dataesr/scientific-paragraphs-categorization huggingface dataset:
The noise in those results could be interpreted to imply a trend in the sensor's response as a function of time. Combining the results from both sites would reinforce the lack of a statistical trend from Site A. Of course, the discussion above depends on there being no systematic differences larger than absolute uncertainties at the multiple sites. Visual inspection of the data in Figure  7  indicates that the results from Site B appear to have a lower ratio than those from Site A. The difference is readily apparent in the data from 2015. A first evaluation of the two datasets examined whether either site indicates a trend in the sensor's radiometric response or possibly a nonlinear response in relation to the surface reflectance levels of site A and B. As mentioned previously, the data from Site A for this spectral band near 650 nm do not indicate a temporal change in the ratio that is statistically different from zero at the 5% confidence level.
The number of datasets for Sensor #2 at Site A is 16, and that for site B is five. None of the means from the two sites for a given band are found to be different statistically at the 5% confidence level. The agreement for several of the bands is close enough that the two symbols are not discernible. The disagreements are again largest at 650 nm, indicating a possible feature in this part of the spectrum between the results of the two sites. It is possible that there is a systematic effect that has been missed in the uncertainty analysis. One advantage of RadCalNet is that the availability of data from other sites and sensors will allow improvements in understanding the RadCalNet uncertainty budgets as well as possible sensor features that could cause site-dependent differences. It is noted above that the band-to-band differences for Sensor #1 for both sites were not statistically different.
The number of datasets for Sensor #2 at Site A is 16, and that for site B is five. None of the means from the two sites for a given band are found to be different statistically at the 5% confidence level. The agreement for several of the bands is close enough that the two symbols are not discernible. The disagreements are again largest at 650 nm, indicating a possible feature in this part of the spectrum between the results of the two sites. It is possible that there is a systematic effect that has been missed in the uncertainty analysis. One advantage of RadCalNet is that the availability of data from other sites and sensors will allow improvements in understanding the RadCalNet uncertainty budgets as well as possible sensor features that could cause site-dependent differences.
Interconsistency The example shown in Figure  12  is a demonstration of how data from RadCalNet can be used for harmonization, or interconsistency, studies. The SI-traceable nature of RadCalNet with its known uncertainties allows the network to be treated as a common reference to understand the consistency between sensors. One advantage to RadCalNet is that no spectral band correction factor  [41]  is needed because the results for each sensor already include the sensor SRF as part of the band weighting. Additionally, the data from the sensors under study need not be from the same dates, and, more importantly, the data from the sensors need not even be from the same sites. A similar philosophy has been recommended for use of results from the reflectance-based approach with personnel present on the site  [12]  and allowed the intercomparison of a dozen satellite and airborne sensors  [42].
The examples above provide a demonstration of the utility of RadCalNet and show how SI-traceability provides the best methodology for ensuring data interconsistency across bands and sensors. The results shown in Figure  12  are very similar, but noticeably different to those in Figures  10  and 11. None of the results shown in Figure  12  are statistically different from a relative or absolute sense, and this is not a surprise since Figure  12  represents a combination of the data in the other two figures. The band-to-band agreement within a sensor agrees to within the absolute uncertainty for each sensor. Likewise, the sensor-to-sensor agreement for similar bands is within the absolute uncertainties. The same conclusions are drawn when using an unweighted average and standard deviation computed from the unweighted average, in essence providing a relative comparison between bands and sensors.
Conclusions This paper has presented information about the new RadCalNet network of instrumented sites. RadCalNet is designed to provide predicted TOA reflectance suitable for the radiometric calibration or monitoring of Earth imagers in the reflected solar portion of the spectrum. The initial network of sites consists of four locations: Baotou (China), Gobabeb (Namibia), La Crau (France), and Railroad Valley Playa (USA). Data from the four sites are available via the RadCalNet portal, along with documentation providing descriptions regarding the instrumentation at each site and SI-traceable uncertainties for the RadCalNet results from the sites. RadCalNet data has been processed by many users, at the time of writing there are more than 300 registered users of RadCalNet from over 35 countries.
The data from RadCalNet are well suited to assess the radiometric stability and accuracy of individual space sensors and to assess the radiometric consistency of multiple sensors, without relying on the assumption of temporal invariance of the observed site nor requiring near-simultaneous observations of the site by the sensors being compared. The SI-traceable nature of the RadCalNet TOA reflectance and its associated uncertainties allows for the consistent radiometric monitoring, comparison, and calibration of space sensors over multiple sites. RadCalNet data are already being used for comparisons, calibrations, and validations e. g. ,  ([43] ). It is expected that over the next few years more sites will be admitted as RadCalNet sites, increasing the range of sites available for comparison.
Funding: The Gobabeb site was set up and instrumented thanks to funding from the European Space Agency Technology and Research Programme, contract 4000110704. The RadCalNet archive and portal are maintained through the European Space Agency Earthnet Programme, contract CCN5 4000110704. NPL received funding for this work from the Metrology for Earth Observation and Climate project (MetEOC-2), Grant Number ENV55 532 within the EMRP programme. It also received funding from the MetEOC-3 project, grant number 16ENV03 under the EMPIR programme. The EMRP and EMPIR programmes are jointly funded by the EMRP participating countries within EURAMET and the European Union's FP7 and H2020 programmes. NPL was also funded by the European Space Agency Technology and Research Programme through the ACTION project and from the UK Government's Department for Business, Energy and Industrial Strategy (BEIS) through the UK's National Measurement System programmes.
AOE's work was supported by the Bureau of International Co-operation Chinese Academy of Sciences (Grant No. 181811KYSB20160040. The University of Arizona received funding from NASA Research Grants NNX14AE20G, NNX15AM86G, NNX16AL25G, and USGS Research Cooperative Agreement G14AC00371. We would like to thank AERONET for processing the sun photometer data, and also the Bureau of Land Management (BLM), Tonopah Nevada Office, for assistance and access to Railroad Valley.
2 ). Pooling specimens from all species, the total prevalence of Wolbachia was 80. 5% in males and 70. 3% in females and this difference was not significant (χ 2 = 2. 45; df = 1; P = 0. 117). Interestingly, a significant difference in incidence of Wolbachia infection was found between the two island groups, with a higher proportion of Vanua Levu than Viti Levu species of Nesobasis being infected with Wolbachia (100% of species from Vanua Levu vs. 47. 37% of species from Viti Levu; χ 2 = 5. 93; df = 1; P = 0. 015). Wolbachia MLSt characterization and strain distribution. Twenty-nine individuals belonging to the 20 infected Nesobasis and Melanesobasis species were selected for MLST genotyping of the Wolbachia. A total of 34 alleles from the genes gatB, coxA, hcpA and fbpA were identified, of which 12 were new to the MLST database (Table  2 ; http://pubmlst.
org/wolbachia). This allelic diversity represented 25 unique Wolbachia strains (Table  2 ), which were identified by the ClonalFrame analyses as belonging to supergroups B, A and F (Fig. 3 ). Wolbachia strains belonging to supergroup B (n = 10) were found in 8 of the Nesobasis species analyzed (Tables  1  and 2 ). Strains B1 to B9 grouped together with high support in the ClonalFrame analysis (Fig. 3a ), with B strains found infecting Isoptera and Lepidoptera as the closest relatives. Strain B10 grouped together with strain 394 from the MLST database, which infects the dragonfly species Crocothemis servilia. With the exception of strains B5 and B6, which were found in 2 Nesobasis species each; the remaining B strains were each found in a single Nesobasis species (Table  2 ). Strains belonging to supergroup A (n = 7) clustered together with high support in the ClonalFrame analysis, forming a monophyletic clade, well differentiated from the other A strains from the MLST database included in the analysis (Fig.
Regarding the co-speciation events, only the one involving the Nesobasis species N. sp. nov. 11 (individuals NE0613, NE0638) and N. sp. nov. 4 (NE0455) and the Wolbachia strains ST_B10, ST_B3 and ST_B5, included associations of high contribution to the overall congruence between topologies detected by ParaFit and PACo (see Fig. 5 , Suppl. Information Figs S1 and S2, and Suppl. Information Table  S5 ). Table  2. Allelic profiles of the Wolbachia infections found in the Melanesobasis and Nesobasis species screened for this study. Listed are: the name given to each strain, the supergroup to which it belongs (according to the results of the ClonalFrame analyses), the allele number for each of the MLST genes, and the species in which that strain has been found. "Unique" alelles refer to those that have not been reported to the MLST database; and "near to" refers to the closest match for these alleles in the MLST database.
Discussion Theory predicts that endosymbionts may become more common in groups of closely related species, as the probability of symbiont transmission between host species declines with increasing genetic distance between hosts. In other words, a high incidence of infection is expected in clades undergoing radiation  13. Consistent with this, and supergroup F (c). Wolbachia strains found infecting the Nesobasis and Melanesobasis species screened in our study appear in blue, whereas strains in black correspond to Wolbachia allelic profiles downloaded from the MLST database (http://pubmlst. org/wolbachia/, see Suppl. Information Table  4 ). Nodes are colored according to their posterior probability values, as depicted in the tree legend. our results indicate that the incidence of Wolbachia in Nesobasis species is among the highest reported in the literature for this endosymbiont.
org/wolbachia/). PCR reactions were carried out using the DreamTaq Green PCR Master Mix (ThermoFisher Scientific, Waltham, Massachusetts). Prior to sequencing, unincorporated primers and dNTPs were digested using Shrimp Alkaline Phosphatase and Exonuclease I (New England Biolabs, Ipswich, Massachusetts, USA). Cleaned PCR products were sequenced in both directions using BigDye v. 3. 1 chemistry (Applied Biosystems, Foster City, California) and capillary electrophoresis on an ABI3130xl Genetic Analyzer (Applied Biosystems). Chromatograms were visually inspected and assembled with Geneious v. 9. 1. 7  35 (https://www. geneious. com). All consensus sequences were trimmed to the appropriate length for database query, using the templates for each locus found in the MLST database (https://pubmlst.
org/wolbachia/info/allele_templates. shtml). A BLAST search of each sequence was performed against the Wolbachia MLST database  36  (http://pubmlst. org/wolbachia). When a sequence had an exact match in the database, it was assigned the designated allele number otherwise the sequence was submitted to the MLST database for allele number assignment. To reconstruct the phylogenetic relationships of the host species, fragments of the Arginine Methyltransferase (PRMT) nuclear and the mitochondrial Cytochrome Oxidase I (COI) genes were amplified using specific primers and annealing temperatures  [37] [38] [39]. PCR reactions, sequencing and chromatogram inspections were carried out as described above (see Suppl. Information Table  S3  for information on primer annealing temperatures). For the PRMT locus, ambiguities with similar peak heights were considered to be heterozygous positions and recoded with IUPAC ambiguity codes.
Some individuals from the species Nesobasis brachycerca (n = 1), N. sp. nov. 9 (n = 1), N. leveri (n = 1) and N. selysi (n = 2); showed superimposed traces typical of heterozygous indels, and these were resolved using the software Indelligent v. 1. 2  40 (http://dmitriev. speciesfile. org/indel. asp). All sequences generated in this study have been submitted to the NCBI GenBank (https://www. ncbi. nlm. nih. gov/genbank/; see Suppl. Information Tables  S1  and S2  for accession numbers).
Genetic data analyses. All sequences were aligned with ClustalW 41 , as implemented in Geneious v. 9. 1. 7. Phylogenetic relationships among the Wolbachia strains found in Nesobasis and Melanesobasis were reconstructed using ClonalFrame v1. 2, a bayesian software that estimates the clonal relationships between the members of a sample, while also estimating the chromosomal position of homologous recombination events that have disrupted the clonal inheritance  42. The complete dataset used in the ClonalFrame analyses included MLST profile sequences from 101 representative Wolbachia strains from A, B, C, F and H supergroups that were downloaded from the MLST database (see Suppl. Information Table  S4 ), together with the MLST strains identifies in the Nesobasis and Melanesobasis species. Three independent ClonalFrame runs of the dataset were performed, with 500,000 MCMC iterations after 100,000 burn-in iterations and all the rest of the parameters set as default.
Convergence of the runs was assessed with the Gelman and Rubin method  43  as implemented in the ParseCF script written by Barry Hall. Phylogenetic relationships between the Nesobasis and Melanesobasis species for both mitochondrial and nuclear DNA were reconstructed under Maximum Likelihood (ML) and Bayesian Inference (BI) frameworks. ML trees were built using the Randomized Axelerated ML algorithms implemented in RaxML-HPC2 v. 8. 2. 8  44  , through the CIPRES web portal (http://www. phylo. org). The analysis was run under the GTR + G model, and bootstrapping was performed under auto majority rule criterion (autoMRE). BI analysis was performed in MrBayes v. 3. 2. 6  45  implemented in Geneious v. 9. 1. 7. Searches were run for five million generations, in two independent runs, using the defaults priors and the best fitting nucleotide substitution model for each locus as selected in jModelTest v2.
1. 10  46, 47. Burn-in samples (first 25% of trees) were discarded, and the remaining were combined to produce a 50% majority rule consensus tree, with bipartition frequencies equal to posterior probabilities values. cophylogenetic analyses. In order to analyze the nature of the interactions between Wolbachia and their damselfly hosts, we followed the global-fit cophylogenetic approaches implemented by ParaFit  48  and PACo  49  ; and the event-based method implemented by Jane  50. For these analyses, the datasets were pruned to contain only those Wolbachia infected hosts included in the MLST analysis for which COI sequences were also available. ParaFit and PACo methods were used to assess the overall congruence between the topologies of the hosts and Wolbachia, and to identify those host-Wolbachia associations contributing to the detected congruence pattern.
Genetic distance matrices (uncorrected p-distances) were obtained using MEGA X  51  for both the Wolbachia concatendated MLST and the host COI datasets. These matrices, together with a matrix representing host-Wolbachia associations, were used as input for both analyses (see Suppl. Information Table  S6 ). ParaFit assesses the congruence between phylogenies through a global test of random association between taxa (with a null hypothesis of no relationship), while PACo explicitly tests the dependence between topologies through a Procrustes superimposition of distance matrixes. For the PACo analysis, we tested the hypothesis that both the Wolbachia and host phylogenies depend on each other; with the null hypothesis that the Wolbachia ordination does not predict the host ordination and vice-versa, such that the host clades are randomly associated to the Wolbachia clades.
Hypothesis testing was performed by setting the parameters methods = "quasiswap" and symmetry = T in the function PACo  49. The relative contribution of individual associations to overall congruence was tested through ParaFitLink1 and ParaFitLink2 tests and assessed through the squared residuals for each association from PACo analysis (non-congruent associations: 95% squared residual confidence interval > median value). All analyses were conducted in R  52  with the packages PACo v0. 3. 260  53 and ape v5. 1  54. All R scripts and the input data used in these analyses can be found in Suppl. Information Table  S6. In order to assess the contribution of a set of evolutionary events to the observed pattern of association between Wolbachia and their hosts, we performed an analysis with the software Jane v4  50  (https://www.
cs. hmc. edu/~hadas/jane/index. html#get). Briefly, Jane attempts to find solutions that minimize the total sum of the event costs through a genetic algorithm. The costs for each evolutionary event were specified as follows: co-speciation = 0 (host and Wolbachia speciate simultaneously); duplication = 1 (Wolbachia speciates and both species remain on the same host); duplication and host switch = 2 (Wolbachia speciates and one of the new species switches onto a different host); loss = 1 (a host speciates and Wolbachia is absent in one of the new host species); and failure to diverge = 1 (a host speciates and Wolbachia remains on both new host species). Wolbachia and host phylogenetic trees were pruned to contain only those individuals for which both MLST and COI sequences were available, using the script filter_tree.
Acknowledgements Funding was provided by the  National Geographic Society Committee for Research and Exploration ;  NSERC   Discovery grants toTNS ; the  International Dragonfly Fund ;  Carleton University  and grants from the  Spanish Ministry with competence in Science, including FEDER  funds to ACR ( CGL2008-02799 ,  CGL2014-53140-P ). MOLC was supported by a  Marie Curie Fellowship  ( PIEF-GA-2013-626504 ) during her stay at the  University of Liverpool. HVG was a postdoctoral fellow with the  Fund for Scientific Research-Flanders  and the  University of Antwerp  at the time of the research. We are grateful to  Nick Donnelly  for his knowledge and expertise in Nesobasis and Melanesobasis taxonomy, and in South Pacific odonates in general. Nick Donnelly and Dan Polhemus helped to identify Nesobasis and Melanesobasis species.
The article concludes by specifying a series of conditions under which changes in knowledge practices can be expected to contribute to wider policy change. Béatrice Cointe, Christophe Cassen and Alain Nadai examine how IAMs became over the past decades the main tool for producing emissions scenarios for the Intergovernmental Panel on Climate Change (IPCC). IAMs represent interactions among environmental, technological and human systems in a single integrated framework. The authors retrace the structuration of the modelling community around both a common normative commitment to produce 'policy-relevant' knowledge, and of a shared 'repertoire' of organisational techniques and knowledge infrastructures, such as the creation of a research consortium, common scenario databases, and model intercomparison projects.
Necessary to "mak[e] measures and measurement meaningful", such work rests on a scientific ambition to know and predict, and on artefacts like datasets, models, and maps. However, it is also embedded in a complex web of social relations, which are shaped by political and economic struggles, and are thus affective, situated, and experiential. Haines argues that the indeterminacies surrounding data and its interpretation, as well as the political use of predictions, frequently lead to frictions, disorientation, and discontent. This material, social, and emotional context shapes what she calls the "political lives of anticipation". Anticipatory governance, the article demonstrates, has as much to do with scientific knowledge, as with situated practices of coping with non-knowledge and uncertainty.
The three final papers consider predictive modelling in risk regulation and security interven-tions. Bilel Benbouzid focuses on a commercially successful software package for predicting crime, PredPol. He analyses the construction and dissemination of the PredPol algorithm against the backdrop of other predictive technologies that motivated and inspired its development: statistical systems for identifying crime 'hotspots' , and algorithms used by geologists to predict earthquakes and seismic aftershocks. By opening the black box of PredPol's mathematical composition -partially, as the algorithm is not public -Benbouzid shows how it embeds specific normative assumptions and policy frames. PredPol aims to 'optimise' police work, which makes it compatible with neo-managerial cost reduction efforts.
Aims. Within the framework of the DustPedia project we investigate the properties of cosmic dust and its interaction with the stellar radiation (originating from different stellar populations) for 814 galaxies in the nearby Universe, all observed by the Herschel Space Observatory. Methods. We take advantage of the widely used galaxy SED fitting code CIGALE, properly adapted to include the state-of-the-art dust model THEMIS. For comparison purposes an estimation of the dust properties is provided by approximating the emission at far-infrared and sub-millimeter wavelengths with a modified blackbody. Using the DustPedia photometry we determine the physical properties of the galaxies, such as, the dust and stellar mass, the star-formation rate, the bolometric luminosity as well as the unattenuated and the absorbed by dust stellar light, for both the old (> 200 Myr) and young (≤ 200 Myr) stellar populations.
These codes ensure conservation of energy by allowing re-emission of the stellar light, absorbed by the dust grains, at longer wavelengths. In this work we use the most recent version of CIGALE 2   (version 2018. 0)  to model the SEDs and to extract the physical properties for the DustPedia galaxies. The code is able to provide both the attenuated and the unattenuated contributions of the different stellar populations (old and young) allowing us to study the fraction of energy that is absorbed by dust for each stellar component. We take advantage of this information to investigate how the stellar populations of galaxies with different morphologies contribute to the dust heating. In addition to CIGALE, we fit single modified black-bodies (MBB) to the FIR-submm observations as an alternative way to estimate the dust temperature and mass.