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model implementing » model implemented (Expand Search), model implementation (Expand Search), model representing (Expand Search)
python model » python tool (Expand Search), action model (Expand Search), motion model (Expand Search)
model implementing » model implemented (Expand Search), model implementation (Expand Search), model representing (Expand Search)
python model » python tool (Expand Search), action model (Expand Search), motion model (Expand Search)
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281
Genomic Surveillance of Pemivibart (VYD2311) Escape-Associated Mutations in SARS-CoV-2: December 2025 BioSamples (n=2)
Published 2025“…Full source code and version details are available upon request.…”
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282
Microscopic Detection and Quantification of Microplastic Particles in Environmental Water Samples
Published 2025“…Image processing algorithms, implemented in Python using adaptive thresholding techniques, were applied to segment particles from the background. …”
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283
Comprehensive Fluid and Gravitational Dynamics Script for General Symbolic Navier-Stokes Calculations and Validation
Published 2024“…It provides a flexible foundation on which theoretical assumptions can be validated, and practical calculations performed. Implemented in Python with symbolic calculations, the script facilitates in-depth analysis of complex flow patterns and makes advanced mathematical computations more accessible. …”
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284
Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service)
Published 2025“…Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). …”
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285
Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds
Published 2025“…The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. …”
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286
Core data
Published 2025“…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”