<|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:"Refugees' and irregular migrants' self-selection into Europe."
Abstract:"We provide the first large-scale evidence on self-selection of refugees and irregular migrants who arrived in Europe in 2015 or 2016. Our analysis uses unique datasets from the International Organization for Migration and Gallup World Polls. We find that refugees are positively self-selected with respect to human capital, as are female irregular migrants. Male irregular migrants are negatively self-selected. These patterns hold whether analyzing individually stated main reason to emigrate, country-level conflict intensity, or sub-regional conflict intensity. Several additional analyses show that our results are unlikely to be driven by omitted variable bias or liquidity constraints. We offer a theoretical framework to explain these patterns, by extending the Roy-Borjas model to include risks related to staying in an unsafe country of origin, risks related to migration, and gender-specific returns to human capital.."
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 = [4,5];
Q3 = [];
Q4 = [0];
Q5 = [1];
Q6 = [1].
Refugees' and irregular migrants' self-selection into Europe
We provide the first large-scale evidence on self-selection of refugees and irregular migrants who arrived in Europe in 2015 or 2016. Our analysis uses unique datasets from the International Organization for Migration and Gallup World Polls. We find that refugees are positively self-selected with respect to human capital, as are female irregular migrants. Male irregular migrants are negatively self-selected. These patterns hold whether analyzing individually stated main reason to emigrate, country-level conflict intensity, or sub-regional conflict intensity. Several additional analyses show that our results are unlikely to be driven by omitted variable bias or liquidity constraints. We offer a theoretical framework to explain these patterns, by extending the Roy-Borjas model to include risks related to staying in an unsafe country of origin, risks related to migration, and gender-specific returns to human capital.