Search alternatives:
significantly influenced » significantly increased (Expand Search), significantly reduced (Expand Search)
influenced decrease » influences disease (Expand Search)
linear decrease » linear increase (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
significantly influenced » significantly increased (Expand Search), significantly reduced (Expand Search)
influenced decrease » influences disease (Expand Search)
linear decrease » linear increase (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
-
1
-
2
-
3
-
4
-
5
-
6
The flexural lumber properties of Pinus patula Schiede ex Schltdl. & Cham. improve with decreasing initial tree spacing
Published 2025“…After accounting for ring width differences, there remained a significant effect of initial spacing on the parameters of models predicting microfibril angle and wood density.…”
-
7
-
8
Structure diagram of ensemble model.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
9
Fitting formula parameter table.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
10
Test plan.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
11
Fitting surface parameters.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
12
Model generalisation validation error analysis.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
13
Empirical model prediction error analysis.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
14
Fitting curve parameters.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
15
Test instrument.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
16
Empirical model establishment process.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
17
Model prediction error trend chart.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
18
Basic physical parameters of red clay.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
19
BP neural network structure diagram.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”
-
20
Structure diagram of GBDT model.
Published 2024“…Furthermore, we quantitatively analyze the specific influence of water content and other factors on the thermal conductivity of stabilized soil and construct a comprehensive prediction model encompassing BP neural network, gradient boosting decision tree, and linear regression models. …”