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Neural networks associated with the paper "Photometric Completeness Modelled With Neural Networks" (Harris & Speagle 2023). Neural networks (`nn_clf_[...].joblib`) are included for all possible parameter combinations and trained over various numbers of artificial star tests (`ngc[...].dat`). See the example notebook (`nn_example.ipynb`) for detailed explanations of the files, their contents, and some usage examples.
globular clusters, completeness function, neural networks
globular clusters, completeness function, neural networks
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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