dataesr/scientific-paragraphs-categorization huggingface dataset:
For summaries that show all transitions to and from a location connected to that particular location, we refer to the circular migration plots in Supplementary Fig. S7. structures that may not be supported by substitutions (Fig. 1 ). However, this is only a single transmission hypothesis compatible with the data, while many other hypotheses will be plausible as should be reflected by the different topologies in the posterior and hence by low node support values. For this reason, we resorted to posterior summaries that focus on the location-transition patterns in the ancestral history of single taxa or on Markov jump estimates, both averaging over all plausible trees. We intentionally examined the performance of our methods on an early SARS-CoV-2 data set that was heavily burdened by spatiotemporal sampling bias.
Methods SARS-CoV-2 genome data sets and associated travel history. To focus on the early stage of COVID-19 spread, we analyzed SARS-CoV-2 genome sequences and metadata available in GISAID on March 10th  8. We curated a data set of 305 genomes by removing error-prone sequences, keeping only genomes with appropriate metadata, and a single genome from patients with multiple genomes available. We assigned each genome a global lineage designation based on the nomenclature scheme outlined in Rambaut et al. 28  using pangolin v1. 1. 14 (https:// github. com/hCoV-2019/pangolin), lineages data release 2020-05-19 (https://github. com/hCoV-2019/lineages). We aligned the remaining genomes using MAFFT v. 7. 453  29  and partially trimmed the 5′ and 3′ ends. All sequences were associated with exact sampling dates in their meta-information, except for one genome from Anhui with known month of sampling.
Upon visualizing root-to-tip divergence as a function of sampling time, using TempEst v. 1. 5. 3  30  based on an ML tree inferred with IQ-TREE v. 2. 0-rc1  31  , we removed one potential outlier. The root-to-tip plots without the outlier are shown in Supplementary Fig. S3. We formally tested for temporal signal using BETS  32. The final 282 genomes were sampled from 28 different countries, with Chinese samples originating from 13 provinces, one municipality (Beijing), and one special administrative area (Hong Kong), which we considered as separate locations in our (discrete) phylogeographic analyses. Phylogenetic signal in the data set was explored through likelihood mapping analysis  33  (Supplementary Fig. S4 ). We searched for travel history data associated with the genomes in the GISAID records, media reports, and publications and retrieved recent travel locations for 64 genomes (22.
5%, Supplementary Table  2 ): 43 traveled/returned from Hubei (Wuhan), 1 from Beijing, 3 from China without further detail (which we associated with an appropriate ambiguity code in our phylogeographic analysis that represents all sampled Chinese locations), 2 from Singapore, 1 from Southeast Asia (which we also associated with an ambiguity code that represents all sampled Southeast Asian locations), 7 from Italy, and 7 from Iran. In this data set, Italy is better represented by recent travel locations than actual samples (n = 4) and Iran is exclusively represented by travelers returning from this country. For 46 out of the 64 genomes, we retrieved the date of travel, which represents the most recent time point at which the ancestral lineage circulated in the travel location. In order to examine (i) to what extent our reconstructions could be updated by the genome data that has become available retrospectively for the same locations and the same time period before March 10 ~4 months after this date, and (ii) how sampling bias can be mitigated by downsampling from the larger collection of available genomes, we assembled an additional data set of 500 genomes.
For this purpose, SARS-CoV-2 genomes were downloaded from GISAID on June 23, 2020 and processed according to the COG-UK pre-analysis pipeline (https://github. com/ COG-UK/grapevine). Briefly, sequences were aligned to the reference sequence Wuhan-Hu-1 (Genbank accession number NC_045512) using Minimap2 v. 2. 17  34. Problematic sites were masked (https://virological. org/t/issues-with-sars-cov-2sequencing-data/473), and sequences with <95% coverage or an overabundance of mutations were removed. Due to the availability of a relatively large amount of genomes from Shanghai and its importance in international air travel, we considered genomes for this location instead of Fujian to maintain the same phylogeographic dimensionality (44 locations) as in the 282 genome data set. We included the same 64 genomes with travel history used in the 282 genome data set, and for the remaining 436 genomes, we performed a subsampling relative to time and geographic location from the sequences sampled before Mach 10, 2020.
In this case, we incorporate Singapore as an ancestral location with a known travel time (Supplementary Table  3 ). Incorporating unsampled diversity in Bayesian phylogeographic inference. To investigate how unsampled diversity may impact phylogeographic reconstructions, we include in our Bayesian inference of the 282 genome data set taxa that are associated with a location, but not with observed sequence data. We identify undersampled locations by considering the ratio of available genomes to the cumulative number of cases for each location (obtained from Our World in Data, https://ourworldindata. org/coronavirus-source-data). To keep all available data, we opt not to downsample genomes, but to add a number of unsampled taxa to specific locations in order to achieve a minimal ratio of taxa (sampled and unsampled) to cumulative number of cases.
In order to marginalize over phylogenetic clustering in our visualization of phylogeographic history, we generate Markov jump estimates of the transition histories that are averaged over the entire posterior in our Bayesian inference  17, 47. We study the ancestral transition history of specific taxa of interest by summarizing their Markov jump estimates as trajectories over time between a number of relevant states. A new BEAST tree sample tool (TaxaMarkovJumpHistoryAnalyzer available in the BEAST codebase at https://github. com/beast-dev/beast-mcmc) and associated R package constructs these estimates. The BEAST tutorial on incorporating travel history also includes information on how to use these tools (http://beast. community/travel_history). We also visualize posterior expected Markov jumps estimates between all locations using Sankey plots and circular migration flow plots.
Acknowledgements We would like to thank all the authors who have kindly shared genome data on GISAID, and we have included a table (Supplementary Tables 4 and5) listing the authors and institutes involved. The research leading to these results has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 725422-ReservoirDOCS ) and from the  European Union 's  Horizon 2020  project  MOOD  (grant agreement no. 874850 ). The Artic Network receives funding from the  Wellcome Trust  through project  206298/Z/17/Z. P. L. acknowledges support by the  Research Foundation-Flanders  (" Fonds voor Wetenschappelijk Onderzoek-Vlaanderen ",  G066215N ,  G0D5117N , and  G0B9317N ). G. B. acknowledges support from the  Interne Fondsen KU Leuven/Internal Funds KU Leuven  under grant agreement  C14/18/094 , and the  Research Foundation-Flanders  (" Fonds voor Wetenschappelijk Onderzoek-Vlaanderen ", G0E1420N).
Data availability BEAST XML input files including the GLM-diffusion model and travel history, with or without unsampled diversity, are available at https://github. com/phylogeography/ travelHistory  50. The SARS-CoV-2 genome data required for running these xmls can be downloaded from www. gisaid. org. Supplementary Tables 2-5 list accession numbers for the genomes used in this study. Case count data were obtained from Our World in Data (https://ourworldindata. org/coronavirus-source-data). Air travel data were obtained from the International Air Transport Association (http://www. iata. org).
Code availability The code for running BEAST analyses with travel history is available in the ancestral_path branch of the BEAST codebase available at https://github. com/beast-dev/ beast-mcmc. The tree sample tool, TaxaMarkovJumpHistoryAnalyzer, is available from the master branch in the same codebase. The R package to visualize Markov jump trajectories is available at https://github. com/beast-dev/MarkovJumpR 51. A tutorial explaining how to perform travel-aware phylogeographic analyses in BEAST and how to summarize them can be found at http://beast. community/travel_history.
The problem of forest fires in Yakutia is not as well studied as in other countries. Two methods of machine learning classifications were implemented to determine the risk of fire: MaxENT and random forest. The initial materials to define fire risk factors were satellite images and their products of various spatial and spectral resolution (Landsat TM, Modis TERRA, GMTED2010, VIIRS), vector data (OSM), and bioclimatic variables (WORLDCLIM). The results of the research showed a strong human influence on the risk in this region, despite the low population density. Anthropogenic factors showed a high correlation with the occurrence of wildfires, more than climatic or topographical factors. Other factors affect the risk of fires at the macroscale and microscale, which should be considered when modeling.
In the second step, based on the literature analysis  [11] [12] [13]  two methods of machine learning classifications were implemented to determine the risk of fire: MaxENT and random forest. The initial materials to define fire risk factors were satellite images and their products of various spatial and spectral resolution (Landsat TM, Modis TERRA, GMTED2010, VIIRS), vector data (OSM), and bioclimatic variables (WORLDCLIM). The results in the form of long-term fire risk maps can be used for fire prevention and planning fire-fighting measures in the territory of the republic. The results can help create an application that can be used to determine the risk of fire and the spread of fire during a disaster. This work has the potential to lay the foundations for the future of a fully automated application of fire risk assessment in the Sakha Republic.
Fires Data In the Republic of Sakha (Yakutia), open data on forest fires are not available. There is a database of forest fires at the Ministry of Environmental Protection (https://minpriroda. sakha. gov. ru), but these data are tabular, and without georeferencing, it is impossible to create a GIS database necessary for this type of research. These data are understated due to the addition to the database of only those fires in which there was action taken to extinguish the fire. Fires that are far from human activity often do not extinguish, so are not classified in the database. The most popular global data source on fire data is the Fire Information for Resource Management System (FIRMS). Data available in the service are collected from the VIIRS 375 m, 750 m, and MODIS Collection 6 Active Fire Product.
The data are collected from 2002 until now. For the studies, we chose data from the MODIS Collection 6 sensor because of their longer availability and enough spatial resolution. We used data between 2001 and 2018 from the FIRMS fire archive. The shapefiles were in the Geographic WGS84 projection. The confidence values ranged from 0% to 100% and ranged in one of three fire classes (low-confidence fire, nominal-confidence fire, or high-confidence fire)  [17].
For these reasons, mostly global datasets were used. Variables were divided into four groups: (1) meteorological (precipitations, temperature, maximum summer temperature, radiation); (2) NDVI; (3) landform (elevation, slope, slope direction); and (4) human activity (distance from roads, distance from settlements, distance from rivers). All data types were in other formats and had different spatial resolutions (Table  1 ). To collect bioclimatic variables, the WorldClim dataset of global climate layers was used. The WorldClim dataset has a spatial resolution of about 1 km 2  [22]. WorldClim is a set of global climate layers (gridded climate data), specifically developed for ecological modeling on GIS. Currently, WorldClim provides several datasets for different temporal scenarios (past, current, and future conditions).
In this work, data for the current condition scenario  were used. WordClim bioclimatic variables are analysis-ready data so the preprocessing was not necessary. NDVI is greatly used in the evaluation of the phenology and productivity of the vegetation. Onigemo et al. claims that the values of NDVI obtained through images at the peak of drought were related to the content moisture and fresh phytomass, showing its potential to estimate fire risk  [23]. Illaera et al. found a good correlation between the NDVI values and the location of wildfires  [24]. To assess NDVI in macro-and microscale, it was decided to use NDVI from two sources: MODIS and LANDSAT images. The Landsat data were used for the determination of NDVI in the Nyurbinsky region. As the region of interest, the following paths and rows were defined: 129-15; 129-16; 130-14; 130-15; 130-16; 131-15.
There were selected available images from the year with the smallest cloud cover. Images without cloud cover at the peak of the growing season (July) were available only for Landsat 5 in 2009. Before creating the mosaic, atmospheric correction was necessary. It allowed for improving images from level 1 to level 2. For NDVI calculations, bands 3 and 4 were used by the formula described by J. W. Rouse in 1973  [25]. To determine the average NDVI in the vegetation season for Yakutia between 2001 and 2018 with MODIS, dataset MOD13A2 Version 6 was used. MOD13A2 provides NDVI values with a resolution of 1 km. The product is derived with a monthly interval. There were selected images at the peak of the growing season (July) from each year. At the territory of Yakutia, there are the following tiles, which were defined as region of interests: h21v01, h22v01, h23v01, h21v02, h22v02, h23v02, h24v02, h23v03, h24v03, h25v03.
Factor To model landform factor data, it was decided to use one of the most used in these types of works, digital elevation models: the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). It incorporates the current best available global elevation data. GMTED2010 is commonly used for radiometric and geometric correction, cover mapping, and extraction of drainage features for hydrologic modeling  [26]. There was a necessity for mosaicking. For the GMTED, the following entities were used: GMTED2010N50E120, GMTED2010N50E150, GMTED2010N70E120, and GMTED2010N70E150. From the DEM, the information about elevation, slope, and slope direction was derived. To investigate the relationship between the fire and the presence of a human, it was necessary to obtain information on the distance of fires from roads, buildings, and rivers.
The data in shape format were downloaded from Open Street Map (OSM). According to previous works on this subject  [27, 28] , to achieve this goal, the Euclidean distance was used. Euclidean distance is a straight line between two points in Euclidean space. The locations were converted to raster. The resolution of the raster was defined by the shortest of the width or height of the extent of the input feature, in the input spatial reference, divided by 250. After the preprocessing, two regions of interest (ROI) were defined for each obtained raster. Each of them was clipped using the forest cover of the Nyurbinsky region and Republic of Sakha (Yakutia) forest cover. The forest cover was made available by the Institute of Biological Problems of Cryolithozone in Yakutia. The same ROI was made for fire points between 2001-2015 from the FIRMS dataset.
This method aims to reduce the variance within classes and maximize the variance between classes. This is done by striving to minimize the average deviations of each class from the middle class while maximizing the deviations of each class from the means of other groups  [29]. New values in rasters have been extracted to each fire point from the FIRMS dataset of over 17 years in the territory of Yakutia and the Nyurbinsky region. The next step was to calculate the area of each class in each factor. Then, all fires between 2001-2018 were summed, separately for each category in each obtained earlier categorized raster. With these data, it was possible to determine the regression, and correlation between the dependent variables and explanatory variables. The same calculation scheme was repeated for each variable.
Long-Term Fire Risk Modeling The next step was to build risk models using two machine learning methods (Figure  3 ). The training dataset was built from the FIRMS fire data between 2001-2015. All registered fire points at the territory in the Republic of Sakha Yakutia and Nyurbinsky region in this period were used. The variables were divided, according to the previously obtained correlation coefficient. As the predictors, two types of datasets were examined. A dataset with a coefficient of correlation equal or higher than satisfactory (higher than 0. 6) and a dataset with all 11 previously selected fire factors. All predictor rasters have been resampled and converted to grid format. Such a set of training data and predictor data were created, separately for the territory of Yakutia and the territory of the Nyurbinsky region.
Classification and regression random forest is competitive with the best available methods and superior to most methods in common use  [35]. The application of random forest can be an effective methodology to predict fire occurrence in different sites. The random forest algorithm is a technique developed by Breiman (2001)  [35]. It combines a large set of decision trees, which is the biggest advantage compared to a simple decision tree algorithm. Each tree is trained by a set of variables, which are randomly selected from the training dataset. All classifications were carried out using SAGA-6. 4. 0 and its modules "Maximum Entropy Presence Prediction", "Random Forest Presence Prediction (ViGrA) Classification". After the processing, two types of models were obtained: presence prediction and presence probability maps.
• Extreme-extreme possibility of fire. To obtain only six possibility classes, it was necessary to reclassify the models. For reclassification, the natural breaks method was used. For the validation of the results, raw fire points data between 2015 and 2018 in the Republic of Sakha (Yakutia) and the Nyurbinsky region were used. To carry out statistical analysis pixel count in each probability class in the presence probability maps and each class at the presence prediction maps were summed. Fire points in each class were summed and divided by the pixel sum in each class. This process was carried out to consider the surface of each class when verifying models. Next, the percentage share of fires in each class was calculated. The last step was regression and correlation analysis.
Fire Risk The next step was to create fire hazard models using two types of modeling methods. Fire risk models in the territory of Yakutia are shown in Figures  4  and 5. Results of the modeling differed, but both methods gave satisfying results. For the territory of the Republic of Sakha (Yakutia), high coefficients of correlation for each prediction method can be observed (Tables  3  and 4 ). Coefficients were slightly higher for the random forest prediction method. In this method, the model with 11 variables did not give considerably better results than the model with nine variables. When modeling using the maximum entropy prediction model, better results gave a dataset with nine variables. In both models, the highest number of fires between 2015-2018 is in the high, very high, and extreme probability classes.
In the very low-risk class, there were less than 1% of fire points. Presence prediction models showed a superior random forest method over the maximum entropy method. In the random forest method, almost 100% of the fire points were in the presence class. The maximum entropy method was characterized by worse results. In the absence, the class located more than 10% of the fire points from the validation dataset. Modeling carried out at the territory of the Nyurbinsky region showed slightly different results than in Yakutia (Figures  6  and 7 ). Correlation coefficients were not as high and did not exceed 0. 9 (Table  5 ). According to the coefficient of correlation, the smallest error was characterized by a model of maximum entropy using six predictor variables. The coefficient of correlation for 11 predictors was only slightly lower and was equal to 0.
Discussion In fire studies, fire risk is one of the major topics and there are many different approaches to this subject. Blanchi et al. ,  in their work about a methodological approach in fire risk studies, collected more than 50 works connected with fire probability cartography. Risk mapping has less than twenty years. Previously, there were rather preferred descriptive approaches. Since the 1990s, there has been an increasing interest in this field  [36]. The study presented the possibilities of using different types of GIS and remote sensing data in modeling the wildfire risk. Results allow us to reflect various aspects of fire studies. The difficulties in fire risk assessment were to point out and clarify possibilities to define wildfire risk. There are many different approaches in hazard mapping based on different datasets, scales, and algorithms.
The multi-approach is relevant in fire management studies  [37, 38]. Gai et al. [39]  developed a spatially weighted index model for fire risk assessment. You et al. [40]  integrated a Forest Resource Inventory Database based on four aspects of topographical, human activity, climate, and forest characteristic factors. Goldarag et al. [41]  used neural networks and logistic regression for fire risk assessment. The biggest challenge in the study was to collect the necessary data due to poor exploration of the area of studies. GIS technologies in Yakutia are under development  [42]. For this purpose, we chose a semiprobabilistic mode of modeling, which gave possibilities of combining historical fire data and physical fire mechanisms  [43].
On the other hand, a larger number of fires may have an impact on the climate, permafrost ecosystems, and the functioning of indigenous people. In connection with this situation, it is necessary to work on new fire risk assessment methods in the Arctic and subarctic zone of Yakutia. Methods should use global databases of fire, climate, and human activities. For building new models that include all these factors, it is necessary to use different types of datasets, like OSM data, climatic datasets, and remote sensing data from different sensors (MODIS, Landsat, Sentinel). According to Gigović et al. , the random forest model could be used at the regional level for forest fire susceptibility mapping  [51]. Banerjee et al. claim that the MaxENT method can be used as a decision support tool for stakeholders of forest resources  [52].
Symmetry 2020,  12, 2018 3 of 18  This research proposed an efficient approach for seed identification and classification based on a deep learning CNN model and the application of symmetry. The model is trained by implementing transfer learning, which focuses on transferring knowledge across domains and is considered a promising machine learning methodology. It is an important machine learning (ML) tool in solving problems related to insufficient training data, which can encourage the idea of not training the model from scratch and significantly reducing the training time  [15]. In this research, a dataset comprised of symmetric images of various seeds was used to carry out the seed classification process. Many symmetric images from each seed class were captured and then augmented to train the proposed model.