<|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:"Gauging urban resilience in the United States during the COVID-19 pandemic via social network analysis."
Abstract:"Social distancing policies and other restrictive measures have demonstrated efficacy in curbing the spread of the COVID-19 pandemic. However, these interventions have concurrently led to short-and long-term alterations in social connectedness. Comprehending the transformation in intracity social interactions is imperative for facil-itating post-pandemic recovery and development. In this research, we employ social network analysis (SNA) to delve into the nuances of urban resilience. Specifically, we constructed intricate networks utilizing human mobility data to represent the impact of social interactions on the structural attributes of social networks while assessing urban resilience by examining the stability features of social connectedness. Our findings disclose a diverse array of responses to social distancing policies regarding social connectedness and varied social reactions across U.S. Metropolitan Statistical Areas (MSAs). Social networks generally exhibited a shift from dense to sparse configurations during restrictive orders, followed by a transition from sparse to dense arrangements upon relaxation of said orders. Furthermore, we analyzed the alterations in social connectedness as demonstrated by network centrality, which can presumably be attributed to the rigidity of policies and the inherent qualities of the examined MSAs. Our findings contribute valuable scientific insights to support informed decision-making for post-pandemic recovery and development initiatives.."
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:
Q1 = [1];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [1];
Q6 = [1].
Gauging urban resilience in the United States during the COVID-19 pandemic via social network analysis
Social distancing policies and other restrictive measures have demonstrated efficacy in curbing the spread of the COVID-19 pandemic. However, these interventions have concurrently led to short-and long-term alterations in social connectedness. Comprehending the transformation in intracity social interactions is imperative for facil-itating post-pandemic recovery and development. In this research, we employ social network analysis (SNA) to delve into the nuances of urban resilience. Specifically, we constructed intricate networks utilizing human mobility data to represent the impact of social interactions on the structural attributes of social networks while assessing urban resilience by examining the stability features of social connectedness. Our findings disclose a diverse array of responses to social distancing policies regarding social connectedness and varied social reactions across U.S. Metropolitan Statistical Areas (MSAs). Social networks generally exhibited a shift from dense to sparse configurations during restrictive orders, followed by a transition from sparse to dense arrangements upon relaxation of said orders. Furthermore, we analyzed the alterations in social connectedness as demonstrated by network centrality, which can presumably be attributed to the rigidity of policies and the inherent qualities of the examined MSAs. Our findings contribute valuable scientific insights to support informed decision-making for post-pandemic recovery and development initiatives.