<|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:"It depends: Partisan evaluation of conditional probability importance."
Abstract:"Policies to suppress rare events such as terrorism often restrict co-occurring categories such as Muslim immigration. Evaluating restrictive policies requires clear thinking about conditional probabilities. For example, terrorism is extremely rare. So even if most terrorist immigrants are Muslim-a high hit rate-the inverse conditional probability of Muslim immigrants being terrorists is extremely low. Yet the inverse conditional probability is more relevant to evaluating restrictive policies such as the threat of terrorism if Muslim immigration were restricted. We suggest that people engage in partisan evaluation of conditional probabilities, judging hit rates as more important when they support politically prescribed restrictive policies. In two studies, supporters of expelling asylum seekers from Tel Aviv, Israel, of banning Muslim immigration and travel to the United States, and of banning assault weapons judged hit rate probabilities (e.g., that terrorists are Muslims) as more important than did policy opponents, who judged the inverse conditional probabilities (e.g., that Muslims are terrorists) as more important. These partisan differences spanned restrictive policies favored by Rightists and Republicans (expelling asylum seekers and banning Muslim travel) and by Democrats (banning assault weapons). Inviting partisans to adopt an unbiased expert's perspective partially reduced these partisan differences. In Study 2 (but not Study 1), partisan differences were larger among more numerate partisans, suggesting that numeracy supported motivated reasoning. These findings have implications for polarization, political judgment, and policy evaluation. Even when partisans agree about what the statistical facts are, they markedly disagree about the relevance of those statistical facts.."
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 = [5];
Q3 = [];
Q4 = [0];
Q5 = [1];
Q6 = [1].
It depends: Partisan evaluation of conditional probability importance
Policies to suppress rare events such as terrorism often restrict co-occurring categories such as Muslim immigration. Evaluating restrictive policies requires clear thinking about conditional probabilities. For example, terrorism is extremely rare. So even if most terrorist immigrants are Muslim-a high hit rate-the inverse conditional probability of Muslim immigrants being terrorists is extremely low. Yet the inverse conditional probability is more relevant to evaluating restrictive policies such as the threat of terrorism if Muslim immigration were restricted. We suggest that people engage in partisan evaluation of conditional probabilities, judging hit rates as more important when they support politically prescribed restrictive policies. In two studies, supporters of expelling asylum seekers from Tel Aviv, Israel, of banning Muslim immigration and travel to the United States, and of banning assault weapons judged hit rate probabilities (e.g., that terrorists are Muslims) as more important than did policy opponents, who judged the inverse conditional probabilities (e.g., that Muslims are terrorists) as more important. These partisan differences spanned restrictive policies favored by Rightists and Republicans (expelling asylum seekers and banning Muslim travel) and by Democrats (banning assault weapons). Inviting partisans to adopt an unbiased expert's perspective partially reduced these partisan differences. In Study 2 (but not Study 1), partisan differences were larger among more numerate partisans, suggesting that numeracy supported motivated reasoning. These findings have implications for polarization, political judgment, and policy evaluation. Even when partisans agree about what the statistical facts are, they markedly disagree about the relevance of those statistical facts.