dataesr/scientific-paragraphs-categorization huggingface dataset:
For system 7, there is one candidate counterimage (7. 6) that is not considered as part of the robust subset as it presents a different colour than the other counterimages. However, the colour variation may be a consequence of this counterimage being behind a very red object (system 6). On the other hand, the morphology and location of candidate image 7. 6 agrees remarkably well with the prediction from the model (see Fig. 5 ). Both systems 14 and 16 are new systems that are confirmed with a high reliability by a version of the lens model that relies solely on the seven systems with spectroscopic redshift. A very similar version of the lens model (that excluded also system 15 from the constraints) predicted system 15 with zgeom = 1. 55 that was confirmed by GLASS data with zspect = 1. 52. We adopt the GLASS estimate for this system.
COMPARISON WITH SIMULATIONS We have extracted a cluster-size halo from the MUSIC dataset of re-simulated clusters  (Sembolini et al. 2013). In particular, we examined the non-radiative run (dark matter plus gas) of the MUSIC-2 data-set looking for a clustersize halo resembling a similar projected mass distribution to that of A370. The MUSIC-2 data-set constitutes a mass limited sample of cluster-like halos selected from the low resolution version of the MultiDark 1 h -1 Gpc simulation (https: //www. cosmosim. org). The MUSIC-2 halos have been resimulated in spheres of 6 h -1 Mpc radius centered in each cluster-size halo. Therefore, the mass resolution (or mass per particle) of the MUSIC-2 halos is increased by a factor of 8 with respect to the parent simulation. After projecting the halos along different lines of sight, we found an unrelaxed halo at z = 0.
The multi-resolution grid, with its shallower less massive BCGs predicts elongated arcs in detailed agreement with the data. Spectroscopic confirmation that systems 7 and 19 have the same redshift would clearly then support the lower mass profiles for the BCGs favoured by the multi-resolution model. We inspected the GLASS data at the positions of systems 7 and 19. For system 7, GLASS data reveal a tentative line in three of the counterimages of this system at around 14000A. This would correspond to a redshift z ∼ 2. 75 for system 7 for the (unresolved) [OII] doublet. System 19 does not show any lines in the spectra. Photometric redshitfs derived for system 19 using the codes BPZ and EAZY result in zBPZ ∼ 0. 3 and zEAzY ∼ 0. 6 respectively. For these redshifts, the lens models predict no radial arcs at the position of system 19 suggesting that the photometric redshifts may be affected by the nearby BCG.
AGN feedback triggered by a SMBH is an interesting possibility as both BCGs are likely to host such SMBH. Chandra data reveals a small X-ray source in the northern BCG that could be a sign of AGN activity. The BCG in the south shows no evidence of associated X-ray emission. As discussed in for example  (Martizzi, Teyssier & Moore 2013) , gas gets ejected by the AGN and returns after cooling. This cycle generates gravitational potential fluctuations that modify the dark matter mass pro-file, resulting in a depletion in the galaxy of vast amounts of baryons and dark matter from the BCG. This mechanism works up to distances of ∼ 20 kpc and effectively produces a plateau of constant dark matter density at the position of the BCG.
ACKNOWLEDGMENTS This work is based on observations made with the  NASA/ESA Hubble Space Telescope  and operated by the  Association of Universities for Research in Astronomy, Inc. under  NASA  contract  NAS 5-2655. Part of the data for this study is retrieved from the  Mikulski Archive for Space Telescope (MAST). The authors would like to thank the  HFF team  for making this spectacular data set promptly available to the community. Support for GLASS ( HST-GO-13459 ) was provided by  NASA  through a grant from the  Space Telescope Science Institute. The scientific results reported in this article are based in part on data obtained from the  Chandra Data Archive 5. We thank the team from the  MUSIC  project for making available the simulations that have been used in this work. We would like to thank  Harald Ebeling  for making the code ASMOOTH (Ebeling, White & Rangarajan 2006) available.
Versions of these models are integrated in more complete forest ecophysiological models simulating the ensemble of forest water and carbon fluxes: (1) MAIDEN (Modeling and Analysis In DENdroecology)  (Gea-Izquierdo et al. , 2015; Misson, 2004) , which contains the isotopic module MAIDENiso  (Danis et al. , 2012) , and (2) MUSICA  (Ogée et al. , 2003 (Ogée et al. , , 2009)). Both account for important postphotosynthetic factors and are able to link photosynthesis and carbohydrate allocation to stem growth. In this paper, we use the MAIDENiso model to decipher the δ 18 O TR variability in American temperature-sensitive species (Picea mariana in northeastern Canada and Nothofagus pumilio in western Argentina). The selected sites are of special interest for palaeoclimatology given that their δ 18 O TR chronologies carry strong temperature signals.
Sampling sites and tree ring data Two high-latitude American native species were studied here: (1) Picea mariana (Mill. B. S. P. ; black spruce), which is a conifer widely distributed over the American boreal forest  (Viereck and Johnston, 1990) , and (2) Nothofagus pumilio  (Poepp. et Endl. Krasser; lenga) , which is an angiosperm deciduous species dominating the high-elevation forests along the Patagonian Andes from 35 to 55 • S  (Donoso, 1981; Schlatter, 1994). We selected two sites of P. mariana in the centre of the Québec-Labrador Peninsula in northeastern Canada (L01 and L20; from 53 • 51 N-72 • 24 W to 54 • 33 N-71 • 14 W; ∼ 480 m elevation; see  Gennaretti et al. , 2014, and Naulier et al. , 2014, for details)  and three sites of N. pumilio in northern Patagonia, western Argentina (NUB, ALM and CHA; from 41 • 09 S-71 • 48 W to 41 • 15 S-71 • 17 W; 1270-1610 m elevation; see  Lavergne et al.
2. 4 ). This estimated set of data is referred to in the following as the estimated δ 18 O P dataset. Second, we run the model with the series of the daily δ 18 O P derived from two general circulation models (GCMs) with different spatial resolutions and enough available data at our site locations: (1) the Melbourne University model (MUGCM; Noone and Simmonds, 2002) forced by varying sea surface temperature (SST) from the HadISST dataset for the 1950-2003 period (2 • × 2 • resolution; hereafter referred to as the MUGCM δ 18 O P dataset), and (2) the Laboratoire de Météorologie Dynamique Zoom model (LMDZ5A;  Hourdin et al. , 2013; Risi et al. , 2010)  with the horizontal winds guided by those of the National Centers for Environmental Protection 20th Century Reanalysis (NCEP20) for the 1950-2008 period  (Compo et al.
, 2011)  (2. 5 • × 3. 75 • resolution; hereafter referred to as the LMDZ-NCEP20 δ 18 O P dataset). The final δ 18 O TR time series are the annual average of the δ 18 O TR daily values (Eq. 1) weighted by the daily simulated stand gross primary production (GPP), assuming a proportional allocation of carbon to the trunk. For the northeastern Canadian sites, the GPP simulated by MAIDENiso was optimized using observations from an eddy covariance station (see  Gennaretti et al. , 2017a). Unfortunately, such observations were not available for N. pumilio, and therefore the parameterization obtained for the GPP of P. mariana was also used for the western Argentinian sites but constraining the simulations with phenological observations extracted from the literature. For example, to respect the annual cycle of the leaf area index (LAI) for N.
Estimated versus modelled and observed δ 18 O P values The modelled δ 18 O P series from the GCMs are similar to the GNIP datasets, with mean values ranging from -12 to -8 ‰ over June-September in northeastern Canada (Fig. S2a ) and from -7 to -3 ‰ over December-April at the western Argentinian sites (Fig. S2b ). In general, δ 18 O P series from LMDZ-NCEP20 model in western Argentina are slightly displaced toward higher values (+1 ‰) in comparison with the GNIP and MUGCM data. The estimated δ 18 O P values based on plausible values of coefficients a and b agree well with those of the models and observations in northeastern Canada. For the western Argentinian sites, they are 2-3 ‰ lower from April to October, i. e. late spring-early autumn (Fig. S2 ). 1 ). The horizontal dot lines in (b) are the mean values of the observed δ 18 O TR.
This suggests that the imprint of the precipitation amount on δ 18 O P in western Argentina is low and that δ 18 O P variations are mainly controlled by seasonal changes in temperature, which is in agreement with previous work  (Rozanski et al. , 1995). However, due to the strong west-to-east precipitation gradient in this region (orographic rain shadow), large δ 18 O P variations occur over short distances  (Rozanski et al. , 1995; Smith and Evans, 2007; Stern and Blisniuk, 2002). Therefore, the daily precipitation dataset extracted from the gridded reanalysis data, which has a low spatial resolution (> 200 km), may not represent the daily variations in precipitation at a local scale faithfully. Therefore, the model may underestimate the contribution of precipitation on δ 18 O P variability in this particular area.
In contrast, in northeast Canada, both temperature and precipitation amount equally control the δ 18 O P variations. The high amount of precipitation falling in summer (∼ 46 %) should have a strong effect and decrease the δ 18 O P values in the condensed water, while high temperatures counteract this effect by increasing this ratio. Before reaching northeastern Canada, the air masses pushed by the dominant westerly winds discharge most of their humidity over the land, leading to a depleted δ 18 O P signal at our sites (for the same reason, δ 18 O TR values at L20, which is located 110 km northeast of L01, are ∼ 1 ‰ lower). Moreover, the δ 18 O P signal in the Canadian sites is comparatively more depleted than in the Argentinian sites, because of their higher latitude. It is worth noting that the resolution of the gridded meteorological dataset used for the Canadian sites is relatively high (∼ 10 km), which means that the local processes are likely well represented.
, 2010) , results at specific sites, especially in mountainous regions such as at our western Argentinian site, can be less accurate (Fig. S2 ; see the offset between GNIP stations and LMDZ-NCEP20). Ideally, daily δ 18 O P longterm records from meteorological stations in the study region should be used as an input of MAIDENiso. Simulations from high-resolution regional circulation models, such as REMOiso, which has a 0. 5 • × 0. 5 • (∼ 55 km) horizontal resolution  (Insel et al. , 2013; Sturm et al. , 2005 Sturm et al. , , 2007)) , may produce reliable local δ 18 O P values. Such a dataset has proven to be quite helpful with MAIDENiso in the Fontainebleau forest (France)  (Danis et al. , 2012). However, up to now, measured or REMOiso δ 18 O P datasets in our regions of study do not exist, which is the case for most regions of the world.
Moreover, early data (1970  from GNIP stations may have been compromised by pan evaporation and therefore isotopic enrichment. Therefore, we recommend that daily GNIP stations are set up in various forested ecosystems, that an effort is accomplished to homogenize older GNIP time series, and that high-resolution simulations of δ 18 O P are performed in wider regions. The modelling of δ 18 O TR values based on the estimation of δ 18 O P is relatively more accurate for northeastern Canada than for western Argentina (Fig. 3 ). As the mean levels of the measured δ 18 O TR values are high at the western Argentinian sites (mean value of about 30 ‰ ), the Bayesian optimization tends to increase the biochemical (ε 0 ) and kinetic (ε k ) fractionations as well as the coefficient a, while reducing the dampening factor (f o ) to reach more representative mean levels of the δ 18 O TR simulation.
Acknowledgements. This study has been co-funded by the research federation ECCOREV. Aliénor Lavergne has been supported by a research associate/lecturer position at the  Aix-Marseille University (France). Fabio Gennaretti  has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement no. 656896. We acknowledge all data providers: the  Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA, Argentina)  for providing the daily temperature data from  La Almohadilla  site; the  National Meteorological Service from Argentina  for providing the monthly temperature data from Bariloche meteorological station (Argentina); the  Department of Natural Resources Canada  for providing the daily climatic data used for Québec; the  US Department of Energy, Office of Science Biological and Environmental Research (BER)  and the  National Oceanic and Atmospheric Administration Climate Program Office  for providing the daily climatic data used for Argentina; and the  SWING  project for providing the daily δ 18 O P data from MUGCM model.
Code and data availability. The code of the model can be found at https://doi. org/10. 6084/m9. figshare. 5446435. v1 (recent version developed by Gennaretti et al. , 2017). The daily δ 18 OP data from the MUGCM model were extracted via the SWING project webpage (SWING project, http://paos. colorado. edu/~dcn/SWING/ database. php). The daily climatic data used for Québec were retrieved from the webpage of the Natural Resources Canada products (Natural Resources Canada, http://cfs. nrcan. gc. ca/projects/3/ 4), while those for Argentina were extracted from the Earth Sys-A. Lavergne et al. : Modelling tree ring cellulose δ 18 O variations tem Research Laboratory (ESRL) of the National Oceanic & Atmospheric Administration (ESRL-NOAA) webpage (ESRL-NOAA, 20th Century Reanalysis V2c, https://www.
esrl. noaa. gov/psd/data/ gridded/data. 20thC_ReanV2c. html). The atmospheric CO 2 concentration data derived from the Mauna Loa station were extracted from the ESRL-NOAA webpage (ESRL-NOAA, Mauna Loa data, http://www. esrl. noaa. gov/gmd/ccgg/trends/). The Supplement related to this article is available online at https://doi. org/10. 5194/cp-13-1515-2017-supplement.
• 12 S-71 • 12 W; 840 m a. s. l. ; Servicio Meteorológico Nacional, Argentina). The resulting maximum and minimum temperature series, covering the 1952-2011 period, fit well with the daily local temperature data from La Almohadilla (ALM) site (41 • 11 S, 71 • 47 W; 1410 m a. s. l. ; data measured by data loggers and provided by IANIGLA) available over the 2002-2012 period (r = 0. 74, p< 0. 001; Fig. S1 in the Supplement). For the northeastern Canadian sites, climate data were obtained from the gridded interpo- lated Canadian database of daily minimum-maximum tem- perature and precipitation covering the 1950-2005 studied period (0. 08 • × 0. 08 • resolution;.
e. , comparing a newly developed fire module to the one it is designed to replace). One study has performed such a comparison on a regional basis, for Europe  (Wu et al. , 2015). Less formal comparisons (e. g. ,  Baudena et al. , 2015)  are difficult to interpret because published simulations differ in terms of the techniques used to initiate the simulations, the climate inputs used, the time interval considered, and the treatment of land use. Diagnosis of the influence of structural differences between models on simulated fire regimes can only be achieved through a comparison of model performance when forced by identical inputs (e. g. ,  Taylor et al. , 2012). The Fire Model Intercomparison Project (FireMIP, http://www. imk-ifu. kit. edu/firemip. php; Hantson et al. , 2016)  seeks to improve our understanding of fire processes and their representation in global models through a structured analysis of simulations using identical forcings and the evaluation of these simulations against observations.
LPJ-GUESS-SIMFIRE-BLAZE The new Blaze-Induced Land-Atmosphere Flux Estimator (BLAZE;  Nieradzik et al. , 2017)  was recently implemented into the latest version of LPJ-GUESS  (Lindeskog et al. , 2013; Smith et al. , 2014). Burned area is generated once per year by the empirical fire model SIMFIRE  (Knorr et al. , 2014 (Knorr et al. , , 2016) )  based on fire weather, fuel continuity, and human population density. This annual burned area is distributed to each month of the year based on mean observed seasonality (climatology) of burned area from GFED3  (Giglio et al. , 2010). Fuel consumption and tree mortality are then estimated using the BLAZE module, which computes fireline intensities from existing fuel load and fire weather parameters which are translated into height-dependent survival probabilities as described in the Population-Order-Physiology (POP) tree demography model  (Haverd et al.
STICS model is composed of seven modules: growth, development, shoot and root growth, yield components, thermal environment, water balance and nitrogen balance  (Brisson et al. , 2008). Leaf area index is a dynamic parameter used to simulate radiative budget, taking into account the morphology of plant cover. Carbon assimilation is simulated with detailed biochemistry of photosynthesis and gross respiration. The effect of CO 2 on stomatal resistance is taken into account to compute the budget of radiative and convective energies. Water is transferred in soil to the underlying horizon as soon as water holding capacity is exceeded. For organic matter evolution, N cycle is first simulated, then C evolution is derived, by assuming a constant value of C/N ratio  (Brisson and Levrault, 2010). STICS takes into account climate, soil characteristics, the type of crop and crop management.
3 and 6. 5 %. For plants, analyses of major inorganic elements of the main families of plants were performed by ICP dry residues on the three cuts of 2015 (at INRA-USRAVE, Bordeaux laboratory, France). A database of the mineral composition of the hay since 1960 has been built from analysis reports conducted for the professional organization of Crau's hay Committee in accordance to the specifications of originally protected designation of COP (in french AOP)  (Mohammed et al. , 2016a). For fertilizers, representative samples used in Crau were collected in 2014 and X-ray diffractometry was performed at ISTerre laboratory from Grenoble University. Chemical analyses of previous years were collected from the Crau's hay Committee (Comité du foin de Crau, 2014). Enquiries were made for fertilizer management, which supplied information about the inputs of fertilizers (see Supplemantary material).
Acknowledgements We are grateful to the farmers in Crau, MM. Blanc and Jarrige, for the generous access to their grasslands, to  François Charron , Deputy Director of the experimental domain ( Le Merle ,  SupAgro Montpellier ) and to  Milanka Babic ,  Fabrice Flamain  and  Ghislain Sevenier  for their help with the sampling and laboratory analysis. We sincerely thank  Françoise Ruget  and.
, 2014. Assessment of contaminants transport in a watershed Song, Z. , Zhao, S. , Zhang, Y. , Hu, G. , Cao, Z. , Wong, M. , 2011. Plant Tatin, L. , Wolff, A. , Boutin, J. , Colliot, E. , Dutoit, T. (Eds. ), Écologie et sprawl on soil resources of Nanjing city using satellite images and digital 3 Mass balance of inputs by irrigation water and uptakes by M. , 2013. Evaluating adjusted forcing and model spread for historical and and their impact on the recharging effluent, the soil aquifer treatment (sat) 185-195. 2211-2233. affected by acid mine drainage, by coupling hydrological and geochemical impact on CO2 consumption by silicate weathering: the role of bamboo. conservation d'une steppe méditerranéenne: la plaine de Crau. Éditions soil databases. Catena 69, 16-30. future scenarios in the CMIP5 generation of climate models.
, 2006). Nevertheless, as with all existing sample-holder standards, the SPINE standard has become a limiting factor at high-throughput beamlines. At the most recent third-generation synchrotron MX beamlines, the time needed to centre and align a crystal with the X-ray beam is comparable to the time needed to collect an X-ray data set  (Svensson et al. , 2015; Casanas et al. , 2016). This significantly impairs beamline efficiency. The situation will become worse at future fourth-generation light sources where only tens of milliseconds will be necessary for a typical MX data collection. This overhead could be significantly reduced by using sample holders that provide precise initial crystal positioning, in particular for crystals harvested by automated systems that can record a crystal position in the sample holder  (Cipriani et al.
This also limits the repositioning precision upon loading/unloading, which makes loop and/or crystal alignment necessary before each data collection  (Lavault et al. , 2006; Pothineni et al. , 2006; Svensson et al. , 2015). Furthermore, the cost of the sample holder can limit the number of samples prepared and therefore the usage of high-throughput techniques for synchrotron data collection  (Abola et al. , 2000). Reusable sample-holder bases can contribute to cost reduction but require additional manpower to clean and recycle the sample holders. Consequently, in 2009 a feasibility study for a compact, precise and, as far as possible, cost-effective sample holder with corresponding manual and robotic handling tools compatible with six-axis industrial robots was initiated at the EMBL Grenoble Outstation.
NewPin NewPin (Fig. 5a  and Supplementary Fig. S7 ) is our ultimate sample-holder proposition. The sample holder is a single pin on which the crystal mount is directly attached. Specifically adapted for fully automated harvesting and data-collection pipelines, it should fit perfectly into future entirely robotic systems that cover crystal harvesting and data collection. In this case, the storage density could reach up to 64 sample holders in a puck and up to 512 samples in the eight-puck topaccess canister compatible with the CX100 shipping dewar. Manual crystal harvesting is difficult but possible using a set of handling tools and pucks with a capacity reduced to 36 sample holders currently proposed here. A possible design for a 64sample version is proposed in Fig. 6(e ). The NewPin sample holder fits in a specific goniometer mount containing a socket (Figs.
Acknowledgements We thank the BioStruct-X partners for their advice during the project and their precious feedback on testing:  Vincent Olieric ,  Isabelle Martiel  and  Takashi Tomizaki  at  SLS  ( Paul Scherrer Institute, Villigen, Switzerland ),  Uwe Mu  ¨ller and  Manfred S. Weiss  at  BESSY II (Helmholtz-Zentrum Berlin fu ¨r Materialien und Energie, Berlin, Germany) ,  Marjolein Thunnissen ,  Thomas Ursby  and  Uwe Mu  ¨ller at  MAX-IV Laboratory (Lund, Sweden) ,  Thomas Schneider ,  Gleb Bourenkov  and  Stefan Fiedler  at  EMBL@PETRA-III  ( European Molecular Biology Laboratory, Hamburg, Germany ),  Sean McSweeney ,  Didier Nurizzo  and  Christoph Mueller-Dickmann  at  ESRF (Grenoble, France) ,  Katherine McAuley  and  David Hall  at  Diamond Light Source Ltd (Oxford, England)  and the  EMBL Grenoble Outstation  ( European Molecular Biology Laboratory, Grenoble, France ).