<|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:"Respondent-driven sampling to recruit in-country migrant workers in China: A methodological assessment."
Abstract:"Aim: Respondent-driven Sampling (RDS) is a new form of chain-referral sampling, which is superior to random sampling and traditional non-probability sampling in hard-to-reach populations. We employed RDS to recruit internal migrant workers in Chengdu, Sichuan Province, China, and examined whether it could be successfully used in this population. Methods: 1,270 migrant workers were recruited into the study. Social demographic information, social network size, and geographic information about participants' residential locations were collected. RDSAT software and geographic information system (GIS) technology were used to examine whether RDS was successful. Results: The results showed that the sample compositions converged to equilibrium very quickly. Sample representativeness testing results showed that females (t = 3.61, p < 0.001) and people aged 46 years old and above (t = 3.222, p < 0.001) were under-represented. GIS results showed that respondents were concentrated in the vicinity of the third road ring of Chengdu, especially in the areas of 3, 4 and 21, which were close to the investigation site. Conclusions: The results demonstrated that RDS is a robust sampling method in the study of migrant workers. Despite its potential utility, it is also important to recognize and mitigate potential limitations, such as geographic proximity.."
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 = [3];
Q3 = [];
Q4 = [0];
Q5 = [1];
Q6 = [1].
Respondent-driven sampling to recruit in-country migrant workers in China: A methodological assessment
Aim: Respondent-driven Sampling (RDS) is a new form of chain-referral sampling, which is superior to random sampling and traditional non-probability sampling in hard-to-reach populations. We employed RDS to recruit internal migrant workers in Chengdu, Sichuan Province, China, and examined whether it could be successfully used in this population. Methods: 1,270 migrant workers were recruited into the study. Social demographic information, social network size, and geographic information about participants' residential locations were collected. RDSAT software and geographic information system (GIS) technology were used to examine whether RDS was successful. Results: The results showed that the sample compositions converged to equilibrium very quickly. Sample representativeness testing results showed that females (t = 3.61, p < 0.001) and people aged 46 years old and above (t = 3.222, p < 0.001) were under-represented. GIS results showed that respondents were concentrated in the vicinity of the third road ring of Chengdu, especially in the areas of 3, 4 and 21, which were close to the investigation site. Conclusions: The results demonstrated that RDS is a robust sampling method in the study of migrant workers. Despite its potential utility, it is also important to recognize and mitigate potential limitations, such as geographic proximity.