<|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:"Understanding bike sharing travel patterns: An analysis of trip data from eight cities."
Abstract:"As a new mobility option, bike sharing is gaining popularity around the world. Understanding the travel patterns of bike sharing trips can provide fundamental basis for researchers to model the use of bike sharing and the associated multi-modal transportation systems, inform bike sharing system design and operation, and guide policy decisions for sustainable transportation development. Using bike sharing trip data from eight cities in the United States, we analyzed the distributions of trip distance and trip duration for bike sharing trips for commuting and touristic purposes. Our results show that both the trip distance and duration follows a lognormal distribution in larger bike sharing systems (e.g., in Boston, Washington DC, Chicago, and New York), while the distribution for smaller systems varies among Weibull, gamma, and lognormal because the systems' geographical boundary restricts the movement of users. Our analysis of the long trips also show that the trip distance and duration also displays a power law decay in the larger systems. (C) 2018 Elsevier B.V. All rights reserved.."
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 = [0];
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Q5 = [];
Q6 = [1].
Understanding bike sharing travel patterns: An analysis of trip data from eight cities
As a new mobility option, bike sharing is gaining popularity around the world. Understanding the travel patterns of bike sharing trips can provide fundamental basis for researchers to model the use of bike sharing and the associated multi-modal transportation systems, inform bike sharing system design and operation, and guide policy decisions for sustainable transportation development. Using bike sharing trip data from eight cities in the United States, we analyzed the distributions of trip distance and trip duration for bike sharing trips for commuting and touristic purposes. Our results show that both the trip distance and duration follows a lognormal distribution in larger bike sharing systems (e.g., in Boston, Washington DC, Chicago, and New York), while the distribution for smaller systems varies among Weibull, gamma, and lognormal because the systems' geographical boundary restricts the movement of users. Our analysis of the long trips also show that the trip distance and duration also displays a power law decay in the larger systems. (C) 2018 Elsevier B.V. All rights reserved.