dataesr/scientific-paragraphs-categorization huggingface dataset:
It assumes hydrostatic equilibrium and the Boussinesq approximation. Vertical mixing is parameterized according to a k -ε model implemented in the generic form proposed by  Umlauf and Burchard (2003)  including mixing induced by surface wave breaking. The bathymetry is derived from GEBCO 08 dataset  (Becker et al. , 2016) , merged with several local databases. The IBI run is forced with atmospheric fields from the ECMWF ERA Interim  (Dee et al. , 2016). The 10 m wind, surface pressure (added with inverse barometer approximation), 2 m temperature, and relative humidity are provided at 3 h intervals. On the contrary, precipitation and radiative fluxes are provided as daily averages from a modified ERA-Interim reanalysis  (Sotillo et al. , 2015). CORE empirical bulk formulae  (Large and Yeager, 2004 ) are used to compute latent sensible heat fluxes, evaporation, and surface stress.
, 2013; Gatti and Pouliquen, 2017)  database are assimilated as well as high-resolution sea surface temperature data obtained from analysis of multi-satellite and Advanced Very High Resolution Radiometer (AVHRR). products and remote sensing sea level anomalies (SLA) measured by radar altimeter (Jason-3, Sentinel-3A, HY-2A, Saral/AltiKa, CryoSat-2, Jason-2, Jason-1, T/P, ENVISAT, GFO, ERS1/2). This study is based on the regional analysis of the IBI averaged circulation between 500 and 1500 m for the period 1992-2016. IBI reanalysis provides ocean fields at 947 and 1045 m depth; therefore, in this work, the 1000 m depth fields shown are derived from the average of these two IBI levels. The resulting average circulation field offers information about the main transports occurring in the region.
Since the time average is computed from daily data at 1/12 • resolution, the analysed fields include the net mesoscale transports, filtering its intrinsic temporal variability. The inter-annual variability in MOW and its associated oceanic patterns is studied by the analysis of the high/low salinity events in Horseshoe Basin at 500-1500 m depth. These events are defined as the 10th and 90th percentiles of the salinity time series. The composites of temperature and salinity are derived from these events providing an image of the average ocean state under these conditions. Part of the analysis is focused over several specific areas (boxes and sections defined in Fig. 1 ). These regional domains were selected when taking into account general circulation patterns. Horseshoe Basin is split into two different boxes: the northern one (Gorringe Bank box, GBB) comprises Gorringe Bank and the adjacent western mountain chain, the southern box (Ampere Bank box, ABB) surrounds the Ampere Bank and the seamounts that form the southern boundary of Horseshoe Basin.
These systems are linked using a one-way nesting approach and form the backbone for further downscaling to nearshore areas (including harbours) through efforts such as reported in the present work. Recently, the Government of Canada has announced a new strategy to increase marine safety and environmental emergency response capability through the Oceans Protection Plan (OPP). The present work aims to evaluate the capability of the NEMO modelling framework to provide very-highresolution modelling at port-scale. Although NEMO was initially developed with climate and global-scale modelling in mind, the development work described in this manuscript would allow the ocean and coastal areas to be modelled using the same modelling framework, simplifying the model development and maintenance of the operational systems.
The third nested grid covers Saint John harbour and the approach at 100-m resolution (SJAP100). Both BoF500 and SJAP100 domains include a substantial portion of the lake and river system, including Reversing Falls. The location of the boundary condition for Saint John River (star on Fig. 2c ) is located upstream of Reversing Falls where the tidal amplitudes are small and salinity approaches zero. This allows us to treat the boundary condition as a freshwater boundary condition (see Section 2. 4 for details). A high-resolution coastline dataset provided by the Canadian Hydrographic Service (CHS) is used to define each model component's coastline. This coastline data is referred to as the Canadian Continuous Vertical Datum Hydrographic Ve r t i c a l S e p a r a t i o n ( H y V S E P ) s o l u t i o n (CANEAST2015v1CL).
For the model bathymetry within the Bay of Fundy and part of the Gulf of Maine, highresolution bathymetry data from CHS is interpolated to an ORCA grid at 100-m resolution. This data provides bathymetry directly to the inner model domain (SJAP100) and is spatially averaged to generate bathymetry for BoF500. Over a small area at the southwest corner of the BoF500 domain, not covered in the CHS database, we interpolate the bathymetry from the global high-resolution SRTM30 product  (Becker et al. 2009 ). In the absence of a wetting-drying scheme in the NEMO version used for this study, the bathymetry of the upper Bay of Fundy was adjusted to ensure numerical stability required because of the large tidal range. The modification included shortening the length of Minas Basin while increasing the mean depth of the water points to avoid drying, while conserving the volume of the basin.
Similarly, the same variables from BoF500 are used to force SJAP100. Therefore, the tidal signal for elevation and velocities are resolved in the high-frequency boundary conditions and propagates from the shelf model to the port-scale model, with external tidal forcing applied only at the BoFSS1/36 boundaries. As part of the one-way nesting approach, the three models are required to run sequentially to provide boundary conditions for the next higher resolution model. Staggered start dates are required to allow the different systems to spin-up sufficiently before beginning the next level. The evaluation period is defined from May 2015 to mid-June 2016, based on the availability of a significant number of high-quality observational datasets within Saint John harbour and the surrounding area (Fig.
The reference level used to report the Oak Point water level is referenced to the Canadian Geodetic Vertical datum of 1928 (CGVD28) and may not be the same as the reference level in our model. Therefore, it was necessary to adjust the Oak Point data to ensure we use a consistent reference level. At three representative tide gauges in the region (Saint John, Yarmouth and Halifax), the observed mean water level (MWL) relative to CGVD28 is 0. 314, 0. 299 and 0. 277 m, respectively. The water levels of the different NEMO model components share the same reference level through the setting up of the barotropic OBC, but their reference to the CGVD28 is unknown. To determine this, we first computed the MWL based on the solution from RIOPS. At the above three locations, the modelled estimated MWL are -0.
373, -0. 382 and -0. 415 m. These estimates are lower than the MWL in the observational data by 0. 687, 0. 681 and 0. 693 m, respectively. This gives 0. 69 m as a good estimate of the offset between the reference levels used for the observed Oak Point water level and by the model. Therefore, we subtract 0. 69 m from the time series of the observed water level at Oak Point before applying it as the model's river OBC. One may argue that the reverse approach would also work, i. e. adding 0. 69 m to the sea surface height of the RIOPS solution before providing it to the OBC of BoFSS1/36. However, this implies adding 0. 69 m to the bathymetry of SJAP100, BoF500 and BoFSS1/36, creating a discrepancy with the bathymetry of RIOPS. In effect, this would change the total water volume of the system and hence affect the model's tidal solutions.
Nevertheless, the quality of the simulated tides in the three nested grids represent a significant improvement over RIOPS, which is currently used operationally. Although the errors are slightly larger than those from the barotropic tidal model WebTide, these results are encouraging for the use of a NEMO baroclinic solution for emergency response to spills, where information on stratification will be required. There are a few dozen locations around the Bay of Fundy for which the CHS has historical tidal constituent data (black dots on Fig. 1 ). Historical stations are mostly concentrated in the upper Bay of Fundy and Passamaquoddy Bay. Passamaquoddy Bay opens on the Bay of Fundy through several narrow channels that are poorly resolved even in the BoF500 grid, limiting the representation of the tidal propagation.
Sea surface temperature In situ sea surface temperature (SST) data was taken from Smart Atlantic buoy moored in the approach (see the red star in Fig. 1 ). Daily averages from the buoy and SJAP100 are shown in the bottom row of Fig. 9. SJAP100 reproduces both the seasonal and sub-seasonal variations well, with a correlation of 0. 95. The model has a small warm bias of 0. 45 °C on average, with a standard deviation of the difference of 1. 1 °C. The warm bias is most evident from May to September when there are more high-frequency variations in the time series; in October to January, both time series are less variable and they match very closely. We also compare the SST over the domain of BoF500 to both RIOPS and a 0. 1 degree resolution analysis produced by the CCMEP. The latter is based on an optimal interpolation scheme that assimilates multiple sources of satellite remote sensing and observational data  (Brasnett and Surcel Colan 2016; Brasnett 2008).
Acknowledgements This study is supported by the  Oceans Protection Plan of the Government of Canada. We thank the tremendous support from various levels of management in Fisheries and  Oceans Canada , and  Environment and Climate Change Canada. Model development and evaluation was guided by considerable advice from experienced colleagues, including  David Greenberg ,  Mitchel O'Flaherty-Sproul ,  Susan Haigh ,  François Roy ,  Yongsheng Wu ,  Charles Hannah ,  Phil MacAulay  and  Rachel Horwitz. We greatly acknowledge many colleagues who provided observational data, including Fred Page and  Sarah Jane Scouten  for the collection and preparation of the surface drifter data. Lastly, we would like to thank the two anonymous reviewers for their very constructive comments that helped improved the manuscript.
The laboratory's previous research in the field of BIM focused on semi-automatic methods for indoor segmentation  (Macher et al. , 2017)  and outdoor segmentation  (Boulaassal et al. , 2010). To have a complete visualisation of the building, the indoor and outdoor point clouds must be registered. This is especially true when the outdoor data are produced in another method or at different dates than the indoor data. This is the case when mobile mapping systems are used to acquire all façades of a street and indoor data are acquired with static or mobile handheld systems. Therefore, indoor and outdoor datasets are not automatically registered together. In the absence of (precise) geo-referencing, we do not want to infer errors due to manual registration. Hence, outdoor and indoor point clouds automated registration is also an active research question.
The first part of our paper will sum up a few of our laboratory's previous studies and how they interact with each other as pieces of a larger pipeline. Researches have been conducted for indoor segmentation in floors and rooms, as well as outdoor façade segmentation. In these works, the segmentation process is mostly automated. The second part describes our implementation of the Point Cloud Library (PCL) as proposed by  Rusu and Cousins (2011)  for the detection of keypoints in our datasets. Then we attempt to use the minimal information contained in those keypoints to register indoor point clouds. Furthermore, we assessed the efficiency of this method for the registration of indoor and outdoor point clouds. The third and final section of the paper discusses the results obtained by studying keypoints features obtained on our data set.
Algorithms were tested on data acquired in and around the zoological museum of Strasbourg (France) (Figure  1 ). Indoor data were acquired with static laser scanning technique and outdoor data with a mobile mapping system (Stereopolis from IGN). The museum consists of four floors and a vast attic, with an internal courtyard. The ground floor is composed of classrooms and laboratories. Two floors are dedicated to the visiting tours and are filled with life-sized animal models as well as complete skeletons. The third floor houses the employees' offices. Pieces of furniture obstruct the walls of most rooms. In the attic, old exposition models are stored. All these obstacles that cannot be removed during data acquisition make this dataset all the more pertinent in order to ascertain the robustness of the method.
Image processing was then performed to clean the image of any remaining noise. Then the region growing implementation of MATLAB is applied to identify each white region. An energy function inspired by  (Boykov et al. , 2001 /Wenzhong et al. , 2019)  is applied to differentiate regions resulting from the obstructions and actual openings. The function first eliminates regions that cannot coherently correspond to windows or doors. Then the function analysed the mean repartition of pixels in the bounding box of the region. More rectangular windows and doors will fill almost all pixels of the bounding box, resulting in a higher mean repartition. With the correct thresholds, the energy functions will discard wall obstructions and segment actual openings such as windows and doors.
RESULTS Keypoints were rotated around a random axis with a random angle between 0 and pi/4. Then they were translated along a random axis with a random distance between zero and two meters. As we can see in Figure  8  and 9, the random transformation that we applied to "unregister" cannot be neglected. We wanted the data to appear as if it were manually and roughly registered. But the initial positions are actually much worse than what an operator could achieve. Yet the ICP was very efficient. It can be inferred that it would be equally or more efficient on initially roughly registered point clouds. The mean distances of registered keypoints to their ground truth is less than 20 cm (Figure  8 ). On average for our dataset, keypoints represent 3. 24 % of the original point cloud. This makes the registration step or any step following keypoints detection much faster.
An important question is whether it is possible to quantify the impact of the averaging procedure implicit in proxy records when performing such comparisons. In this paper, we address this question from a theoretical angle by using a conceptual coupled ocean-atmosphere model and investigating its dynamical properties. We apply dynamical systems theory to measure the dimensionality of the system, and compare the results for model output with a high temporal resolution versus a degraded dataset where the system is known only through long-term averages. This allows us to objectively quantify the modifications induced by the averaging. We conclude by applying our approach to a number of instrumental and reconstructed indices of large-scale climate modes and discussing the implications of our results for the general analysis of climate data.
Data In addition to output from an idealised model, we also analyse real-world timeseries at different temporal resolutions. Specifically, we use daily NINO3 data provided by the NOAA climate prediction center  [Barnston and Livezey, 1987; Reynolds et al. , 2007]  over the period 01 Jan 1981 -28 Feb 2018, monthly NINO3 data over 1854 to 2016 provided by  [Huang et al. , 2017]  and a yearly NINO3 dataset over the period 1049-1995 provided by  Mann et al. [2009]. We further analyse daily NAO data provided by the NOAA climate prediction center  [Barnston and Livezey, 1987; Reynolds et al. , 2007]  over the period 01 Jan 1981 -28 Feb 2018, monthly NAO data over 1854 to 2016 provided by  [Jones et al. , 1997]  and yearly data over the period  1049 -1995 [Trouet et al. , 2009]]. -5-Confidential manuscript submitted to Tellus A.
Processing of photographic samples Photographic quadrat samples were analysed using the image segmentation tool provided by the photoQuad software  (Trygonis & Sini, 2012). Image segmentation refers to the automatic partitioning of the source photograph into distinct segments (patches) that share similar colour properties (see also  Teixidó et al. , 2011b). Individual segments were then interactively selected by the user in order to be assigned to specific taxa or any other custom category (Fig. 2 ). Finally, the relative proportion of individual species and substrate categories to the total quadrat area (percent cover) was automatically calculated by the software. Sessile organisms were identified to the lowest possible taxonomic level. Visually similar taxa that could not be identified through photographs were aggregated into a) multi-species groups (as in the case of Crambe crambe/Spirastrella cunctactrix and Filograna implexa/ Salmacina dysteri), b) groups of higher taxonomic levels (e.
PERMDISP is a statistical technique used for comparing the degree of sample dispersion of different groups, based on a distance matrix. When PERMDISP is applied to a Jaccard distance presence/absence matrix, it can be regarded as equivalent to a test for similarity in β-diversity among groups  (Anderson et al. , 2006). For the comparison of α-diversity between sites and localities, a PERMANO-VA (9999 permutations) analysis was conducted based on untransformed data and a Euclidean distance matrix  (Anderson, 2001). The relative contribution of individual species to the total biotic cover per site was visualized through the construction of a pseudo-colour map. All analyses were carried out using PRIMER v6 software with PERMANOVA+ add-on package  (Clark & Gorley, 2006; Anderson et al. , 2008).
, 2009). Moreover, filamentous mucilaginous algal aggregates were observed to periodically flourish at several sampled sites and to fully cover gorgonian colonies. Although no apparent ecological damage was detected, their potentially harmful effects on benthic organisms (mainly caused through starvation or suffocation) may be exacerbated under conditions that enhance their prolonged persistence  (Giuliani et al. , 2005; Schiaparelli et al. , 2007; Piazzi et al. , 2018b). This study represents the first quantitative assessment of the shallow coralligenous assemblages of the N Aegean Sea, and directly addresses the need for baseline data regarding their diversity and conservation status (UNEP-MAP-RAC/SPA, 2008; UNEP-MAP-RAC/SPA, 2015). By focusing on assemblages that are dominated by the yellow gorgonian E.
Acknowledgements This work has been co-financed by the  EU Social Fund and Greek National Funds  through the NSRF 2007-2013 Research Funding Program: Heracleitus II, Investing in knowledge society. Special thanks are due to  Kike Ballesteros  for advice on the identification of macroalgae,  Dimitris Vafidis  for verification of Anthozoa, and  Vasilis Gerovasileiou  for validation of Porifera. We are grateful to  Elena Akritopoulou ,  Vasilis Gerovasileiou ,  Hektor Giourgis ,  Elina Samara , and  Panagiotis Papadelis  for their valuable contribution during fieldwork. Ilias Papadopoulos  and the  Lesvos Scuba, Diving Pelion , and  Dolphin Dive  diving centers are acknowledged for their technical support in the field.
Acknowledgments Experiments were performed on beamline ID24 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( ERC PLANETDIVE  grant agreement  670787 ). Lawrence Livermore National Laboratory  is operated by  Lawrence Livermore National Security, LLC , for the  U. S. Department of Energy, National Nuclear Security Administration , under contract  DE-AC52-07NA27344. The authors are thankful to  E. Edmund ,  Y. Guarnelli , and  A. Polian  (IMPMC) as well as  J. Jacobs  ( SESS-HP Pool, ESRF ) for the support in the DAC preparation. Experimental data are presented in Figures 1-3 and  S3-S5.
Acknowledgements. We thank  Fritz Schlunegger  for his constructive comments at an early stage of this project and  Stefan Schmid  for providing the latest version of the Alpine cross section assembled by his group. We also thank  Mary Ford  and  Christoph von Hagke  for their constructive feedback on a previous version of this paper. Review statement. This paper was edited by  Bernhard Grasemann  and reviewed by  Xiaodong Yang  and one anonymous referee.
After the exponential growth, rates decelerate until the culture enters the stationary phase during which little or no growth occurs. Figure  3. Typical logistic bacterial growth curve depicted as the logarithm of biomass concentration versus time. After an initial lag period, bacterial growth accelerates and reaches an exponential growth phase during which the growth rate is constant. After exponential growth, rates decelerate until the culture enters the stationary phase during which little or no growth occurs. Bacterial growth rates in this study were calculated using the GrowthRates software  (Version 2. 1, December 11, 2015)  developed by Barry G. Hall and others at the Bellingham Research Institute  (Hall et al. , 2014). The growth rate constant µ is calculated only considering the exponential growth phase according to: µ = (!"# ! ! ! !"# ! ! ) (! ! ! ! ! ) (1) where  ! represents the biomass concentration at time.
The dimension of µ is reciprocal time. The results obtained from the GrowthRates program were verified by hand for each experiment. The uncertainty in the retrieved values of µ is given by the standard error of the linear regression of the biomass concentrations considered to calculate the rate. If only two points were used for the calculation, the uncertainties in µ are estimated to be ±20 % based on the associated uncertainties in the biomass concentrations. The bacterial growth behavior in the stationary phase was quantified by linear regression and is presented as the stationary phase growth rate, µ !"#".
Acknowledgements This research was supported by the  Marie Curie   EU-FP7 CO2-REACT Research  and  Training Network. OP acknowledges partial support from  RFBR  projects Nos 19- 55-15002  and  17-05-00348. The authors would like to thank  Liudmila Shirokova ,  Carole Causserand ,  Aurélie Lanzanova ,  Frédéric Candaudap  and  Michel Thibaut  from the  Géosciences Environnement Toulouse  for their help with experimental work, wet chemical analyses, and XRD-measurements, respectively. Stefanie Lutz ,  Maren Kahl ,  Martin Fuller  and  Richard Walshaw  from the  School of Earth and Environment, University of Leeds  are acknowledged for their help with SEM investigations. Eydis Eiriksdottir ,  Deirdre Clark ,  Rebecca Neely  and  Sigurdur Gislason  from the  Earth Science Department of the University of Iceland  are acknowledged for the provision of suspended particulate material and for support during field work.
The cross-correlation coefficient between a pair of aerosol fields quantifies the degree of correlation-as long as this coefficient is not zero, the aerosol variables are not independent of each other, i. e. , they are correlated to some extent. However, development of a sophisticated physical model to parameterize these correlations is intractable due to the complexity of the real physical world. On the other hand, empirical methods, such as principal component analysis (PCA) or empirical orthogonal function (EOF) analysis, can be used to capture phenomenological correlations between aerosol fields. By implementing PCA over a training dataset, the original fields can be transformed to superpositions of a set of mutually orthogonal vectors, or principal components (PCs). Then, the original aerosol fields can be approximately reconstructed using a linear combination of a certain number of PCs, with accuracy dependent on the number of PCs employed.