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Comparative analysis of Sentinel-2 and PlanetScope imagery for chlorophyll-a prediction using machine learning models
Published 2025-03-01“…We developed three scenarios to select key features, aiming to optimize the retrieval of Chl-a for a lake in North Carolina, USA. Utilizing five machine learning models, namely linear regression (LR), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR), we constructed inversion models. …”
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Recombinant factor VIIa: new insights into the mechanism of action through product innovation
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Utilising LEGO® Six Bricks® to Enhance the Pedagogy of Pre-Service Teachers in South Africa
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Unlocking the potential of cacao yield with full sun cultivation
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First record of Phlebia tremelloidea (Bres.) Parmasto in Italy
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Ecological drivers of evolution of swine influenza in the United States: a review
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Navigating a Climate in Crisis through a Biomimetic Epistemology
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Unveiling the antiglioblastoma potential of harmicens, harmine and ferrocene hybrids
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Idiopathic Gingival Hyperplasia: A Case Report with a 17-Year Followup
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Cholecystoduodenal Fistula and Urosepsis in A Febrile Emergency Department Patient: A Case Report
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Sensitivity of dynamical systems to parameters in a convex subset of a topological vector space
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Design of a dual randomized trial in a type 2 hybrid effectiveness—implementation study
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Occupational therapists’ experiences of hegemony in a mental health setting: A practice-based enquiry
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A Study of Epstein-Barr Virus BRLF1 Activity in a Drosophila Model System
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A Customized Local Energy Trading Approach Using a Comprehensive Set of Performance Indices
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Factors associated with cancer treatment delay: a protocol for a systematic review and meta-analysis
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