Search alternatives:
largest decrease » largest decreases (Expand Search), marked decrease (Expand Search)
linear decrease » linear increase (Expand Search)
larger decrease » marked decrease (Expand Search)
latent decrease » latency decreased (Expand Search), content decreased (Expand Search), greatest decrease (Expand Search)
largest decrease » largest decreases (Expand Search), marked decrease (Expand Search)
linear decrease » linear increase (Expand Search)
larger decrease » marked decrease (Expand Search)
latent decrease » latency decreased (Expand Search), content decreased (Expand Search), greatest decrease (Expand Search)
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741
“Surface Browsing” May Allow “Filter-Feeding” Protozoa to Exert Top-Down Control on Colony-Forming Toxic Cyanobacterial Blooms
Published 2023“…We show that this is not so: the model ciliate Paramecium has an impact on Microcystis populations through grazing, even when large colonies occur, and this leads to a corresponding decrease in toxic microcystins. …”
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742
“Surface Browsing” May Allow “Filter-Feeding” Protozoa to Exert Top-Down Control on Colony-Forming Toxic Cyanobacterial Blooms
Published 2023“…We show that this is not so: the model ciliate Paramecium has an impact on Microcystis populations through grazing, even when large colonies occur, and this leads to a corresponding decrease in toxic microcystins. …”
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743
“Surface Browsing” May Allow “Filter-Feeding” Protozoa to Exert Top-Down Control on Colony-Forming Toxic Cyanobacterial Blooms
Published 2023“…We show that this is not so: the model ciliate Paramecium has an impact on Microcystis populations through grazing, even when large colonies occur, and this leads to a corresponding decrease in toxic microcystins. …”
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Data_Sheet_1_Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach.pdf
Published 2020“…This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. …”
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759
Data_Sheet_1_Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach.pdf
Published 2020“…This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. …”
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760
Data_Sheet_1_Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach.pdf
Published 2024“…This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. …”