dataesr/scientific-paragraphs-categorization huggingface dataset:
Results of Real-Life Experiments Table  5  presents results for a single node in different scenarios. In scenarios 1-3 the node operated in continuous mode sending 40 ADV messages in each data interval of length τ = 10 s. The scanners were able to receive from 1 to 32 messages in different data intervals depending on the scenario. This means that in each scenario and for every scanner (S1, S2 and S3) the effective DRR equals 1-i. e. , all sequence number sent from this node were correctly received. The S3 scanner outperformed S2 and S1 which results in higher PLR values and higher numbers of received ADV packets (N RxPacket ). Different values for each scanner are due to the fact that S3 was running on a PC computer while S1 and S2 were RasperryPi-based. The presence of scanners operating in active mode (scenario 2 and 3) increases PLR values compared to only passive scanning (scenario 1) which is a consequence of additional SCAN_REQ / SCAN_RESP messages that are exchanged between the nodes and active scanners.
Results differ significantly in the scenario 4 where all the nodes terminated ADV transmission upon reception of SCAN_REQ from the scanner S1. Intermittent operation significantly lowers the number of ADV packets transmitted by each node in data interval-as presented in Table  5 , the median of N TxPacket dropped from 40 to 1. On the one hand this drop reduces the likelihood of correct ADV reception by passive scanners and causes the DRR for S2 and S3 to drop. On the other hand, the DRR for S1 was still 1, and the efficiency of transmission for all the nodes improved (i. e. , the values of PLR dropped). This is due to lower collisions in the communication channel and lower load of the scanners. Comparing performance of S1 in all the scenarios (Figure  3 ) it can be seen that active scanning with intermittent transmission of advertisements allows to reduce the number of messages in the radio channel.
Consequently, this transmission mode improves the reception of packets (cf. PLR in Table  5 ), and maintain the same DRR for the active scanner. Figure  4  presents the plot of the number of ADV packets transmitted by a single node and received by S1 scanner for all data intervals in the scenario 4. For over 55% of the data intervals the node has sent only one ADV message and turned off the advertisements-this means both the ADV and SCAN_REQ messages were sent successfully in the first attempt for 55% of the data intervals. On the other hand, for over 80% of data intervals the scanner S1 has received a single ADV message. Higher ratio for the scanner is related to the fact that the scanner has lost some of the ADV before the one that was correctly received. It also shows that once the ADV was received the SCAN_REQ was transmitted back and its transmission was effective-otherwise the node would have kept sending another ADV messages and cause the scanner to receive more then one ADV message.
Considering the DRR τ for the network of 150 nodes and 250 ms advertisement interval it can be seen that the results are slightly higher compared to real-life experiments (Figure  5 , Table  7 ) which is due to the simplified model of the nodes and scanner used in simulations. Despite simplifications, the results present that intermittent operation allows to significantly lower the data interval while maintaining high value of DRR. This means that the nodes can effectively transmit data more frequently and increase the effective data throughput when they operate in intermittent mode. For nodes operating in intermittent mode the DRR drops for dense networks, frequent transmission of advertisements, and short data intervals. This is because of the large PLR for those networks (Figure  9 ). In such scenarios the nodes restart ADV transmission frequently (due to small values of data interval) and experience lots of collisions (due to large number of nodes and small values of advertisement interval).
Prionospio Malmgren 1867 and Aurospio Maciolek 1981 (Annelida: Spionidae) are polychaete genera commonly found in the deep sea. Both genera belong to the Prionospio complex, whose members are known to have limited distinguishing characters. Morphological identification of specimens from the deep sea is challenging, as fragmentation and other damages are common during sampling. These issues impede investigations into the distribution patterns of these genera in the deep sea. In this study, we employ two molecular markers (16S rRNA and 18S) to study the diversity and the distribution patterns of Prionospio and Aurospio from the tropical North Atlantic, the Puerto Rico Trench and the central Pacific. Based on different molecular analyses (Automated Barcode Gap Discovery, GMYC, pairwise genetic distances, phylogenetics, haplotype networks), we were able to identify and differentiate 21 lineages (three lineages composed solely of GenBank entries) that represent putative species.
2014). Furthermore, the ISA administrates the regional environmental management plan across the CCZ, so-called Areas of Particular Environmental Interest (APEI). Sequences of 122 specimens morphologically assigned to Prionospio and Aurospio from eight exploration contract areas and one APEI were included (Fig. 1 : the German exploration contract area 'BGR', Russia and Poland among other countries 'IOM', Belgium 'GSR', French 'Ifremer' & 'Ifremer-2', Singapore 'OMS', Britain UK-1 and the APEI-6). Of these, 53 specimens were collected on the two United Kingdom Seabed Resources Ltd. (UKSR) cruises AB01 and AB02 to the UK-1 exploration contract area stratum A and stratum B, the OMS contract area, and the APEI-6. Details on sampling methods are given in  Glover et al. (2016). Maps and metadata from UK-1 stratum A have been published in earlier taxonomical work on macrofaunal material from this cruise  (Dahlgren et al.
2012 ; http://www. geneious. com) and deposited in GenBank (for accession numbers, see Supplement Table  1 ). The obtained sequences of the different gene fragments were aligned separately using MAFFT  (Katoh and Standley 2013; V 7. 402 ) implemented with CIPRES Science Gateway V. 3. 3  (Miller et al. 2010; www. phylo. org).
Initial identification of species, phylogenetic analyses, and haplotype networks To obtain a first estimation of the number of species present in our data set, the Automated Barcode Gap Discovery  (Puillandre et al. 2012 ; ABGD) was conducted with 16S rRNA. The ABGD identifies potential barcoding gaps separating hypothetical species, which is based on the assumption that interspecific genetic distances are larger than intraspecific distances. The ABGD analysis was run on the web-based version of the software (http:// wwwabi. snv. jussieu. fr/public/abgd/abgdweb. html). Pairwise uncorrected p-distances were used for the analyses (Table  2 ), which were calculated with MEGA7  (Kumar et al. 2016) , including all available sequences for each gene, respectively. Standard settings for ABGD were kept, except for Pmin (0.
To assess the phylogenetic relationships among the studied specimens and to assess whether the lineages suggested by ABGD are monophyletic, phylogenetic analyses were performed with Bayesian inference and maximum likelihood. Both gene fragments were analyzed separately, as well as concatenated with MrBayes  (Ronquist and Huelsenbeck 2003; version 3. 2 ). The maximum likelihood was performed using Randomized Axelerated Maximum Likelihood  (Stamatakis 2014; RAxML v. 8. 2. 10) on XSEDE with rapid bootstrapping (1000 iterations) via the CIPRES Science Gateway V. 3. 3  (Miller et al. 2010; www. phylo. org). Whether a given phylogenetic hypothesis can be rejected by the data is often not obvious from the tree topology, especially in cases where some branches are not significantly supported. We used the approximately unbiased test  (Shimodaira 2002)  as implemented in the program CONSEL  (Shimodaira and Hasegawa 2001)  to test the statistical significance of likelihood differences between the best ML topology and the ML topology, in which the genus Aurospio was constrained to be monophyletic.
The constrained ML tree and the site-wise likelihoods associated with each topology (necessary for conducting the approximately unbiased test) were calculated with GARLI 2. 0  (Zwickl 2006). Due to missing sequences of the 18S genes for a large part of the analyzed specimens and the very few mutations, only the phylogenetic analysis of the 16S rRNA gene is included. For the 16S rRNA analysis, different spionids that are not assigned to the Prionospio complex were chosen as outgroups (Supplement 1). Additionally, available 16S rRNA sequences of Prionospio and Aurospio from GenBank were included in the analyses (Supplement 1). Four chains were run for 5*10 7 generations, with sampling every 1200th generation, and discarding the first 25% as burn-in. Thus, the convergence chain runs were validated using TRACER v.
Species delimitation and diversity The ABGD analysis of the 16S rRNA dataset retrieved 21 lineages when a barcode gap threshold of 1 4. 9% was employed. The 21 lineages were designated to Prionospio sp. A to sp. P, Aurospio sp. Q to sp. T, as well as Aurospio cf. dibranchiata and Aurospio foodbancsia  Mincks et al. 2009. The lineage Aurospio cf. dibranchiata is named according to corresponding GenBank records assigned in the analyses (Supplement 1), but with reservation, as no genetic data is available for the holotype or from the type locality. Pairwise genetic distances (uncorrected p-distances) between the 21 lineages varied for 16S rRNA from 5. 1-32. 0% (based on the 21 lineages derived from 16S) (Table  2 ). The lowest pairwise distances were found between lineages Prionospio sp. C and sp.
In a recent study,  Guggolz et al. (2019)  found barcoding gaps of ~2-2. 8% in 16S rRNA between Laonice species, another spionid genus, from the tropical Atlantic. The fact that we found higher intraspecific distances, in particular within Prionospio sp. H and Aurospio cf. dibranchiata, might be explained by the larger geographic sampling in our study. It appears likely that the overall intraspecific diversity would be higher if additional regions and populations were studied. Based on the higher pairwise distances for 16S rRNA between lineages C and D (5. 1-6. 2%) compared to the distances within lineage Prionospio sp. H (4. 8%), we follow the species delimitation of ABGD instead of GMYC. Based on the available data, we postulate that each one of the 21 lineages, identified with ABGD, represents a separate species within the investigated dataset with reservations for lineages Prionospio sp.
C, sp. D and sp. H. One has to keep in mind that a higher number of species might be present within the dataset (e. g. , Prionospio sp. E and sp. A might be further split) using a less conservative threshold for delimitating species, but this additional splitting and the differences found between ABGD and GMYC analyses would not change the main finding, concerning the dispersal potential of Prionospio and Aurospio (see below). Unfortunately, our previous morphology-based species identification of the Prionospio and Aurospio specimens from the tropical Atlantic  (Guggolz et al. 2018)  showed high inconsistencies with the results presented here regarding the number of species. The assignment to genera showed consistency with the molecular results, but the number of species had been underestimated with morphological identification  (Guggolz et al.
Especially, the taxonomic boundaries of Prionospio and Aurospio are problematic, as the main distinguishing feature is the beginning and shape of the branchiae, which has been suggested to be insufficient to discriminate the genera  (Paterson et al. 2016; Sigvaldadottir 1998; Wilson 1990 ). The 16S rRNA phylogeny of the present work highlights this difficulty as monophyly for both genera, Prionospio and Aurospio, was not supported. Of the 21 species identified within the study, Aurospio foodbancsia, Aurospio, sp. Q and sp. R were studied solely on the basis of sequence data obtained from GenBank. For Aurospio sp. Q and sp. R, only single sequences each were available from GenBank without further taxonomic information and these could have been misidentified, a well-known problem  (Collins and Cruickshank 2012; Kvist et al.
Irrespective of the taxonomic status of Prionospio and Aurospio, the dataset presented here provides profound insights into the diversity of these genera in the deep sea. In the samples for the tropical Atlantic studied herein, spionids were a dominant faunal component with more than 73% of all sampled polychaetes  (Guggolz et al. 2018). The potential 19 deep-sea species (excluding the two GenBank entries Aurospio sp. Q and R) make up around 17% of the worldwide described Prionospio and Aurospio species (around 108 species listed in WoRMS http://www. marinespecies. org/, Read and Fauchald 2018) , keeping in mind that we cannot say for sure whether they represent species new to science or already described species. Our findings support previous reports that both genera are abundant in the deep sea, and presumably the number of species is still underestimated  (Paterson et al.
Acknowledgments The authors are grateful to the crew of the  RV Sonne RV Melville  and  RV Thomas G. Thompson  for valuable support during the Vema-TRANSIT expedition and the two ABYSSLINE cruises and to many students and colleagues for sample sorting and help on deck. Craig R. Smith  ( University of Hawaii ) and  Adrian Glover  ( Natural History Museum, London ) are acknowledged for coordinating the  ABYSSLINE  project and sampling onboard. Thank you to L. Menot (Ifremer) who participated in the JPI Oceans and MIDAS projects that allowed to sample additional polychaetes from the  CCZ. T. Guggolz  received a PhD fellowship from the  Bauer Foundation (Germany). Special thanks to the GRG, for supporting the first author with valuable comments and new ideas. Marco Neiber  is thanked for support and help with statistical tests.
Two anonymous reviewers are thanked for valuable comments improving the manuscript. Funding information Open Access funding provided by Projekt DEAL. The project was undertaken with financial support of the  PTJ (German Ministry for Science and Education) , grant  03G0227A  to  A. Brandt  and  C. Devey  and from  UK Seabed Resources  and the  Swedish Research Council FORMAS  to  H. Wiklund  and  T. G. Dahlgren. P. Bonifácio  received financial support from the  JPI Oceans pilot action 'Ecological Aspects of Deep-Sea Mining ' and from the  European Union   Seventh Framework Program  ( FP7/2007-2013 ) under the  MIDAS  project, grant agreement no. 603418.
Abstract. Ice particle activation and evolution have important atmospheric implications for cloud formation, initiation of precipitation and radiative interactions. The initial formation of atmospheric ice by heterogeneous ice nucleation requires the presence of a nucleating seed, an ice-nucleating particle (INP), to facilitate its first emergence. Unfortunately, only a few long-term measurements of INPs exist, and as a result, knowledge about geographic and seasonal variations of INP concentrations is sparse. Here we present data from nearly 2 years of INP measurements from four stations in different regions of the world: the Amazon (Brazil), the Caribbean (Martinique), central Europe (Germany) and the Arctic (Svalbard). The sites feature diverse geographical climates and ecosystems that are associated with dissimilar transport patterns, aerosol characteristics and levels of anthropogenic impact (ranging from near pristine to mostly rural).
Caribbean INPs were positively correlated to parameters related to transported mineral dust, which is known to increase during the Northern Hemisphere summer. A wind sector analysis revealed the absence of an anthropogenic impact on average INP concentrations at the site in central Europe. Likewise, no Arctic haze influence was observed on INPs at the Arctic site, where low concentrations were generally measured. We consider the collected data to be a unique resource for the community that illustrates some of the challenges and knowledge gaps of the field in general, while specifically highlighting the need for more long-term observations of INPs worldwide.
, 2014). For this study, INPs were sampled at the Amazon Tall Tower Observatory (Brazil; 2. 144 • S, 59. 000 • W; Fig. S1  in the Supplement), the Volcanological and Seismological Observatory of Martinique (Caribbean Sea; 14. 735 • N, 61. 147 • W; Fig. S2 ), the Taunus Observatory (Germany; 50. 221 • N, 8. 446 • E; Fig. S3 ) and the Zeppelin Observatory (Norwegian Arctic; 78. 908 • N, 11. 881 • E; Fig. S4 ). Between May 2015 and January 2017 a total of 1212 aerosol samples (7704 data points) were collected and analyzed for INPs in the deposition and condensation freezing modes (Fig. 1 ). Aerosol samples were collected using electrostatic precipitation on silicon substrates in a semiautomated routine. Samples were shipped to our laboratory in Frankfurt and were subsequently analyzed for ice nucleation activity using the FRIDGE isothermal vacuum diffusion chamber  (Schrod et al.
The measurements at Taunus Observatory began earlier and continued longer, but here we focus on the concurrent sampling effort conducted within the framework of the EU FP7 BACCHUS (Impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding; European Union Seventh Framework Programme) project (Fig. 1 ). Due to the failure of the complementary aerosol instrumentation at the Caribbean site, sampling was interrupted between December 2015 and May 2016. Furthermore, the exchange of sampling substrates with the Amazonian site was logistically challenging. Thus, the Amazon data set is represented by several shorter periods of continuous measurements. Between October 2015 and February 2016 some unexplained contamination in the process of wafer cleaning prevented cleaning substrates to below the desired background level of INP at the lowest temperature.
As a consequence no data below -25 • C are available for this period. Typically, aerosol samples were collected with PEAC7 daily or once every 2 days at local noon. Sampling time was prescribed to 50 min with a 2 L min -1 flow rate. The sampled aerosol particles resulting from this 100 L of air were found to usually generate well-resolved ice crystal numbers in the investigated temperature regime using the FRIDGE analysis system. However, the level of representativeness of the deployed sampling strategy is difficult to assess (see discussion). Yet, the pursued sampling protocol ensured a consistent database. Samples were then stored in PetriSlide containers after collection until they were shipped unfrozen in packages of 25-50 to our laboratory in Frankfurt (transport time was usually less than a week).
Ice crystals activate rapidly on the surface of INPs and grow to macroscopic sizes within some tens of seconds. The ice crystals are counted automatically by a CCD (charge-coupled device) camera viewing the measurement from above. After a measurement is completed, the valve to the water vapor source is closed, and the valve to the vacuum pump is opened. Subsequently, ice crystals evaporate, and a new combination of temperature and ice supersaturation can be selected (Table  2 ). Typical measurement uncertainties are summarized in the caption of Table  4. A complete description of the method can be found in  Schrod et al. (2016). One of the main strengths of FRIDGE is the direct visual observation of the ice crystal formation on the surface of the sample substrate. No complicated data analysis is required to establish the number of INPs.
Site-specific INP characteristics At each measurement station a diverse array of supplementary meteorological, aerosol and gas data from the stations was collected in parallel to the INP sampling. Unfortunately, the parameters, instrumentation and time coverage vary considerably between the four sites. Observations include typical meteorological parameters such as temperature, relative humidity, precipitation, etc. , as well as the total aerosol particle number and mass concentrations, aerosol size distributions, black carbon concentrations, aerosol optical thickness, gaseous pollutant markers, and greenhouse gases. However, despite a rigorous effort including correlation analysis, factor analysis and trajectory sector analysis, we were ultimately unable to identify a single parameter or a set of parameters that account for the total observed variation of INPs.
TO During the timeframe of the global sampling effort (about 640 d) 400 PEAC7 samples were collected and analyzed from TO (i. e. , one sample every 1. 6 d). The sampling frequency of valid INP concentrations (i. e. , above the detection limit) remains as good as one sample every 2 d for measurements at -20 and -25 • C. This is by far the best data coverage of the four stations. We found a moderate but significant correlation between the PM 10 concentrations and INPs throughout the spectrum of T and RH conditions. The Pearson correlation coefficient is as high as R = 0. 27 (N = 304, p 0. 01) at -25 • C, where we have the best data coverage. Although the particulate matter was significantly enhanced, when wind was coming from the heavily populated and industrialized Rhine-Main metropolitan region (Table  3 ), the average INP concentration was not found to differ significantly from other times, when air masses were arriving from other directions (Fig.
Conclusions The data from our small but unique measurement network can be considered particularly valuable, and we hope lessons can be learned from this effort that will help to guide future INP monitoring efforts. Significant infrastructural and logistical investments are represented by the INP measurements that cover an observational period of 21 months in total. Well above 1000 samples were collected, retrieved and analyzed in this project at a large array of temperature and supersaturation conditions, characterizing the INP concentrations in the deposition and condensation freezing modes. The investigated sites represent diverse climatic regions and ecosystems that experience varying degrees of anthropogenic influence. In spite of the great differences in basically all characteristics that are expected to define the aerosol concentration, composition and source apportionment, we observed fairly similar INP concentrations for all four stations for the methods and sampling strategy applied.
We thank AWI and AWIPEV staff, including Verena Mohaupt, for the logistics provided in sample transport to and from Ny-Ålesund. We thank Hans-Christen Hansson for his valuable scientific input and the supporting aerosol data from Ny-Ålesund. We thank HLNUG and MadininAir for providing air quality data for TO and MQ, respectively. For the operation of the ATTO site, we acknowledge the support by the German Federal Ministry of Education and Research (BMBF contract nos. This open-access publication was funded by the Goethe University Frankfurt. Review statement. This paper was edited by Hinrich Grothe and reviewed by Paul DeMott and one anonymous referee.
Data availability. The INP data of this study have been uploaded to the PANGAEA data repository: https://doi. org/10. 1594/ PANGAEA. 925732 (last access: 18 December 2020, Schrod et al. ,  2020b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-15983-2020-supplement.