IOU values of four algorithms. by Hong Li (20183)

Parameters of ML algorithms. by Md. Maeen Molla (20827041)

Resource Demand and Spatial Coefficients. by Yuhang Wang (332111)

Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Table 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 10_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 3_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 4_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 8_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 6_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 5_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Table 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 7_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

PLS-SEM algorithm. by Ernest Nyamekye (18451455)

The structure of the spatial attention module. by LinNa Li (5665885)

Qualitative comparison of this paper’s algorithm with frontier RGB-D Salient Object Detection models. by Runqing Li (1827358)

Parameters of the SVM algorithm with Gaussian kernel used to generate the predictive model. by M. Isabel Ramos (20565201)

Comparison of target detection algorithms. by Diansheng Zhang (22640973)

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