dataesr/scientific-paragraphs-categorization huggingface dataset:
The 50-level vertical discretization retained for this system has 1 m resolution at the surface decreasing to 450 m at the bottom, and 22 levels within the upper 100 m. The bathymetry used in the system is a combination of interpolated ETOPO1 and GEBCO8 databases. The system was initialized on 11 October 2006 based on the temperature and salinity profiles from the EN4 monthly gridded climatology. The atmospheric fields forcing the ocean model are taken from the ECMWF (European Centre for Medium-Range Weather Forecasts) Integrated Forecast System. A 3-h sampling is used to reproduce the diurnal cycle. The system does not include neither tides nor pressure forcing. The monthly runoff climatology is built with data on coastal runoffs and 100 major rivers from the  Dai et al (2009)  database  (Lellouche et al.
These are complemented by 11 tidal harmonics built from FES2004  (Lyard et al. , 2006)  and TPXO7. 1  (Egbert and Erofeeva, 2002)  tidal models solutions. Fresh water river discharge inputs are implemented as lateral open boundary condition for 33 rivers. Flow rate data imposed is based on a combination of daily observations from PREVIMER, simulated data from E-HYPE hydrological model and monthly climatological data from GRDC and French "Banque Hydro" dataset. Further details can be found in  Sotillo et al. , (2015). Originally, the operational IBI system was based on a periodic re-initialization from the GLOBAL parent solution. Afterwards, IBI has steadily evolved: by April 2016, an upgrade of the downscaling methodology was implemented, substituting the periodic re-initialization by an spectral nudging technique in order to avoid temporal discontinuity inherent to the periodic re-initialization and minimize dependency from the GLOBAL parent solution on the shelf.
The chosen spectral nudging method permits to ″nudge″ the low frequency IBI system solution towards the large scale GLOBAL solution in those areas where this global solution is supposed to be better (mainly off the shelf and in deep waters) due to the assimilation of lower frequency signals. Thus, the nudging is regionally limited in those areas where the parent system can not improve the regional model (e. g. where there is no data assimilation of altimetry or where the physics is missing, for instance on the shelf) or where the spatial filtering processes are potentially damage (for instance close to the bottom or the open boundaries). This spatial weight function is a 3D mask showing transitions between zones where the IBI system is kept nudged (typically off shore, outside from the shelf area, and off the open boundaries) and the ones where IBI remains free.
The shallower level is at 2. 5 m depth. The bathymetry is derived from a combination of the GEBCO bathymetry data set and fineresolution bathymetric charts of the Strait of Gibraltar and the continental shelf of the Gulf of Cadiz and northern coast of the Alboran Sea. The bottom topography is represented as partial vertical cells. In the two lateral open boundaries (west and east) the model is forced by daily mean temperature, salinity and velocity fields from CMEMS-IBI regional model  (Sotillo et al. , 2015). In addition, tidal and meteorologically-driven barotropic velocities are prescribed across the open boundaries, the former extracted from the model described by  Carrere and Lyard (2003)  and the latter from the storm surge operational system developed by  Álvarez-Fanjul et al. (2001) , which accounts for the remote effect of the atmospheric forcing in the Ocean Sci.
copernicus. eu/). Complementarily, opportunistic inter-comparisons are conducted in the frame of diverse EU-funded projects such as MEDESS-4MS  (Sotillo et al. , 2016-a) : 35 satellite tracked drifters were released on both sides of the Strait of Gibraltar and the quality-controlled in situ data of sea surface temperature and currents were collected to build the MEDESS-GIB database  (Sotillo et al. , 2016-b) , providing hence a complete Lagrangian view of the surface inflow of Atlantic waters through the GIBST and the Alboran Sea. Such valuable oceanographic information was subsequently used to intercompare IBI and SAMPA forecast products to identify strengths (realistic simulation of the Atlantic Jet and the Algerian Current) and shortcomings (position and intensity of the Alboran gyres, especially the western one) in both models performance.
Recent works relying on this HFR system have successfully investigated the water exchange between Algeciras Bay and the Strait of Gibraltar  (Chioua et al. , 2017) , the impact of the atmospheric pressure fluctuations on the mesoscale water dynamics of the Strait of Gibraltar and the Alboran Sea  (Dastis et al. , 2018) , the dominant modes of spatio-temporal variability of the surface circulation  (Soto-Navarro et al. , 2016)  or the characterization of the Atlantic surface inflow into the Mediterranean Sea  (Lorente et al. , 2018). In the present work, quality-controlled hourly HFR current measurements collected during the entire 2017 were used as benchmark to elucidate the skill of a number of OOFSs. As shown in Figure  1-c , the data availability was significantly high: almost 100% in the selected transect, decreasing in the easternmost sectors.
Temperature The CMEMS L3 satellite-derived daily data were used to validate the SST fields predicted by both GLOBAL and IBI. Maps of SST anomalies were computed on a monthly basis (Figure  2 ). Apparently, both models behaved similarly during winter (defined as JFM). In January, low warm anomalies were detected off-shelf in northwest Atlantic waters, while cold SST anomalies were encountered in coastal areas of the English Channel and the Irish Sea, and also surrounding the Balearic Islands in the Western Mediterranean (Figure  2  (Figure  2, c-d ). The negative bias previously observed almost disappeared around the Balearic Islands and in the Irish Sea, turning even into slightly warm anomalies inside the English Channel in the case of GLOBAL. During spring months (AMJ), a small positive bias spread over almost the entire IBI domain (Figure  2 , e-f).
Besides, IBI properly reproduced the SST field in the northern IBI area although clearly overpredicted coastal temperatures over ACUS, in the periphery of the Canarias Islands. During summertime (JAS), the warm SST spring anomalies previously identified in both GLOBAL and IBI became even more pronounced in July, locally reaching values up to 2ºC (Figure  2 , g-h). Besides, new features were revealed as a result of the onset of the upwelling season, such as the positive bias in the Iberian Upwelling System (IUS), higher in the case of IBI (Figure  2, h ). According to the reduced bias in both western coastal upwelling systems (Figure  2 , g), GLOBAL seemed to better replicate the SST field likely thanks to recent progresses in data assimilation schemes and to the growing wealth of available observational data.
8 (early summer). Furthermore, IBI seemed to outperform GLOBAL in the two northernmost sectors within the study-domain: English Channel (ECHAN, Figure  3-g ) and Irish Sea (IRISH, Figure  3 -h) in terms of higher correlation and lower RMSD, especially pronounced during summertime. Tidally driven mixing could account for a portion of the discrepancies encountered between the coarser detided GLOBAL and IBI model solutions, where the former predicts an overstratification in shelf-seas. Finally, as SST estimations are routinely assimilated in GLOBAL system, a more precise performance is generally expected offshore, as proved for ICANA or WESMED, among other sub-regions. Results above exposed reveal that SST divergences between IBI and GLOBAL forecast datasets are mainly found on the shelf near coastal areas featuring a complex bathymetry.
The lower performance of IBI in ICANA sub-region was previously reported by  Aznar et al (2016)  when inter-comparing IBI forecast and 1/12º reanalysed solutions. At this point it is worth recalling that GLOBAL includes a data assimilation scheme, whereas IBI takes realistic ocean conditions from weekly global analyses. This fact shows up the possible benefits of the observational data assimilation in these areas, at least in terms of surface variables. Furthermore, a fraction of observed model-buoy discrepancies in SST can be explained in terms of disparate depth scales: whereas IBI and GLOBAL daily outputs are representative of the temperature in the upper one meter of the water column, moored buoys provide temperature estimations at a deeper nominal depth (between 1 and 3. 5 m, depending on the brand).
Salinity As pointed out in the introduction, the enhancement of riverine forcing is still as a priority in ocean modelling as the estuarine circulation is mainly driven by horizontal density gradients which are ultimately modulated by freshwater inputs. In this context, previous works have investigated the potential benefits of replacing old climatologies by data from hydrological model predictions  (O´Dea et al. , 2017). Here we provide a specific example to illustrate the discrepancies between GLOBAL and IBI performances in the Galician coast (NW Spain), as a consequence of the different horizontal resolution and distinct runoff forcing implemented in the operational chain. While both models performances are rather similar in open-waters (according to the results derived from the validation against 3D Argofloat profiles and exposed in the QUID), higher discrepancies are expected to arise in coastal and shelf areas as they are governed by small-scale processes such as land-sea breezes, runoff (and the resulting stratification and buoyancy-driven circulation), transport materials (nutrients, sediments, pollutants, etc.
Additional statistical indicators were computed: two histograms illustrated the number of hourly zonal (U) and meridional (V) velocity data per class interval (Figure  9 , a-b). HFRderived zonal velocity estimations exhibited a Gaussian-like shape clustered around 84 cm•s -1 and slightly shifted to lower values in the case of SAMPA coastal model (79 cm•s -1 ). Both datasets show similar positive bias and variability, with the standard deviation around 56-57 cm•s -1 for 2017 (Figure  9 , a). IBI and GLOBAL presented narrowed histograms, with distributions positively biased and constrained to zonal velocities above 0 and 40 cm•s -1 , respectively. In the case of meridional currents, each distribution exhibits a nearly symmetrical Gaussian-like shape but biased towards different values (Figure  9, b ).
Discuss. , https://doi. org/10. 5194/os-2018-168 Manuscript under review for journal Ocean Sci. Discussion started: 17 January 2019 c Author(s) 2019. CC BY 4. 0 License. afterwards, by the 18 th of August. A secondary peak of SST was monitored by the 25 th , before the CCC started weakening. In accordance with previous statements about model behaviour for the zonal currents, once again SAMPA outperformed the parent systems as reflected by a significantly high correlation of 0. 89 and a lower but statistically relevant RMSD of 1. 22ºC. IBI presented a general bias (positive the first week of august and negative the rest of the month) but adequately reproduced the temporal variability of the SST field (correlation of 0. 67). In the case of GLOBAL, the system could not benefit from data assimilation in this intricate coastal area with low level of available observations: worse skill metrics were subsequently obtained, with a correlation of 0.
In terms of temperature, results highlighted the overall benefits of both the GLOBAL data assimilation in open-waters and the increased horizontal resolution of IBI in coastal areas, respectively. IBI outperformed its coarser parent system in those coastal regions characterized by a jagged coastline and a substantial slope bathymetry. As GLOBAL has a smoothed bathymetry and do not resolve many narrow features of the real sea floor, the depths where mixing takes place could be biased. Besides, those mixing processes acting at scales smaller than the grid cell size might substantially affect the resolved large-scale flow in the coarser GLOBAL system. On the other hand, since GLOBAL is a detided model solution, tidally-driven mixing could account for a portion of the discrepancies found between GLOBAL and satellite-derived SST estimations in energetic tidal areas such as the English Channel, the North Sea and the Irish Sea.
Finally, SAMPA coastal model outputs were analysed in order to put in a broader perspective the context of the onset, development and end of such flow reversal and its impact on the AJ-WAG coupled system. The synergistic approach based on the integration of HFR observing network and SAMPA predictive model has proved to be valid to comprehensively characterize the highly dynamic coastal circulation in the GIBST and the aforementioned episodic full reversals of the surface inflow. In this context, data assimilation would provide the integrative framework for maximizing the joint utility of HFR-derived observations and coastal circulation models. A data assimilation scheme could be incorporated in future operational versions of SAMPA in order to improve its predictive skills, since similar initiatives are currently ongoing with positive results  (Hernández-Lasheras et al.
Phenotypic Data and Common Gardens We used the tree height growth data of Scots pine (P. sylvestris) from 15-year-old plants measured in six planting sites established in Spain, the southern range of the species (Figure  A1 ). Sixteen genetically distinct Spanish populations  [30, 31, 39]  that cover the species distribution in Spain were planted in each planting site (Figure  A1 ). The seeds for population seedlots were collected during the years 1988 and 1989  [39, 40]. A seedlot was composed by mixing the seeds collected in natural populations from at least 25 mother trees, with a 50-m separation distance between each individual, to avoid interbreeding  [41]. In 1991, two-year-old plantlets originating from the seedlots were planted in the planting sites, following a randomized complete block design, with four blocks and a 16-tree square plot for each population, planted at a 2.
Climate Data We used two sources of gridded climate data for the present and future conditions: (1) We used the Gonzalo-Jiménez  [42]  Spanish climatic model-the average climate was calculated for the period between 1961 and 1999-covering Spain at 30 arc sec resolution (~1 × 1 km), to calibrate our models. We used a total of 51 temperature and precipitation-related climate variables to accurately assess the main climatic drivers promoting among-population differentiation and plasticity (Table  1 ). We named the climatic variables of the planting site and the population origin _s or _p, respectively (e. g. , AHM_s and AHM_p for the annual heat-moisture index of the planting site or at the population origin, respectively). (2) We used the WorldClim climate database  [43, 44] -the average climate was calculated for the period between 1970 and 2000-at 30 arc sec resolution (~1 × 1 km), to make spatial predictions of tree height growth for the current and future climates.
We started with the selection of the two-variable interaction (round 1) and then tested the quadratic effects of both climate variables (rounds 2 & 3) and so on downwards the main effects of each predictor (round 4). Fixed effects were tested, using the maximum likelihood (ML), and random effects were tested using the restricted maximum-likelihood method (REML). We computed the percentage of variance explained by the fixed effects of the best supported model, MR 2 , and the percentage of variance explained by the fixed and random effects together, CR 2 , [49,50]. The goodness-of-fit of the best model was assessed by examining the predicted vs. observed values. We used normal error distribution with an identity link. We used R (version 3. 2. 3, 10 December 2015) run in the Linux-GNU operating system to perform all of the analyses, and the "lme4" and "lmerTest" packages  [50, 51].
Spatial Predictions of the Local Adaptation of Tree Height Growth for Southern Scots Pine Populations under the Impacts of the Current and Future Climate We predicted the optimum tree height growth (H OPT ) and local tree height growth (H L ) at age 15, based on our models. Our predictions were performed across the species' range in Spain  [52]. Then, we estimated LA H as the difference among them. Moreover, we predicted tree height growth by the year 2070 under the RCP 8. 5. scenario (H L -FUT)  [53]. Differences between H L -FUT and H L , (D FUT-PRES ), can inform us about the climate change impact on Scots pine populations. Positive values of D FUT-PRES mean that Scots pine populations will increase the tree height growth, while negative values would indicate a decrease. We used the 'raster' package in R for all of the spatial computations.
Funding: This study was funded by: the  Spanish Ministry of Economy and Competitiveness , through the following projects VULPINECLIM (MINECO,  CGL2013-44553-R ), ADAPCON ( CGL2011-30182-C02-01 ),  FENOPIN  ( AGL2012-40151-C03-02 ), and the following two projects: " Investments for the future" Programme IdEx Bordeaux  (reference  ANR-10-IDEX-03-02 ) and the  EFPA (Département Ecologie des Forêts, Prairies et milieux Aquatiques) INRA  ( Institut National de la Recherche Agronomique ) Integration Project. NVP was funded by a fellowship  FPI-MCI  ( BES-2009-025151 ) and a postdoctoral contract associated with the European Project  H2020  " Optimising the management and sustainable use of forest genetic resources in Europe" (GenTree ; grant agreement No  676876 )". NGM was supported by an  AgreenSkills postdoctoral fellowship.
The versatility of MUST 2 has been proved with the successful use of different trigger options and electronics. The data is acquired by means of software developed for the CERN's Scalable Readout System electronics and subsequently analyzed with a muon trajectory reconstruction algorithm, which retrieves the: time of passage, 2D position, zenith and azimuth angles of the muons traversing the detector. The characteristics, performance and limitations of the data acquisition chain are presented and evaluated, a series of guidelines towards the improvement of its efficiency of are provided. A series of characterization tests has been carried out in different environments: controlled muon beam, open sky, at the bottom of a valley and in underground conditions. These tests have enabled a better understanding of the performance of the detector and allowed to tune up its operational parameters.
Despite the weak statistics of the test runs, the measured muon flux has shown a good correlation with the surrounding target volumes. A campaign of measurements in real field conditions has been carried out at the Saint-Saturnin-les-Apt (Vaucluse, France) dam. The experimental results obtained are in consonance with the values anticipated by the digital model, the field transportability and the capability to perform long-term out-of-lab measurements have been demonstrated. On the downside, the impact of the external temperature on the data acquisition should be balanced out to get a steady acquisition and monitor the temporal evolution of the muon flux. In conclusion, the successful proof-of-concept trial allows to validate the MUST 2 camera for transmission muography purposes. The current work is the result of countless hours of dedication, not only mine but also from several people from different fields and backgrounds.
Numerical simulations of the detector charge dispersion made with the Garfield++ software can help to estimate the spatial resolution. Since a full simulation of the detector physics would be excessively time consuming, electron drifts are not fully simulated. Instead, the results from the gas simulation presented in Section 5. 3 are used to characterize the electrons after their drift in the TPC. Gaussian distributions are used with mean and variance depending on the drift length, gas nature and electric field; only then is amplification fully simulated. Regarding the geometry of the simulation, only a finite zone containing 20 strips of 1mm pitch in the upper readout plane is considered. The results of the charge dispersion along the X direction during the electron drift are shown in Figure  5.
Electric field Field homogeneity is of major importance in obtaining a straight projection of the particle path during TPC operation. The shape of the electric field inside the TPC has been simulated with two different software packages -QuickField and COMSOL multiphysics®-both yielding equivalent results. The first scenario considers a TPC without electric field homogenizers, a drift cathode polarized at -3. 000 V and a grounded Micromesh. Figure  5. 19 shows the result of the simulation made with COMSOL multiphysics®: the field lines allow visualizition of the shape of. ,⃗ +/012 , and represent the most probable path of the electrons generated inside the drift volume. The figures reveal important electric field distortions near the TPC frame. The non-orthogonality of the field lines to the detection plane cannot be neglected in the first 60 mm around the metallic frame 3 , which results in an alteration of over 16% of the active surface.
This scenario is illustrated in Figure  5. 42. Both images represent a 2D histogram of the detected points in an acquisition immediately following the closing of the TPC. This dataset corresponds to the experiment for validating the self-trigger from the micromesh described in Section 5. 7. 3. The active zone of the detector was limited to the zone between Y channels 128 to 256 and X channels 1 to 1. 024. 42 (A) presents 3 points with abnormally high detection rates located near the detector frame, probably due to the presence of particles after a service operation in this zone (exchange of the gas line). After 3 h, Figure  5. 42 (B) shows that the high detection rates at the same points has drastically decreased to the average.
Valley measurements The laboratory main entrance is located, as seen in Figure  7. 15, at the lowest point of a narrow valley. Figure  7. 20 (A) shows a 360º panoramic view with the location of the detector, inside the white modular building. An experimental measurement was carried out at the entry platform of the LSBB with the same experimental setup as for the open sky measurements. A dataset corresponding to 3h of acquisition was been compared by means of the ratio shown in Equation (7. 2). The elements of the ratio are inversed compared to the previous section to highlight the zones with larger attenuations. Another representation of the same ratio is shown in Figure  7. 21 (A), this time considering the zenith and azimuth angles. The number of muons measured has been distributed into a 90 x 90 matrix, with the color scale indicating the ratio value in a given bin.
Rev. Physyque Appliquée 12, 67. xi Yamashita, T. et al. (1992). Measurements of electron drift velocities xii Santos, M. A. G. et al. (2017). Ion mobility studies for the ILC experiment. Presentation at CERN RD51 collaboration meeting. xiii Biagi database (v8. 9), www. lxcat. net, retrieved on April, 2018 xiv Itikawa database, www. lxcat. net, retrieved on April, 2018 xv "COMSOL Multiphysics Reference Manual, version 5. 2", COMSOL, Inc, www. comsol. com xvi Bunemann, O. , et al. (1949) Design of grid ionization cham-bers. xxii Silva, M. (2015). Evolution of Ionizing Radiation Research. Mitsuru Nenoi, ISBN 978-953-51-2167-1. DOI: 10. 5772/60914 xxiii.