atmosphere,

Reference 21

Resolution: verified exact; doi, observed 2026-08-12T10:33:44.775525Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.