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Found 1013 dataset(s) matching "machine learning".
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This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany...
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The open dataset, software, and other files accompanying the manuscript "An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty...
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This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir...
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This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The...
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<p dir="ltr">The dataset contains micrographs of <i>Hoplolaimus</i>, <i>Helicotylenchus</i>, <i>Meloidogyne</i>, <i>Mesocriconema,</i> <i>Pratylenchus</i>, <i>Trichodorus</i>, and...
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A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available...
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This dataset contains a comparison of packet loss counts vs handovers using four different methods: baseline, heuristic, distance, and machine learning, as well as the data used to train a machine...
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Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational cost. This paper posits that...
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These data represent an annotated training data for machine learned life history classification of the daily activity of dabbling ducks (f. Anatidae sf. Anatinae) using hourly GPS data. Each row...
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Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing...
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This dataset contains surface-ocean partial pressure of carbon dioxide (pCO2) that the ensemble mean of six two-step clustering-regression machine learning methods. The ensemble is a combination...
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This child item describes a public-supply delivery machine learning model that was developed to estimate public-supply deliveries. Publicly supplied water may be delivered to domestic users or to...
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This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal...
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We applied machine learning (ML) models to forecast streamflow drought from 1 to 13 weeks into the future at more than 3,000 streamgage locations across the Conterminous United States. We applied...
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Git archive containing Python modules and resources used to generate machine-learning models used in the "Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the...
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The 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods data set models urban settlements in the Continental United States (CONUS) as of 2015. When...
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Global demand for lithium, the primary component of lithium-ion batteries, greatly exceeds known supplies and this imbalance is expected to increase as the world transitions away from fossil fuel...
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This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project, with the objective of developing a machine learning approach to identifying new...
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ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models
This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using...
