<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Under the background of healthy China: Regulating the analysis of hybrid machine learning in sports activities to control chronic diseases."
Abstract:"One of the most important concerns in human life is concentrating on health. Major threats to human life are chronic diseases such as cancer and diabetes. China government mainly focusing on understanding the progression and spreading of chronic diseases over the population for allocating medical resources and designing a strategy in healthcare. Various conventional methods have been used for fetching chronic disease indicators in large scale based on the population health. But they are costly, not time effective and less accuracy in prediction. But this paper used Hybrid Predicting Model designed by incorporating the main features of the Gaussian Mixture Method and Collaborating Topic Modelling to increase the predic-tion accuracy. The proposed HPM method experimented on human mobility pattern dataset collected from the various metropolitan area of China. From the dataset, HPM predicts the rate of chronic disease presence and relative activity. GMM obtain the health condition whereas CTM obtains the data sparsity. The proposed hybrid prediction method is implemented in MATLAB software and experimented. Form the obtained results and comparing with the other existing methods, it is identified that the HPM outper-forms in terms of prediction accuracy. HPM is evaluated using real-time check-in and chronic disease dataset in China cities. The proposed HPM method obtained 0.09% of the value which is high than the other baseline methods. From the obtained MSE and value, it is well clear the proposed HPM outperforms than the baseline methods. (C) 2020 Elsevier Ltd. All rights reserved.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:

Under the background of healthy China: Regulating the analysis of hybrid machine learning in sports activities to control chronic diseases
One of the most important concerns in human life is concentrating on health. Major threats to human life are chronic diseases such as cancer and diabetes. China government mainly focusing on understanding the progression and spreading of chronic diseases over the population for allocating medical resources and designing a strategy in healthcare. Various conventional methods have been used for fetching chronic disease indicators in large scale based on the population health. But they are costly, not time effective and less accuracy in prediction. But this paper used Hybrid Predicting Model designed by incorporating the main features of the Gaussian Mixture Method and Collaborating Topic Modelling to increase the predic-tion accuracy. The proposed HPM method experimented on human mobility pattern dataset collected from the various metropolitan area of China. From the dataset, HPM predicts the rate of chronic disease presence and relative activity. GMM obtain the health condition whereas CTM obtains the data sparsity. The proposed hybrid prediction method is implemented in MATLAB software and experimented. Form the obtained results and comparing with the other existing methods, it is identified that the HPM outper-forms in terms of prediction accuracy. HPM is evaluated using real-time check-in and chronic disease dataset in China cities. The proposed HPM method obtained 0.09% of the value which is high than the other baseline methods. From the obtained MSE and value, it is well clear the proposed HPM outperforms than the baseline methods. (C) 2020 Elsevier Ltd. All rights reserved.