<|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:"Data-driven Human Mobility Modeling: A Survey and Engineering Guidance for Mobile Networking."
Abstract:"Over the last decades, modeling of user mobility has become increasingly important in mobile networking research and development. This has led to the adoption of modeling techniques from other disciplines such as kinetic theory or urban planning. Yet these techniques generate movement behavior that is often perceived as not realistic for humans or provides only a macroscopic view on mobility. More recent approaches infer mobility models from real traces provided by positioning technologies or by the marks the mobile users leave in the wireless network. However, there is no common framework for assessing and comparing mobility models. In an attempt to provide a solid foundation for realistic mobility modeling in mobile networking research, we take an engineering approach and thoroughly discuss the required steps of model creation and validation. In this context, we survey how and to what extent existing mobility modeling approaches implement the proposed steps. This also summarizes helpful information for readers who do not want to develop a new model, but rather intend to choose among existing ones.."
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 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [0];
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Data-driven Human Mobility Modeling: A Survey and Engineering Guidance for Mobile Networking
Over the last decades, modeling of user mobility has become increasingly important in mobile networking research and development. This has led to the adoption of modeling techniques from other disciplines such as kinetic theory or urban planning. Yet these techniques generate movement behavior that is often perceived as not realistic for humans or provides only a macroscopic view on mobility. More recent approaches infer mobility models from real traces provided by positioning technologies or by the marks the mobile users leave in the wireless network. However, there is no common framework for assessing and comparing mobility models. In an attempt to provide a solid foundation for realistic mobility modeling in mobile networking research, we take an engineering approach and thoroughly discuss the required steps of model creation and validation. In this context, we survey how and to what extent existing mobility modeling approaches implement the proposed steps. This also summarizes helpful information for readers who do not want to develop a new model, but rather intend to choose among existing ones.