Showing 2,041 - 2,060 results of 18,229 for search 'significantly ((((((largest decrease) OR (greater decrease))) OR (we decrease))) OR (a decrease))', query time: 0.75s Refine Results
  1. 2041

    Raw data V-trial. by Torsten Schober (20485754)

    Published 2024
    “…Empirical models for the relationships between the investigated plant traits and PD/DVP were created using linear regression analysis preceded by a lack-of-fit test. An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m<sup>2</sup>. …”
  2. 2042

    Raw data D-trial. by Torsten Schober (20485754)

    Published 2024
    “…Empirical models for the relationships between the investigated plant traits and PD/DVP were created using linear regression analysis preceded by a lack-of-fit test. An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m<sup>2</sup>. …”
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  19. 2059

    Detailed information of the observation datasets. by Weidong Ji (129916)

    Published 2025
    “…On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”
  20. 2060

    General technical specification for GW154/6700. by Weidong Ji (129916)

    Published 2025
    “…On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”