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Development of a predictive emissions model using a gradient boosting machine learning method

Research article (Environmental Technology & Innovation, 2020) · cited 65× · AI/ML
academia openalex:W3041189004
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Development of a predictive emissions model using a gradient boosting machine learning method

Summary

Development of a predictive emissions model using a gradient boosting machine learning method is a scholarly article[1].

Key Facts

  • Development of a predictive emissions model using a gradient boosting machine learning method's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Development of a predictive emissions model using a gradient boosting machine learning method. Retrieved May 24, 2026, from https://4ort.xyz/entity/development-of-a-predictive-emissions-model-using-a-gradient-boosting-machine-learning-method
MLA “Development of a predictive emissions model using a gradient boosting machine learning method.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/development-of-a-predictive-emissions-model-using-a-gradient-boosting-machine-learning-method.
BibTeX @misc{4ortxyz_development-of-a-predictive-emissions-model-using-a-gradient-boosting-machine-learning-method_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Development of a predictive emissions model using a gradient boosting machine learning method}}, year = {2026}, url = {https://4ort.xyz/entity/development-of-a-predictive-emissions-model-using-a-gradient-boosting-machine-learning-method}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Development of a predictive emissions model using a gradient boosting machine learning method — https://4ort.xyz/entity/development-of-a-predictive-emissions-model-using-a-gradient-boosting-machine-learning-method (retrieved 2026-05-24)

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