dataesr/scientific-paragraphs-categorization huggingface dataset:
Software implementation is straightforward and can be based on the flexible data formats proposed in  (Davydenko et al. , 2021). Almost any software environment (including Microsoft Excel) can be used to implement the simple methods proposed, but we recommend R because it allows flexible implementation of visual tools. The workflows proposed can be directly used in a wide range of settings ranging from inventory control applications to climate forecasting. In particular, these workflows are useful for inventory control settings and the case of intermittent demand data where non-negative actuals and forecasts are assumed. Although our focus has been on forecasting and time series data the methods are also applicable for regression analysis, especially for panel data and multi-target regression.
Acknowledgements We would like to thank our colleagues from the  Service Mer at the Station Biologique of Roscoff  for their support in algal sampling and shipping, and  Sophie Le Panse  from the  MERIMAGE platform  ( FR 2424 ,  CNRS-Sorbonne Université ) for technical assistance during sample chemical fixation. The authors also want to thank the  PICT-IBiSA imaging facility  in the  Institut Curie  for the use of the NanoSIMS ion microprobe.
Tokyo) might partly be attributed to a wider size range of U. Tokyo BC measurements and a difference in corrections for undetected BC between the measurements. Because M BC measurements were consistently and traceably made by the individual groups, the systematic differences in absolute M BC values are expected to be smaller than 20 % at least for the PAMARCMiP and ARCTAS datasets (U. Tokyo) and the HIPPO dataset (NOAA). Aerosols were also sampled on filters aboard the aircraft during PAMARCMiP and analyzed using transmission electron microscopy (TEM;  Adachi et al. , 2021). In this study, we used potassium-containing particles as an indicator of influences from biomass burning. Gaseous carbon monoxide (CO) concentration was measured using an Aerolaser AL5002 CO monitor  (Gerbig et al. , 1999; Scharff et al.
, 2016). The flight tracks are shown in Fig. 1 , and the flights are listed in Table  1. The data obtained during 5 min after taking off and 5 min before landing were not used to avoid possible influences of local pollutants emitted from the base Station Nord. Vertical profiles of BC and other atmospheric species were measured up to 5 km during most of the research flights. Appendix B presents 6 d kinematic back trajectories of air parcels measured aboard the aircraft.
The M BC values measured by COSMOS and those measured by The University of Tokyo SP2 agreed to within about 10 % at Ny-Ålesund  (Ohata et al. , 2019). Because COSMOS data were not obtained at these two sites in 2008 and 2010, data obtained by particle absorption soot photometers (PSAP; Radiance Research, Seattle, WA, USA;  Ogren et al. , 2017)  were used after scaling their M BC values to agree with COSMOS values by using a scaling factor derived from side-by-side measurements  (Sinha et al. , 2017; Ohata et al. , 2021a). Daily averaged data were used in this study. Note that measurements at Barrow Atmospheric Baseline Observatory near Utqiaġvik are referred to as measurements at Barrow hereafter. 3 Numerical model simulations The CAM5-ATRAS model calculates major chemical and aerosol processes in the atmosphere  (Matsui, 2017; Matsui and Mahowald, 2017; Matsui et al.
, 2018). The model considers seven aerosol species (sulfate, nitrate, ammonium, dust, sea salt, organic aerosol, and BC) with a twodimensional sectional representation consisting of 12 bins of particle sizes from 1 to 10 000 nm dry diameter and 8 bins of BC mixing states for fine particles (40 to 1250 nm). Simulations were performed with a horizontal resolution of 1. 9 • × 2. 5 • latitude and longitude and 30 vertical layers. Meteorological nudging used data for temperature and horizontal wind in the free troposphere (<800 hPa) from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2) dataset  (Gelaro et al. , 2017). We used monthly AN emissions from the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset  (Hoesly et al. , 2018)  and daily BB emissions from the Global Fire Emissions Database version 4.
01 hPa (the model top) in a hybrid sigma-pressure coordinate system. The model simulation was performed from January 2008 to December 2018 after a 1-year spin-up run with the prescribed sea surface temperature and sea ice data  (Ishii et al. , 2005). In the simulations, horizontal wind fields were nudged toward 6-hourly Japanese 55-year Reanalysis data  (Kobayashi et al. , 2015). Simulations used monthly AN emissions from the MACCity emissions dataset  (Lamarque et al. , 2010)  and daily BB emissions from the Global Fire Assimilation System dataset (GFAS;  Kaiser et al. , 2012). We also performed a model simulation that omitted the BB emissions of BC to estimate the separate contributions of AN and BB sources to total BC concentrations. 4 Year-to-year variation in BC and influences from BB.
5 • N, the latitude of the Arctic Circle. This latitude generally corresponded to locations of a boundary of the polar dome, in which Arctic air mass tends to be confined, as estimated for the NETCARE 2014 and 2015 experiments  (Bozem et al. , 2019). The PAMARCMiP M BC values were generally similar to those obtained during the HIPPO experiment, whereas systematically higher values were observed during the ARCTAS and NETCARE experiments. Although each aircraft measurement was made over a limited area and time duration, they document significant year-to-year variations in M BC values in the Arctic spring. In the following sections, BC mass concentrations obtained during these four aircraft experiments are examined. Using the four aircraft datasets, BC column amounts at altitudes between 0 and 5 km (COL BC , in units of µg m -2 ) were calculated by vertically integrating the median M BC values at each altitude obtained at latitudes north of 66.
However, although the BB BC values were higher in the ARCTAS and NETCARE cases, the total (AN + BB) COL BC (A) values were still significantly lower than the observations. Reasonable agreement was found only when the calculated BB BC contributions were multiplied by a factor of 3 or more (C in Fig. 5 ). As described in Sect. 3, CAM5-ATRAS and MRI-ESM2 utilized GFED and GFAS BB BC emissions, respectively. Consequently, these results suggest that BB BC emissions could be largely underestimated, or the removal of BC from BB emissions was overestimated, although the level of these uncertainties cannot be evaluated in this study. In Appendix D, model-calculated vertical M BC profiles are compared with observations for entire altitude ranges in which observed data are available, namely, up to 10.
Moreover, plumes with enhanced M BC Medians and 25 %-75 % ranges were calculated for 31 d periods with the median date chosen to be the median date of the experiment given in Table  3. Fire counts were averaged for latitudes north of 50 • N. Values in parentheses are relative to PAMARCMiP. values up to 250 ng m -3 were observed at altitudes around 5 km, as shown by the red data points in Fig. 2a. In this section, we propose that these enhanced M BC values were likely due to influences from BB emissions. We then examine possible differences in microphysical properties between these BC particles and other BC particles that were least affected by BB. The plumes with enhanced M BC values were observed at similar locations and altitudes on 2, 3, and 4 April. In total, twenty-nine 1 min datasets with M BC >50 ng m -3 were obtained.
Summary The PAMARCMiP aircraft-based experiment measured vertical profiles of M BC at altitudes up to 5 km in spring 2018. Median M BC values in individual altitude ranges were 7-18 ng m -3 at altitudes below 4. 5 km. These concentrations were systematically lower than those observed during ARC-TAS in 2008 and NETCARE in 2015, whereas they were similar to those observed during HIPPO in 2010. Vertically integrated BC mass concentrations for altitudes below 5 km in the Arctic (>66. 5 • N, COL BC ) observed during the ARC-TAS and NETCARE experiments were higher than the PA-MARCMiP value by factors of 4. 2 and 2. 7, respectively. When data obtained only at >80 • N were compared, the same tendency was observed: the ARCTAS and NETCARE COL BC values were higher by factors of 3. 6 and 2. 3, respectively.
, 2012). In contrast, the measured D m range of the NOAA SP2 during the NOAA-ARCPAC experiment was 90-600 nm. The M BC was estimated by multiplying the integrated mass of BC particles by 1. 1, considering the undetected mass fractions of BC outside the detection range  (Spackman et al. , 2010; Schwarz et al. , 2010). These differences in measurements and data reductions could have resulted in some systematic differences between the two M BC datasets. Because M BC measurements were consistently and traceably made by the individual groups, the systematic differ- ences in absolute M BC values obtained by them are expected to be smaller than 20 %.
SO, MK, AY, NM, KA, OE, HB, MZ, and ABH contributed to the PAMARCMiP experiments and data analysis of SP2, TEM, and CO and CO 2 gas analyzers. NO and HM conducted numerical model simulations using MRI-ESM2 and CAM5-ATRAS, respectively. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. We are indebted to all the PAMARCMiP 2018 participants for their cooperation and support. The authors also acknowledge the  Alfred Wegener Institute (AWI)  for the support to conduct the PAMARCMiP 2018 campaign, the use of the PO-LAR 5 research aircraft, and the skill and safety exemplified by the pilots and flight staff. We thank the  Atmospheric Chemistry department of the Max Planck Institute for Chemistry  for providing the CO instrument and  Dieter Scharffe  for his support during the preparation phase of the campaign. We thank  Yutaka Kondo ,  Bryan Thomas ,  Peter Detwiler ,  Ross Peterson , and the  Norwegian Polar Institute  for supporting the ground-based COS-MOS measurements at  Barrow and Ny-Ålesund. We also thank  J. Ryan Spackman  and  Joshua P. Schwarz  for making comparison measurements of BC during the ARCTAS and ARCPAC experiments.
Financial support. This research has been supported by the  Environmental Restoration and Conservation Agency  (grant nos. JPMEERF20205001 ,  JPMEERF20202003 ,  JPMEERF20172003 , and  JPMEERF20165005 ), the  Ministry of the Environment, Government of Japan  (grant no. MLIT1753 ), the  Ministry of Education, Culture, Sports, Science and Technology  (grant nos. JP17H04709 ,  JP18H03363 ,  JP18H05292 ,  JP20H00638 ,  JP21H03582 ,  JPMXD1300000000 , and  JPMXD1420318865 ), and the  Deutsche Forschungsgemeinschaft  (grant no. project number  268020496 TRR 172 ). Review statement. This paper was edited by  Radovan Krejci  and reviewed by two anonymous referees.
USA COVID-19 Data Revisited As an application of the network ideas discussed above, we consider COVID-19 data from the USA, https://www. worldometers. info/coronavirus/country/us/ (accessed on 26 October 2020), within the period (21 January-26 October 2020). The total cases are plotted in Figure  14  as a function of days. We implemented a fitting procedure using the results from Section 4. 3, as described in detail in the Appendix A. The result of the fit is represented by the continuous line in the figure, being almost indistinguishable from the empirical data. These results illustrate the potential usefulness of the cluster method in complex territorial environments spanning both large space and temporal scales. We then calculated the daily cases, as successive variations of the total cases, which are shown by the green circles (real data) and continuous blue line (fit).
Acknowledgements. This study is part of the  ANR   14-CE03-0006 VIP Mont Blanc  and the  EU ALCOTRA AdaPT Mont Blanc  project. We thank  Michel Legrand  and  Owen King  for their help and constructive comments during the writing of the manuscript. We thank the Editor as well as  W. Haeberli  and two anonymous reviewers for their very valuable comments which helped to improve the manuscript significantly.
Knight discussed how radiation and microbiome relate to human spaceflight  [20] [21] [22]. Another focus was the variance in weight loss and glycemic response concerning diet choice, especially regarding plant consumption  [23, 24]. He noted three projects currently underway: American Gut Project, Microsetta Initiative, and Global FoodOmics, all of which study the relationship between what enters and exits the body and what happens between these processes. Dr. Knight also discussed the use of mass spectrometry in the study of metabolite distribution in mice. Using germ-free (GF) and specific-pathogen-free (SPF) mice, it was shown that the metabolomics data of GF and SPF mice varied drastically throughout the body, and microbiome differences were shown to be responsible for new amino acid conjugations in bile acid  [25].
He outlined some specific sequencing platforms, such as Illumina, PacBio, Oxford Nanopore, and their unique strengths. Lastly, Dr. Petrosino reviewed The Environmental Determinants of Diabetes in the Young (TEDDY) study. The study investigates the relationship between genetics and the environment (including the microbiome) in developing type 1 diabetes mellitus (T1DM). He reviewed different published data from TEDDY, including how a temporal developmental pattern in the microbiome of study participants was identified  [49]  and suggested that similar studies could be undertaken to identify factors that could mature or progress the microbiome during spaceflight. He also posited whether viral communities inhabiting the gut microbiome could serve as a biomarker for immune system function. One study identified that enterovirus B infections might be involved in the development of islet autoimmunity  [50] , a key mechanism in the pathogenesis of T1DM.
Acknowledgments SLM and DD would like to thank  Rachael Dempsey  ( Baylor College of Medicine ) for creative and effective outreach efforts and materials. SKM and SLM would like to thank  Lauren Breeyear  for the administrative support,  Leslie Maxfield  and the  Caltech Academic Media Technologies and Communications team  for hosting the webinar, and members of the  Mazmanian laboratory (Reem Abdel-Haq ,  Joseph Boktor ,  Jessica Griffiths , and  Henry Schreiber ) for moderating questions.
CORDEX FPS LUCAS models This study uses nine RCMs composed of different combinations of five regional atmospheric models and six LSMs. The combinations are shown in Table  1  which also specifies the model versions, key references for each model and their representation of snow-vegetation interactions. The ensemble consists of two regional models (WRF and CCLM) that use multiple LSMs allowing the analysis to isolate the impact of the LSMs on the results. Uniquely, the ensemble consists of two WRF-NoahMP simulations that differ only by their representation of convection and planetary boundary layer processes. Hereafter, each of these combinations will be considered as different RCMs as they differ in the way they represent different atmospheric and land surface processes. Data for all snow variables was available for WRFc-NoahMP, CCLM-CLM5.
0, WRFa-NoahMP, RCA, WRFb-CLM4. 0. However, REMO-iMOVE, RegCMa-CLM4. 5, and CCLM-TERRA did not have data for snow depth while CCLM-VEG3D could only provide a binary number of 0 and 1 for snow cover fraction. Snow depth for REMO-iMOVE, RegCMa-CLM4. 5, and CCLM-TERRA was calculated from snow water equivalent using a constant value of 312 kg/m 3.
Acknowledgements CICERO researchers acknowledge funding from the  Research Council of Norway  (grant  254966 ). In Norway, the simulations were stored on the server NIRD with resources provided by  UNINETT Sigma2 -the National Infrastructure for High Performance Computing  and Data Storage in Norway. WRFc-NoahMP simulations were performed and stored on resources provided by UNINETT Sigma2 -the  National Infrastructure for High Performance Computing and Data Storage  in Norway ( NN9280K ,  NS9001K ,  NS9599K ). WRFb-CLM4. 0 simulations were supported by computational time granted from the  National Infrastructures for Research and Technology  S. A. (GRNET S. A. ) in the National HPC facility -ARIS -under project  ID pr005025  and  pr007033_thin. Edouard L. Davin  and  Ronny Meier  acknowledge financial support from the  Swiss National Science Foundation (SNSF)  through the  CLIMPULSE  project and thank the  Swiss National Supercomputing Centre (CSCS)  for providing computing resources.
P. Hoffmann  is funded by the  Climate Service Center Germany (GERICS)  of the  Helmholtz-Zentrum Hereon  in the framework of the  Helmholtz-Institut Climate Service Science (HICSS)  project  LANDMATE. The authors gratefully acknowledge the  WCRP CORDEX Flagship Pilot Study LUCAS "Land use and Climate Across Scales '' and the research data exchange infrastructure and services provided by the  Jülich Supercomputing Centre, Germany , as part of the  Helmholtz Data Federation  initiative. R. M. Cardoso ,  D. C. A. Lima P. M. M. Soares  were supported by national funds through  FCT (Fundação para a Ciência e a Tecnologia, Portugal)  under project  LEADING  ( PTDC/CTA-MET/28914/2017 ), and project  UIDB/50019/2020.
Introduction Open-ended learning refers to a process where an agent or robot must learn how to solve an unbounded sequence of tasks that are not known in advance. It is a key challenge to efficiently apply robotics to real world tasks  [1] : real world environments are diverse and changing, robot users can define new tasks or modify existing tasks, and the robot capabilities themselves may change due to damage, upgrades or wear and tear. As modifying the robot programming to manually adapt it to new goals and situations is costly and unpractical, we strive to provide the robot with a developmental system allowing it to autonomously acquire and adapt its perceptual, cognitive and motor abilities in an iterative way. It has been proposed that this system can be implemented by combining some exploration processes able to build datasets of motor, perceptual and other relevant data about the world and the tasks at hand, and a representational redescription process able to use this data to build and update markovian models of the tasks, which can then be exploited  [2].
End-to-end learning techniques, such as deep reinforcement learning algorithms, combine both processes to directly build policies  [3] , and can theoretically be used to learn new tasks and adapt to environmental variations in such open-ended scenarios. However, they require very large learning datasets, which is not suited to real robotics tasks where evaluating the outcome of actions or policies is slow and costly  [4] , except in the rare cases when many real robots can be used in parallel  [3]. They are also notably slow to converge when reward states are sparse, which is often the case in tasks necessitating to reach some very specific goal states using only partial, high-dimensional and noisy obervations of the world. An approach to deal with this issue is to keep exploration and redescription separated and to explore in a simulated environment.
Evaluating actions in simulation is cheaper and faster than with real robots. Sample-inefficient exploration algorithms, such as quality-diversity (QD) algorithms  [5, 6] , can thus be used. Rather than a single policy, these techniques learn a repertoire of actions covering a given behavioral space, i. e. a large dataset of diverse actions that reach various goals in that space. Such repertoires of diverse actions can be exploited to solve different tasks, and to adapt to new unplanned situations arising in open-ended scenarios, such as obstacles in the environment, or the robot breaking down. They can also be used as a training dataset for other machine learning algorithms, e. g. deep learning, to learn state representations  [7]  or parameterized policies  [8]. Most of the previously cited works, however, remain in the simulation domain.
Some of those algorithms, such as MAP-Elites  [29] , rely on discretizing the behavior space into a set of bins (a behavioral map), and then filling the bins with increasingly higher quality actions. This makes the algorithm simpler, allows for efficient data visualization, and easily allows to exploit the discrete behavioral map through techniques such as bayesian optimiation  [30]. For high-dimensional behavior spaces, the number of bins for the MAP-Elites grows exponentially with the number of additional dimensions. In order to handle high-dimensional behavior space, Vassiliades et al. [31]  propose an extension to replace the equal grid of the MAP-Elites algorithm by an adaptive one using centroidal voronoi tessellations. It partitions the behavior space into k homogeneous geometric regions, and prevents the number of bins from growing exponentially for high-dimensional datasets.
Some existing methods focus on improving the simulation to make the policies more transferable, for example by adding noise to the simulation to generate more robust behaviors  [9] , by coevolving the policy and the simulator  [34, 35] , or by updating the simulator's forward model thanks to real observations  [36]. Those methods, however, are not sufficient yet to tackle highdimensional problems in open environments  [37]. Other approaches rely on combining learning in the simulated domain with more limited experimentations in the real domain. For methods learning inverse models or parameterized policies, the reality gap is then a domain transfer problem: the learnt simulation controller must be transferred to the real world. Using state of the art deep transfer learning methods, such as learning domain-invariant features  [38]  is challenging due to an unbalanced dataset between simulated and real data, and the lack of an exploratory process to efficiently sample real world data.
Reflecting the dominance of bovine production within Ireland's AFOLU sector, primary input data to initialise the model are national herd sizes (derived from milking cow and suckler-cow numbers) and average animallevel productivity (e. g. milk yield per cow) to determine feed energy intake, fertiliser application rates and grass utilisation rates to determine stocking densities and production outputs, followed by proportions of any spared grassland (relative to the baseline year) going to alternative land uses. In v1. 0, alternative land uses are limited to fallow or commercial or conservation forestry and rewetting of drained organic soils (bioenergy cropping and anaerobic digestion can be readily integrated for coupling with downstream energy models). Subsequent iterations and model coupling will account for upstream effects of e.
Modelling architectural overview GOBLIN incorporates seven modules, displayed in a dataflow diagram  (Pressman, 2010)  in Fig. 2 , a number of which are derived from previous models on national grassland intensification  (Mc Eniry et al. , 2013) , farm LCA  (Jones et al. , 2014; Styles et al. , 2018)  and forest GHG fluxes  (Duffy et al. , 2020a). The flow of data is represented by arrows between interlinked modules (brown rectangles), processes (purple circles) and data stores (green, open ended rectangles) (Fig. 2 ). The scenario, herd, grassland, livestock, land use, forestry, and integration modules included in GOBLIN reflect initiation and synthesis functions, along with data on the main activities and emissions arising within the AFOLU sector. The modules are run in sequential order, with subsequent modules relying on the output generated by previous modules.