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  • วารสารสังคมศาสตร์ มหาวิทยาลัยนเรศวร, 18, 2, 39-72

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  • วารสารดุษฎีบัณฑิตทางสังคมศาสตร์, 11, 2, 536-550

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    Authors: Brian R. Stockwell; Elaine Bradley; Deborah Davis; Jonathan Smith;

    A new rural development paradigm has emerged over the last decade. It is multifaceted by nature, connecting practices of landscape management, agritourism, organic and sustainable farming, and value-chain analysis and management. Increased food production in peri-urban areas in the developed world is typical of this new paradigm. Peri-urban areas are the transitional zones between rural and urban landscapes that experience constant population change and disturbance of traditional social, environmental, and economic characteristics. Sustainable community development initiatives are complicated in these fragmented and often contested landscapes. A case study on Australia's Sunshine Coast analyzes the challenges and opportunities of reconfiguring agri-food production systems to achieve the type of multifunctional landscape preferred by the community and primary producers alike. Scenario analysis, interviews, and surveys of traditional midscale farmers with more recent micro- to small primary producers and food artisans provide insight into the challenges faced at a grassroots level. The role of government in facilitating supportive policy and planning and connecting and building the capacity of key actors involved in local and regional food value chains is reviewed. The paper argues that the government is essential to the successful planning and management of peri-urban areas because of the fragmented and/or contested quality of this unique agri-food landscape. Without further investment in place-based collaborative research, planning, capacity building, and economic development, the local food movement in these peri-urban areas is likely to continue to occupy only a narrow "alternative" cultural and economic space.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Qianqian Xie; Dingguo Jiang; Ling Yong; Xiao Xiao; +6 Authors

    AbstractRecently, the safety of anthraquinone (AQ) in Chinese tea has received much attention. This study determined the concentration of AQ in 1573 Chinese tea samples by gas chromatography-tandem mass spectrometry (GC-MS/MS). It was observed that the total detection rate of AQ was 60.97%, and the mean concentration was 0.0170 mg/kg. The concentration levels of AQ in tea varied by different types of tea, different packaging types, different sale spots and different tea-producing areas. The results of the deterministic assessment show that the health risks associated with daily exposure to AQ via tea consumption are low in different populations in China. For the general population, the mean daily exposure of AQ via tea consumption was (2.50 × 10−4) µg/kg body weight (BW), 0.0037% of the acceptable daily intake of AQ (6.8 µg/kg BW). In the different sex-age groups, the highest mean daily exposure of AQ via tea consumption was found in the male group aged ≥ 60 years, which was (2.84 × 10−4) µg/kg BW. The high consumer exposure (95th percentile, P95) was found in the female group aged ≥ 60 years, which was (9.36 × 10−4) µg/kg BW. Green tea is the main type of tea with AQ exposure by Chinese tea consumers.

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    Article . 2024
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    Authors: Anubhav Tripathi; Rashmi Poudel; Reema Gurung; Unisha Ghimire; +3 Authors

    AbstractDrought severely affects maize growth and productivity, making the identification of tolerant genotypes essential, especially in local maize. Therefore, a greenhouse pot experiment was conducted in Sundarbazar, Lamjung, from February to April using 30 local maize accessions under irrigated and drought conditions in a completely randomized block design to study nine vegetative traits and screen drought tolerance for upcoming breeding programs. Accession NGRC05570 showed relative tolerance to root length and number, NGRC05586 to shoot length, NGRC05589 to root-to-shoot ratio, NGRC05591 to moisture content in roots, NGRC05578 to moisture in shoot and relative water content (RWC), NGRC05561 to root volume and NGRC05573 to normalized difference vegetation index (NDVI). Accession NGRC05571, although lacking singular dominance in traits, exhibited an overall tolerance for NDVI, root-to-shoot ratio, root number, root length and RWC. Corelation analysis between shoot moisture content and shoot length showed a significant negative correlation under drought (−0.38) but a significant positive correlation under irrigation (0.38). Similarly, a significant negative correlation between the root-to-shoot ratio and shoot moisture content was observed under drought (−0.62) but not under irrigation. The possibility of accession NGRC05571, collected from Surkhet, being used in upcoming breeding studies has been seen, but a study on its ability to tolerate reproductive drought would be more insightful.

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    Authors: Ruohao Yuan; Yumeng Luo; Fanshu Xu; Xu Wang; +2 Authors

    AbstractIn response to the challenges posed by rugged terrain in Yunnan, hindering large-scale mudslide screening efforts, this article introduces a dual-channel Convolutional Neural Network (CNN) constructed using elevation data from historical mudslide-prone valleys (Digital Elevation Model, DEM) and remote sensing imagery. The network is designed to facilitate the comprehensive assessment of potential mudslide hazards in gullies, serving as a crucial tool for early mudslide disaster warning. The model initially employs an enhanced residual structure to extract fundamental features from both types of data. Subsequently, it leverages the SE module and deep separable structure to emphasize the importance of relevant features and expedite model convergence. Finally, the model classifies the gullies under evaluation based on their similarity to gullies where mudslides have previously occurred. Experimental results demonstrate the model’s robust performance in assessing mudflow-prone gullies, achieving an impressive precision rate of up to 81.10% and a recall rate of 82.76%. When applied to evaluate the potential hazard of mudslide gullies across the entirety of Nujiang Prefecture, the model predicts that 87.80% of the mudslide locations are at an extremely high risk. These findings underscore the viability of utilizing image-based gully feature analysis for assessing the hazard levels of mudslide-prone gullies.

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    Authors: Xiao Lyu; Yuan Liu; Wenlong Peng;

    ABSTRACTCultivated land system confronts with a series of problems hindering its socioeconomic and ecological sustainability. Sustainable intensification of cultivated land use (SICLU) is considered as an efficient method to achieve three aspects of sustainable goals in society, economy and ecology simultaneously. Farmers, the main executor of all agricultural strategies, are the core of SICLU research. However, there is a small quantity of researches on SICLU from the perspective of farmers’ social psychology. Therefore, this article measured the level of SICLU at the farmer scale using super-efficiency SBM model, and relying on structural equation modeling and an innovative research framework, explored the association mechanism among farmers’ cognition, intention and behavior of SICLU. Finally, the results showed that (1) most of samples are at a medium or higher level of SICLU. (2) all samples’ cognition are significantly positively related to their behavior and their behavior significantly negatively affect their intention. (3) correlation characteristics among them are slightly different under each level. The proposed research outline not only expand the scope of SI research to the perspective of social psychology in breadth, but in depth take targeted actions to protect agricultural environment, make cultivated land system sustainable and alleviate food crisis.

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    Authors: Gezimu Gelu; Chanako Dane; Alemnesh Ayza; Markos Habtewold;

    AbstractThe experiment was conducted to determine the irrigation scheduling effect on hot pepper green pods, yield attributes, and irrigation water productivity (WP) in the Arba Minch area. Field trials comprised five levels of treatments: 140 % MAD, 120% MAD, 100% MAD, 80% MAD and 60% MAD. The results revealed that different depletion levels had significantly affected hot pepper’s yield and related attributes. The maximum yield of hot pepper was observed under 100% MAD without significant variation of 120% MAD whereas the minimum was under 140% MAD in both years of experimentation. WP was also highly influenced by depletion, and the maximum water productivity of (24.67 kg/ha-mm, 24.72 kg/ha-mm was observed under 120% MAD and minimum (19.26 kg/ha-mm, 16.49 kg/ha-mm) under 60% MAD in the year 2019 and 2021, respectively. The results revealed that as the level of depletion increased, irrigation frequency (event) increased, yield and water productivity decreased of hot peppers. 120% MAD offered the highest economic return (11,795.96 US$) and saved water, wage, and irrigation events compared to more frequent applications. The current findings showed that applying 120% MAD is better for hot pepper production in the Arba Minch areas and similar ecology.

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    Authors: Emmanuel Tetteh Jumpah; Abdulai Adams; Tomas Ratinger; Bernard Kwamena Cobinna Essel; +1 Authors

    AbstractAdopting sustainable intensification technologies improves the productivity, incomes, and livelihoods of small farm households; however, their adoption remains disproportionately low. Although soil, climate and vegetative cover are important factors in determining the viability of sustainable intensification technologies in agronomic trials or field experiments, they are rarely considered in socio-economic analyses of smallholder adoption decisions. Using agroecological zones as proxies for similar soil, climate, and vegetation conditions in two agroecological zones in northern Ghana, we examined their effects on smallholder farmers’ decisions to adopt sustainable intensification technologies. We applied a multistage sampling technique to obtain data from 461 small farm households in the Sudan savannah and Guinea savannah agroecological zones. We observed a statistically significant difference in the covariates of adopters and non-adopters of sustainable intensification technologies in terms of age (6.389, p < 0.010), education (0.106, p < 0.010), group membership (0.648, p < 0.010), access to extension services (1.274, p < 0.010) and access to credit (GH¢52.436, p < 0.010). The odds of sustainable intensification technologies adoption depend on age, number of agricultural extension visits, group or farmer-based organisation membership, education and agroecological factors (2.140, p < 0.050). The study provides an opportunity for future research to consider the inclusion of farm-level measurement of agroecological variables in explaining the adoption behaviour of small farm households.

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    Authors: Seyed Vahid Razavi-Termeh; Abolghasem Sadeghi-Niaraki; Saman Razavi; Soo-Mi Choi;

    ABSTRACTThis study focuses on determining the optimal distance metric in the K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four distance metrics of the KNN algorithm, namely KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized for flood susceptibility mapping (FSM) in Estahban, Iran. A spatial database comprising 509 flood occurrence points extracted from satellite images and 12 factors influencing floods was created for analysis. The particle swarm optimization (PSO) algorithm was employed for hyperparameter optimization and feature selection, considering eight influential factors as modelling inputs. The modelling results revealed that the KNN-Manhattan algorithm exhibited superior accuracy (root mean squared error (RMSE) = 0.169, mean absolute error (MAE) = 0.051, coefficient of determination (R2) = 0.884, and area under the curve (AUC) = 0.94) compared with the other algorithms for identifying flood-prone areas. The KNN-Minkowski algorithm followed closely, with an RMSE of 0.175, MAE of 0.056, R2 of 0.876, and AUC of 0.939. The KNN-Euclidean algorithm achieved an RMSE of 0.183, MAE of 0.061, R2 of 0.842, and AUC of 0.929, whereas the KNN-Chebyshev algorithm achieved an RMSE of 0.198, MAE of 0.075, R2 of 0.842, and AUC of 0.924.

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  • วารสารสังคมศาสตร์ มหาวิทยาลัยนเรศวร, 18, 2, 39-72

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  • วารสารดุษฎีบัณฑิตทางสังคมศาสตร์, 11, 2, 536-550

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    Authors: Brian R. Stockwell; Elaine Bradley; Deborah Davis; Jonathan Smith;

    A new rural development paradigm has emerged over the last decade. It is multifaceted by nature, connecting practices of landscape management, agritourism, organic and sustainable farming, and value-chain analysis and management. Increased food production in peri-urban areas in the developed world is typical of this new paradigm. Peri-urban areas are the transitional zones between rural and urban landscapes that experience constant population change and disturbance of traditional social, environmental, and economic characteristics. Sustainable community development initiatives are complicated in these fragmented and often contested landscapes. A case study on Australia's Sunshine Coast analyzes the challenges and opportunities of reconfiguring agri-food production systems to achieve the type of multifunctional landscape preferred by the community and primary producers alike. Scenario analysis, interviews, and surveys of traditional midscale farmers with more recent micro- to small primary producers and food artisans provide insight into the challenges faced at a grassroots level. The role of government in facilitating supportive policy and planning and connecting and building the capacity of key actors involved in local and regional food value chains is reviewed. The paper argues that the government is essential to the successful planning and management of peri-urban areas because of the fragmented and/or contested quality of this unique agri-food landscape. Without further investment in place-based collaborative research, planning, capacity building, and economic development, the local food movement in these peri-urban areas is likely to continue to occupy only a narrow "alternative" cultural and economic space.

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    Authors: Qianqian Xie; Dingguo Jiang; Ling Yong; Xiao Xiao; +6 Authors

    AbstractRecently, the safety of anthraquinone (AQ) in Chinese tea has received much attention. This study determined the concentration of AQ in 1573 Chinese tea samples by gas chromatography-tandem mass spectrometry (GC-MS/MS). It was observed that the total detection rate of AQ was 60.97%, and the mean concentration was 0.0170 mg/kg. The concentration levels of AQ in tea varied by different types of tea, different packaging types, different sale spots and different tea-producing areas. The results of the deterministic assessment show that the health risks associated with daily exposure to AQ via tea consumption are low in different populations in China. For the general population, the mean daily exposure of AQ via tea consumption was (2.50 × 10−4) µg/kg body weight (BW), 0.0037% of the acceptable daily intake of AQ (6.8 µg/kg BW). In the different sex-age groups, the highest mean daily exposure of AQ via tea consumption was found in the male group aged ≥ 60 years, which was (2.84 × 10−4) µg/kg BW. The high consumer exposure (95th percentile, P95) was found in the female group aged ≥ 60 years, which was (9.36 × 10−4) µg/kg BW. Green tea is the main type of tea with AQ exposure by Chinese tea consumers.

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      Cogent Food &amp; Agriculture
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    Authors: Anubhav Tripathi; Rashmi Poudel; Reema Gurung; Unisha Ghimire; +3 Authors

    AbstractDrought severely affects maize growth and productivity, making the identification of tolerant genotypes essential, especially in local maize. Therefore, a greenhouse pot experiment was conducted in Sundarbazar, Lamjung, from February to April using 30 local maize accessions under irrigated and drought conditions in a completely randomized block design to study nine vegetative traits and screen drought tolerance for upcoming breeding programs. Accession NGRC05570 showed relative tolerance to root length and number, NGRC05586 to shoot length, NGRC05589 to root-to-shoot ratio, NGRC05591 to moisture content in roots, NGRC05578 to moisture in shoot and relative water content (RWC), NGRC05561 to root volume and NGRC05573 to normalized difference vegetation index (NDVI). Accession NGRC05571, although lacking singular dominance in traits, exhibited an overall tolerance for NDVI, root-to-shoot ratio, root number, root length and RWC. Corelation analysis between shoot moisture content and shoot length showed a significant negative correlation under drought (−0.38) but a significant positive correlation under irrigation (0.38). Similarly, a significant negative correlation between the root-to-shoot ratio and shoot moisture content was observed under drought (−0.62) but not under irrigation. The possibility of accession NGRC05571, collected from Surkhet, being used in upcoming breeding studies has been seen, but a study on its ability to tolerate reproductive drought would be more insightful.

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    Authors: Ruohao Yuan; Yumeng Luo; Fanshu Xu; Xu Wang; +2 Authors

    AbstractIn response to the challenges posed by rugged terrain in Yunnan, hindering large-scale mudslide screening efforts, this article introduces a dual-channel Convolutional Neural Network (CNN) constructed using elevation data from historical mudslide-prone valleys (Digital Elevation Model, DEM) and remote sensing imagery. The network is designed to facilitate the comprehensive assessment of potential mudslide hazards in gullies, serving as a crucial tool for early mudslide disaster warning. The model initially employs an enhanced residual structure to extract fundamental features from both types of data. Subsequently, it leverages the SE module and deep separable structure to emphasize the importance of relevant features and expedite model convergence. Finally, the model classifies the gullies under evaluation based on their similarity to gullies where mudslides have previously occurred. Experimental results demonstrate the model’s robust performance in assessing mudflow-prone gullies, achieving an impressive precision rate of up to 81.10% and a recall rate of 82.76%. When applied to evaluate the potential hazard of mudslide gullies across the entirety of Nujiang Prefecture, the model predicts that 87.80% of the mudslide locations are at an extremely high risk. These findings underscore the viability of utilizing image-based gully feature analysis for assessing the hazard levels of mudslide-prone gullies.

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    Authors: Xiao Lyu; Yuan Liu; Wenlong Peng;

    ABSTRACTCultivated land system confronts with a series of problems hindering its socioeconomic and ecological sustainability. Sustainable intensification of cultivated land use (SICLU) is considered as an efficient method to achieve three aspects of sustainable goals in society, economy and ecology simultaneously. Farmers, the main executor of all agricultural strategies, are the core of SICLU research. However, there is a small quantity of researches on SICLU from the perspective of farmers’ social psychology. Therefore, this article measured the level of SICLU at the farmer scale using super-efficiency SBM model, and relying on structural equation modeling and an innovative research framework, explored the association mechanism among farmers’ cognition, intention and behavior of SICLU. Finally, the results showed that (1) most of samples are at a medium or higher level of SICLU. (2) all samples’ cognition are significantly positively related to their behavior and their behavior significantly negatively affect their intention. (3) correlation characteristics among them are slightly different under each level. The proposed research outline not only expand the scope of SI research to the perspective of social psychology in breadth, but in depth take targeted actions to protect agricultural environment, make cultivated land system sustainable and alleviate food crisis.

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    Authors: Gezimu Gelu; Chanako Dane; Alemnesh Ayza; Markos Habtewold;

    AbstractThe experiment was conducted to determine the irrigation scheduling effect on hot pepper green pods, yield attributes, and irrigation water productivity (WP) in the Arba Minch area. Field trials comprised five levels of treatments: 140 % MAD, 120% MAD, 100% MAD, 80% MAD and 60% MAD. The results revealed that different depletion levels had significantly affected hot pepper’s yield and related attributes. The maximum yield of hot pepper was observed under 100% MAD without significant variation of 120% MAD whereas the minimum was under 140% MAD in both years of experimentation. WP was also highly influenced by depletion, and the maximum water productivity of (24.67 kg/ha-mm, 24.72 kg/ha-mm was observed under 120% MAD and minimum (19.26 kg/ha-mm, 16.49 kg/ha-mm) under 60% MAD in the year 2019 and 2021, respectively. The results revealed that as the level of depletion increased, irrigation frequency (event) increased, yield and water productivity decreased of hot peppers. 120% MAD offered the highest economic return (11,795.96 US$) and saved water, wage, and irrigation events compared to more frequent applications. The current findings showed that applying 120% MAD is better for hot pepper production in the Arba Minch areas and similar ecology.

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    Authors: Emmanuel Tetteh Jumpah; Abdulai Adams; Tomas Ratinger; Bernard Kwamena Cobinna Essel; +1 Authors

    AbstractAdopting sustainable intensification technologies improves the productivity, incomes, and livelihoods of small farm households; however, their adoption remains disproportionately low. Although soil, climate and vegetative cover are important factors in determining the viability of sustainable intensification technologies in agronomic trials or field experiments, they are rarely considered in socio-economic analyses of smallholder adoption decisions. Using agroecological zones as proxies for similar soil, climate, and vegetation conditions in two agroecological zones in northern Ghana, we examined their effects on smallholder farmers’ decisions to adopt sustainable intensification technologies. We applied a multistage sampling technique to obtain data from 461 small farm households in the Sudan savannah and Guinea savannah agroecological zones. We observed a statistically significant difference in the covariates of adopters and non-adopters of sustainable intensification technologies in terms of age (6.389, p < 0.010), education (0.106, p < 0.010), group membership (0.648, p < 0.010), access to extension services (1.274, p < 0.010) and access to credit (GH¢52.436, p < 0.010). The odds of sustainable intensification technologies adoption depend on age, number of agricultural extension visits, group or farmer-based organisation membership, education and agroecological factors (2.140, p < 0.050). The study provides an opportunity for future research to consider the inclusion of farm-level measurement of agroecological variables in explaining the adoption behaviour of small farm households.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Cogent Food &amp; Ag...arrow_drop_down
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    Cogent Food &amp; Agriculture
    Article . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Cogent Food &amp; Ag...arrow_drop_down
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      Cogent Food &amp; Agriculture
      Article . 2024
      Data sources: DOAJ
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    Authors: Seyed Vahid Razavi-Termeh; Abolghasem Sadeghi-Niaraki; Saman Razavi; Soo-Mi Choi;

    ABSTRACTThis study focuses on determining the optimal distance metric in the K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four distance metrics of the KNN algorithm, namely KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized for flood susceptibility mapping (FSM) in Estahban, Iran. A spatial database comprising 509 flood occurrence points extracted from satellite images and 12 factors influencing floods was created for analysis. The particle swarm optimization (PSO) algorithm was employed for hyperparameter optimization and feature selection, considering eight influential factors as modelling inputs. The modelling results revealed that the KNN-Manhattan algorithm exhibited superior accuracy (root mean squared error (RMSE) = 0.169, mean absolute error (MAE) = 0.051, coefficient of determination (R2) = 0.884, and area under the curve (AUC) = 0.94) compared with the other algorithms for identifying flood-prone areas. The KNN-Minkowski algorithm followed closely, with an RMSE of 0.175, MAE of 0.056, R2 of 0.876, and AUC of 0.939. The KNN-Euclidean algorithm achieved an RMSE of 0.183, MAE of 0.061, R2 of 0.842, and AUC of 0.929, whereas the KNN-Chebyshev algorithm achieved an RMSE of 0.198, MAE of 0.075, R2 of 0.842, and AUC of 0.924.

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ International Journa...arrow_drop_down
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