<|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:"Investigation on changes in the usage patterns of Seoul Bike usage patterns owing to COVID-19 according to pass type."
Abstract:"Human mobility has been significantly impacted by varying degrees of social distancing and stay-at-home directives that have been implemented in many countries to prevent the spread of COVID-19; this effect was observed regardless of the mode of transportation. Several studies have indicated that bike-sharing is a relatively safe option in terms of COVID-19 infection, and more resilient than public transportation. However, previous studies on the effects of COVID-19 on bike-sharing, rarely considered the type of pass in their investigation of the pandemic-induced changes in usage patterns of shared bikes. To overcome this limitation, this study used trip records obtained from Seoul Bike to investigate the changes in usage patterns of shared bikes during the COVID-19 pandemic. The spatiotemporal usage patterns were characterized in this study based on the type of pass. Additionally, using t-tests and k-means clustering, we discovered significant factors that influenced changes in one-day pass usage rates and temporal usage patterns at the station level. Finally, we constructed spatial regression models to estimate changes in bike rentals caused by COVID-19 based on pass type. The findings provided a comprehensive understanding of how bike-sharing usage varies depending on pass type, which is closely related to shared bikes trip purposes.."
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:

Investigation on changes in the usage patterns of Seoul Bike usage patterns owing to COVID-19 according to pass type
Human mobility has been significantly impacted by varying degrees of social distancing and stay-at-home directives that have been implemented in many countries to prevent the spread of COVID-19; this effect was observed regardless of the mode of transportation. Several studies have indicated that bike-sharing is a relatively safe option in terms of COVID-19 infection, and more resilient than public transportation. However, previous studies on the effects of COVID-19 on bike-sharing, rarely considered the type of pass in their investigation of the pandemic-induced changes in usage patterns of shared bikes. To overcome this limitation, this study used trip records obtained from Seoul Bike to investigate the changes in usage patterns of shared bikes during the COVID-19 pandemic. The spatiotemporal usage patterns were characterized in this study based on the type of pass. Additionally, using t-tests and k-means clustering, we discovered significant factors that influenced changes in one-day pass usage rates and temporal usage patterns at the station level. Finally, we constructed spatial regression models to estimate changes in bike rentals caused by COVID-19 based on pass type. The findings provided a comprehensive understanding of how bike-sharing usage varies depending on pass type, which is closely related to shared bikes trip purposes.