<|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:"Characterization of human mobility based on Information Theory quantifiers."
Abstract:"Location-aware services provide valuable information for capturing human mobility patterns. In this context, analyzing the mobility dynamics, such as the means of trans-portation and their speeds, leads to better solutions by understanding the underlying data generating process and identifying different patterns. Strategies based on extracting Information Theory measures associated with ordinal patterns methods, for example, Complex-Entropy Causality Plane and Fisher-Shannon Causality Plane, have reached relevant advancements in distinguishing different time series dynamics. Thus, they are promising tools to explain those complex behaviors to improve human mobility -based services. In this work, we aim to characterize the users' means of transportation based on their speed time series derived from the Geolife dataset. Therefore, for each type of transportation, we observe the speed dynamics over time and correlate their associated Information Theory quantifiers with colored noises mapped onto the causal planes. Evaluation results show the potential of our study, allowing us to distinguish motorized and non-motorized means of transportation. Also, based on that mapping, we can estimate the transportation switching. (c) 2022 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 = [0];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [1].
Characterization of human mobility based on Information Theory quantifiers
Location-aware services provide valuable information for capturing human mobility patterns. In this context, analyzing the mobility dynamics, such as the means of trans-portation and their speeds, leads to better solutions by understanding the underlying data generating process and identifying different patterns. Strategies based on extracting Information Theory measures associated with ordinal patterns methods, for example, Complex-Entropy Causality Plane and Fisher-Shannon Causality Plane, have reached relevant advancements in distinguishing different time series dynamics. Thus, they are promising tools to explain those complex behaviors to improve human mobility -based services. In this work, we aim to characterize the users' means of transportation based on their speed time series derived from the Geolife dataset. Therefore, for each type of transportation, we observe the speed dynamics over time and correlate their associated Information Theory quantifiers with colored noises mapped onto the causal planes. Evaluation results show the potential of our study, allowing us to distinguish motorized and non-motorized means of transportation. Also, based on that mapping, we can estimate the transportation switching. (c) 2022 Elsevier B.V. All rights reserved.