<|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:"Clustering the Velib' dynamic Origin/Destination flows using a family of Poisson mixture models."
Abstract:"Studies on human mobility, including Bike Sharing System Analysis, have expanded over the past few years. They aim to give insight into the underlying urban phenomena linked to city dynamics and generally rely on data-mining tools to extract meaningful patterns from the huge volume of data recorded by such complex systems. This paper presents one such tool through the introduction of a family of generative models based on Poisson mixtures to automatically analyse and find temporal-based clusters in Origin/Destination flow-data. Such an approach may provide latent factors that reveal how regions of different usage interact over time. More generally, the proposed methodology can be used to cluster edges of temporal valued-graphs with respect to their temporal profiles and is thus particularly suited to mine patterns in dynamic Origin/Destination matrices commonly encountered in the field of transport. An in-depth analysis of the results of the proposed models was carried out on two months of trips data recorded on the Velib' Bike-Sharing System of Paris to validate the approach. (C) 2014 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 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [0];
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Q6 = [1].
Clustering the Velib' dynamic Origin/Destination flows using a family of Poisson mixture models
Studies on human mobility, including Bike Sharing System Analysis, have expanded over the past few years. They aim to give insight into the underlying urban phenomena linked to city dynamics and generally rely on data-mining tools to extract meaningful patterns from the huge volume of data recorded by such complex systems. This paper presents one such tool through the introduction of a family of generative models based on Poisson mixtures to automatically analyse and find temporal-based clusters in Origin/Destination flow-data. Such an approach may provide latent factors that reveal how regions of different usage interact over time. More generally, the proposed methodology can be used to cluster edges of temporal valued-graphs with respect to their temporal profiles and is thus particularly suited to mine patterns in dynamic Origin/Destination matrices commonly encountered in the field of transport. An in-depth analysis of the results of the proposed models was carried out on two months of trips data recorded on the Velib' Bike-Sharing System of Paris to validate the approach. (C) 2014 Elsevier B.V. All rights reserved.