dataesr/scientific-paragraphs-categorization huggingface dataset:
Contributing to this problem is the inhomogeneous nature of the photometric data both within and across the various deep survey fields. While deep spectroscopic samples are available for fields such as COSMOS, the variation in filter coverage between survey fields makes it impractical to fully apply this training sample to other fields. Given these constraints, we believe that template based pho-tometric redshifts still represent the best starting point when estimating photo-zs for the datasets and science goals of interest. Future work will explore the application of such empirical photo-z estimates to the widest tiers of the LOFAR survey. For this study we base the photometric redshift estimates on the eazy photometric redshift software presented in  Brammer et al. (2008). As mentioned above, several different template fitting photometric redshift codes have been published and have been widely used in the literature, e.
g. BPZ  (Benítez 2000) , LePhare  (Arnouts et al. 1999; Ilbert et al. 2005)  or Hy-perZ  (Bolzonella et al. 2000). The key differences in approach (and potential outcomes) between these codes are primarily the choice in default template sets as well as their treatment of redshift priors based on magnitude or spectral type. Both of these assumptions can be changed either within eazy itself or in subsequent analysis of its outputs. We are therefore confident that our choice of specific photometric redshift code does not strongly bias the results of our analysis and we note that alternative template fitting codes could be used without systematically affecting the results.
Photometric zeropoint offsets The addition of small magnitude offsets to the observed photometry of some datasets has been shown to improve photometric redshift estimates  (Dahlen et al. 2013). While typically small ( 10%), these additional offsets can often substantially reduce the overall scatter or outlier fractions for photo-z estimates. To calculate the appropriate photometric offsets we use the commonly followed strategy of fitting the observed SEDs of a subset of galaxies while fixing their redshift to the known spectroscopic redshift. For a training sample of 80% of the available spectroscopic sample, the offset for each band is then calculated from the median offset between the observed and fitted flux values for sources with S/N > 3 in that band. To ensure that spurious offsets are not being applied based on a small number of catastrophic failures in the photometry we perform a bootstrap analysis to calculate the scatter in estimated zeropoint offsets.
Overall photometric redshift accuracy Before analysing the photometric redshift properties of the radio source population specifically, it is useful to first verify the overall redshift accuracy of the estimates in the respective fields. For galaxy evolution studies (where the overall bias is less critical), the two most important metrics are typically the robust scatter, σNMAD, and outlier fraction, O f. Figures  5  and 6  illustrate how these metrics vary with redshift and magnitude for the full Boötes and COSMOS spectroscopic samples. In Table  3  we also present the corresponding photo-z quality metrics for the full spectroscopic sample and all subsets of radio detected sources. As expected given the availability of medium band observations, the COSMOS photo-zs (Fig. 5 ) typically have lower scatter than the Boötes dataset at any given redshift.
(2016b, as  shown in Fig. 5 ) the combined literature photo-zs performance improves, with scatter and outlier fractions at z < 2 comparable to the best estimates from this analysis, but a poorer performance above this range. For both the Boötes and COSMOS samples we find that the EAZY and Atlas template sets perform comparably and typically produce the lowest scatter (σNMAD) in both the full spectroscopic sample and the full radio selected population. However, in the sub-samples of X-ray detected sources or IR AGN, we find no consistency between the two different datasets. In the wide area dataset, the XMM-COSMOS template set performs significantly better in almost all metrics than the other two sets, for AGN populations (see Table  3. Conversely, in the deep field the XMM-COSMOS set performs worst for the key σNMAD and O f metrics in the subset of X-ray/Opt/IR AGN sources.
Given the consistent methodology used for both datasets, the underlying reason for this discrepancy is likely due to the differences in the source populations included in the relevant spectroscopic samples (see Section 2. 3). As seen in Fig. 4 , the Boötes X-ray/Opt/IR AGN source population is typically significantly optically brighter than that probed in COSMOS and may therefore have intrinsically different SEDs. One clear conclusion that can be drawn from Figures 5/6 and Table  3  is that there is no single template set which performs consistently best across all subsets and datasets. Differences in the redshifts estimated by the three different template sets are found to systematically depend strongly both on optical magnitude (a proxy for overall S/N) and redshift. Specifically, as sources become optically fainter the range between the highest and lowest predicted redshifts systematically increases.
In Fig. 7  we show the relative scatter and outlier fractions of these two matched samples. We find that up to z ∼ 1 (where both spectroscopic samples are fairly representative), photometric redshifts estimated for radio sources have typically lower scatter and outlier fraction than galaxies with no radio detection that have similar magnitudes. This trend is true for both datasets and for all three template sets. Above z ∼ 1, photo-zs for radio sources are significantly worse than their matched non-radio detected counterparts. This trend of increasing scatter/outlier fraction with redshift is not unexpected given as redshift increases the radio detected sources are increasingly luminous AGN for which photo-z estimates are expected to struggle. In the Boötes field specifically (filled symbols), we see that at z ∼ 1 there is a significant jump in the measured scatter for radio sources.
In contrast to the naive expectation of increasing scatter with radio luminosity, it is evident that there is no clear evolution in σNMAD across the ∼ 4 orders of magnitude in radio luminosity probed by our samples. The measured scatter follows a similar trend when examined as a function of radio flux. Between < 1 mJy and 100 mJy, σNMAD remains effectively constant for all three template libraries in both and both datasets. It is only at the very brightest radio fluxes (0. 1 < Sν,150MHz < 1 Jy) where scatter increases for this redshift regime. Some evidence of increasing outlier fraction as a function of log 10 (L150MHz) exists for both the deep and wide fields, with O f rising by ∼ 2 between log 10 (L150MHz) ≈ 23 and log 10 (L150MHz) 26. As a function of radio flux, the trend of increasing outlier fraction is even more pronounced.
(2) For both datasets, we use the equivalent I/i + band optical magnitude for calculating the magnitude dependence. We also assume a characteristic magnitude of i + = 20 for the COSMOS sample  (Laigle et al. 2016 ) and I = 18 for the shallower Boötes sample. The parameters c and k are then fit using the emcee Markov Chain Monte Carlo fitting tool (MCMC Foreman-Mackey et al. 2013). In Fig. 10  and 11 we show the resulting F (c) distributions for the all sources as well as the radio detected sources (and subsets thereof) after the redshift PDFs have been calibrated using the full spectroscopic redshift sample. For the Boötes field, the consensus PDFs from all three template sets are significantly improved for the full spectroscopic sample. The 0 to 50% credible interval ranges are all very accurately measured, with only a small remaining overconfidence within the 80% credible interval.
The success of the ensemble redshifts is an excellent illustration that the techniques developed to provide marginal gains for the 'normal' galaxy population  (Dahlen et al. 2013; Carrasco Kind & Brunner 2014b)  can also provide very significant improvements for more diverse datasets. While the consensus estimates do improve on the photoz predictions for radio sources which also satisfy optical and IR AGN criteria (and to a lesser degree have strong X-ray flux), the overall quality of these estimates still remains very poor compared to those of the general radio source population. Salvato et al. (2008 Salvato et al. ( , 2011) )  illustrated that although the use AGN-dominated SEDs in the photometric redshift fitting can improve results for AGN photo-zs, additional steps are required to maximise the accuracy: namely strict magnitude priors based on optical morphology and corrections for variability in optical magnitudes between observations at different epochs.
Radio surveys for cosmology One of the key scientific capabilities provided by the next generation of radio interferometers like the Square Kilometre Array is as a tool for studying cosmology  (Camera et al. 2012; Ferramacho et al. 2014; Jarvis et al. 2015). One such avenue for studying cosmology with radio surveys is through weak lensing (WL) experiments  (Brown et al. 2015). Thanks to the different systematics offered by radio continuum observations (both in the intrinsic populations explored and the instrumentation), weak lensing studies with the SKA will be highly complementary to the increasingly powerful optical weak lensing studies planned for the next decades (e. g. EUCLID;  Laureijs et al. 2011). As with their optical counterparts, weak lensing studies with the SKA will be heavily reliant on photometric redshift estimates based on the all sky photometric data available.
ACKNOWLEDGEMENTS The research leading to these results has received funding from the  European Union Seventh Framework Programme   FP7/2007-2013/  under grant agreement number  607254. This publication reflects only the author's view and the  European Union  is not responsible for any use that may be made of the information contained therein. KJD and HR acknowledges support from the  ERC   Advanced Investigator programme NewClusters 321271. PNB is grateful to  STFC  for support via grant  ST/M001229/1. KM was supported by the  Polish National Science Center  ( UMO-2012/07/D/ST9/02785  and  UMO-2013/09/D/ST9/04030 ). The authors thank  Mark Brodwin ,  Duncan Farrah ,  Mattia Vaccari  and  Lingyu Wang  for valuable additional feedback and discussion. Finally, the authors thank the anonymous referee for their feedback and contributions to improving the manuscript.
Acknowledgements. The authors thank  Steph Cornford , the three anonymous reviewers, and the editor  Hilmar Gudmundsson  for suggestions that helped to clarify and improve the paper. Support for  Tong Zhang ,  Stephen Price ,  Lili Ju , and  Wei Leng  was provided through the  Scientific Discovery through Advanced Computing (SciDAC)  program funded by the  US Department of Energy (DOE), Office of Science, Advanced Scientific Computing Research and Biological and Environmental Research. Tong Zhang  was also supported by the  National Basic Research Program  (973) of China under grant No. 2013CBA01804  and  CHINARE2016. Lili Ju  was partially supported by the  US National Science Foundation  under grant No. DMS-1215659. Wei Leng  was partially supported by the  National 863 Project of China  under grant No.
2012AA01A309  and the  National Center for Mathematics and Interdisciplinary Sciences of the Chinese Academy of Sciences. Elmer/Ice development and simulations presented here were partly funded by the  Agence Nationale pour la Recherche (ANR)  through the  SUMER, Blanc SIMI   6-2012. FELIX-S simulations presented here used computing resources of the  National Energy Research Scientific Computing Center  (NERSC; supported by the  Office of Science of the US Department of Energy  under Contract  DE-AC02-05CH11231 ). Elmer/Ice simulations discussed in this paper used computing resources of  CINES (Centre Informatique National de l'Enseignement Supérieur, France ) under allocations 2015-016066 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). This study was inspired by discussions with  Frank Pattyn  and  Gaël Durand  at the first MISOMIP workshop, supported by the  Center for Global Sea-Level Change at New York University Abu Dhabi.
Deliverables will be provided through i) research workshops synthesising and addressing key challenges in IRES science, supporting research exchange and educating young researchers, and ii) researcher-stakeholder workshops translating improved knowledge into tangible tools and guidelines for protecting IRES and raising awareness of their importance and value in societal and decision-maker spheres. This Action is organized within six Working Groups to address: (i) the occurrence, distribution and hydrological trends of IRES; (ii) the effects of flow alterations on IRES functions and services; (iii) the interaction of aquatic and terrestrial biogeochemical processes at catchment scale; (iv) the biomonitoring of the ecological status of IRES; (v) synergies in IRES research at the European scale, data assemblage and sharing; (vi) IRES management and advocacy training.
Therefore, the compilation and synthesis of the existing knowledge and data on IRES from this consortium, as proposed in this Action, will strongly contribute to fill this historical gap on the preservation and amelioration of freshwater bodies, as currently considered in the EU Water Framework and Habitats Directives, and ultimately increase the social and political value for these ecosystems, which are a key water resource in many European regions In addition, characteristics of IRES may expand into currently still perennial European streams due to the foreseen patterns of global change (higher human water demand and climate change). These changes are expected to increase the number and length of IRES and to dry up today's perennial systems, resulting in dramatic alterations of flow regimes.
Description of the state-of-the-art The first challenge (C1) is to locate, map and predict IRES in river networks, spatialize flow intermittence patterns, explore historical evolution of these patterns, and distinguish natural and anthropogenic IRES. Hydrological datasets are produced for and from perennial rivers, and they consequently ignore or misrepresent IRES  (Datry et al. 2014 , Datry et al. 2011 , Snelder et al. 2013). This hydrological gap is a primary limit to studying, understanding and managing IRES efficiently  (Datry et al. 2011 , Prat et al. 2014 ). To overcome this, two approaches have been proposed: regionalisation techniques to model the location of IRES in networks based on the limited flow data available (e. g. Snelder et al. 2013 ) and frequent observations of flow states at key sites along a river (e.
g. Turner and Richter 2011). However, regionalisation techniques have a very low predictive power due to the limited amount of data and the limited spatial and temporal extent of visual observations. This is even more valid in headwater areas, most of them being IRES (Benstead and Leigh 2012). Another limitation of both approaches is that they cannot distinguish between natural and anthropogenic IRES. Yet, this information is crucial to further understand and manage hydrological and ecological alterations of IRES. The second challenge (C2) is to understand how alterations in flow regimes impair IRES biodiversity, functions and services, and consequently, how to define environmental flows in IRES. Flow alterations have been studied mostly in perennial rivers and their effects on IRES functioning and services are poorly known  (Larned et al.
C1 will be addressed using various approaches, some of them being novel and unique in this field. The first step will involve collating all continuous hydrological data and associated climatic (e. g. temperature, rainfall) and catchment-scale (e. g. geology, topography, vegetation) data available across the participating countries and producing a metadatabase. Regionalisation procedures will be used to predict drying risk at catchment scales and trend analyses will identify areas at risk of increasing drying (i. e. sentinel catchments). Maps of IRES in the participating countries will be produced, and the very first hydrological classification of IRES at the European scale will be generated to identify key hydrological variables and drivers. In addition to these major advancements, novel approaches coupling aerial photography, citizen science initiatives and social sciences will be tested and developed.
For example, for areas where data are scarce or fragmented, aerial unmanned vehicles (AUVs) and satellite images will be used to locate drying sections (Benstead and Leigh 2012). In targeted key areas, local community, angler and river technical staff knowledge will be explored and analysed to extract additional information about drying history; notably, this will help to identify natural and anthropogenic IRES. The development of a European-scale network of citizen scientists to monitor, locate and map river flow states will also be promoted (e. g. Turner and Richter 2011) based on smartphone technology, as used, for example, in the point source data application developed in the COST Action "Loss of the Night" and in the ongoing TRivers project (www. lifetrivers. eu). All gathered information will be stored in the meta-database.
C2 will be tackled by examining available information on the biological and functional responses to flow variability in IRES and synthesizing this information in a conceptual model from which flow management strategies can be developped. A meta-database compiling available information from participating members will synthesize this information. IRES ecosystem services will be established and economically quantified, along with their links to flow regimes. These actions aim to contribute to environmentally sound flow management in IRES. C3 will be addressed by a meta-ecosystem modelling approach, which will couple terrestrial and aquatic models of carbon and nutrient cycling at the catchment scale for different IRES. The sharing of scientific information about carbon and nutrient dynamics during dry and flowing phases among members, together with a deep literature review, will be a key step towards meeting this challenge.
This data synthesis will be complemented by modelling techniques to explore the influence of spatial and temporal patterns of drying on carbon and nutrient dynamics in IRES and to compare them to patterns from perennial streams. This will improve our understanding of the extent to which processes in dry phases influence water quality during flowing phases and will help to refine current estimates of the contribution of river systems to carbon processing at the global scale. To address C4, the different bioindicators developed for perennial rivers will be adapted for IRES. Meta-analyses examining the occurrence of different species across gradients of flow intermittence will allow assignment of a score of "sensitivity to flow intermittence" to each taxon. These scores will be validated using the expertise, data, and knowledge shared among the participants.
In relation to the Challenge The Action will achieve its objectives by: creating an interdisciplinary network of knowledgeable river researchers in order to promote data and experience sharing and a new generation of experiments and projects on IRES; • A COST Action is certainly the most promising approach to improve this situation across European and neighbouring and partner countries. This Action will promote collaborations between researchers and stakeholders, translate synthesized knowledge into simple, tangible tools, guidelines and protocols for improving IRES management and conservation, and raise awareness for the ecological relevance of IRES and their significant extent in the landscape.
The scientific and applied innovative perspective of this Action will contribute to: • Scientific integration and visibility of European experimental research: The Action will establish short-and long-term intertwined research efforts and close collaboration with national, European and international research networks, thereby favouring the visibility of European research communities in the hot topic of IRES; • Data sharing: The Action will produce 2 meta-databases. This will undoubtedly promote short-and long-term synergies among researchers from European and non-European countries. In addition, the Action will promote the creation of a citizen-science network to monitor river flow states and overcome the current lack of hydrological data. This effort will continue beyond the end of the Action.
Dissemination and/or Exploitation Plan The Action outputs will be disseminated through: • Outstanding scientific papers, including a key synthesis paper on IRES management; • One electronic handbook with guidelines for IRES management; • Publicly available meta-databases on IRES research activities, experiments and projects, as well as a meta-database of flow stations with intermittent flows across the participating countries; • A website of the Action which will be updated; • The organisation of 4 STS at 4 international conferences; • The organisation of 3 RTMWs during which water resource managers from participating countries will be invited to share experiences, limitations, ideas, and concerns, and to discuss the tools and guidelines produced during the Action;.
g. exploring the memories of local people in key areas to extract additional information about drying history); explore IRES drivers in key catchments and differentiate natural and anthropogenic IRES; develop a European-scale network of citizen scientists to locate, map and monitor river flow states using smartphones; produce a hydrological meta-database. Milestones. WG1 will organise 1-2 annual workshops, including one with WG5 and WG6.
Deliverables. Workshop minutes; meta-database of continuous flow data; maps of IRES at the European scale; identification of sentinel areas; a European-scale network of citizen scientists to monitor river flow states; a continuously developing database of drying occurrence across European river networks. WG 2: Flow alterations, ecosystem services and management of IRES Objectives. Quantify the functions and ecosystem services provided by IRES and explore their responses to flow alterations; define rules for flow management in IRES. Tasks. Establish the functions and services provided by IRES; quantify their economic value; explore their relationships with flow regime components and alterations by connecting currently fragmented knowledge across the participating countries; produce a meta-database of past and ongoing scientific projects in IRES; develop an ecohydrological conceptual framework to guide management of flows in IRES.
WG 5: Networking Objectives. to build the network; to link past and ongoing research projects to IRES; to share experience and information; to synthesize the state-of-the-art and knowledge gap analysis of IRES; to ensure active participation of stakeholders. Tasks. To organise annual topical and cross-disciplinary workshops; address the challenge of providing open information on projects and experiments, to facilitate sharing of data and results for general synthesis; meta-analyses and modelling; create a stakeholder committee and ensure continuous exchanges between researchers and stakeholders. Milestones. WG5 will organise annual workshops with all WGs, and several smaller workshops for WG1, 2, 3 and 4 on specific networking aspects.