dataesr/scientific-paragraphs-categorization huggingface dataset:
This is most likely related to postglacial colonisation in the more northern locations, while for the southern sites an ephemeral nature of local populations may be the cause of this. For the Celtic Sea sample of B. minimus, the significant SSD value may result from mild admixture effects, or perhaps a lingering effect due to refuge populations during the last glacial maximum. However, the EBSP analysis was consistent with population expansion and therefore no conclusions can be drawn on this. Using the complete dataset, i. e. , including the long branches we found, recent population decline of G. choledochus was not supported. Excluding the long branches from these analyses yielded the same conclusion of population expansion not being supported (Fig. S1 ; Table  5 ). On the basis of the nuclear sequences, we may tentatively conclude that the long branches found for this species are The Bayesian Skyline Plot for B.
The Arctic Ocean is one of the fastest changing oceans, plays an important role in global carbon cycling and yet is a particularly challenging ocean to study. Hence, observations tend to be relatively sparse in both space and time. How the Arctic functions, geophysically, but also ecologically, can have significant consequences for the internal cycling of carbon, and subsequently influence carbon export, atmospheric CO 2 uptake and food chain productivity. Here we assess the major carbon pools and associated processes, specifically summarizing the current knowledge of each of these processes in terms of data availability and ranges of rates and values for four geophysical Arctic Ocean domains originally described by  Carmack & Wassmann (2006) : inflow shelves, which are Pacific-influenced and Atlantic-influenced; interior, river-influenced shelves; and central basins.
2004 ) but to date there has not been any attempts to include representations of the coloured fraction (CDOM). Schwarz et al. (2002)  have shown that to adequately represent this component in a model, it would require incorporation as a state variable. CDOM and the associated photochemical processes are highlighted here as important for carbon cycling into the microbial and autotrophic components of the food web, particularly in the RiS, but it is not clear that sufficient data exist to support development of such a component at this time. To realistically represent the processes associated with the microbial food web, biogeochemical models need explicitly to represent the bacterial loop processes, including F B,P and F B,P , as well as the exudation of DOC from autotrophs and heterotrophs.
These internal processes have been highlighted here as potentially important for the contemporary CB and the RiS. While these important bacterial processes have been explicitly represented in some marine ecosystem models  (Lancelot et al. 2000) , to date these processes have been included in Arctic ecosystem models only through implicit representation of bacteria. Explicit representations of flocculation (F FL ) and photodegradation (F PC ) dynamics are also not common. Again, realistic representations of these processes are hindered by lack of a clear understanding of the mechanistic processes and a shortage of data for model formulations and parameterization. Furthermore, the fluvial input of carbon into RiS can play an important role in carbon cycling. However, it remains common for simulated river inputs to carry no dissolved tracers, only freshwater, to the model  (Long et al.
2003). This problem remains a particularly big challenge when looking to model the Arctic marine carbon cycle because of the lack of pan-Arctic observational data  (Gerdes et al. 2009). Ice and cloud problems make satellite chlorophyll a observations very limited and sampling of other ecosystem components and processes generally requires in situ experiments, which are expensive and logistically challenging in this region. These limitations make for poorly spatially-and temporally-resolved data for model tuning and validation. Lower cost, user-friendly analytical systems are needed to enable temporal and spatial coverage of data relevant to understanding carbon cycling. Thoughtful model development, in concert with measurements and experimental efforts on the underrepresented components of the Arctic marine carbon cycle will result in more robust models with data assimilative capabilities and more reliant predictive capabilities.
Acknowledgements This paper resulted from a  joint Arctic in Rapid TransitionÁ Association for Polar Early Career Scientists  workshop supported by the  International Arctic Science Committee , the  Prince Albert II of Monaco Foundation , the  Polish Academy of Sciences  and the  Institute of Oceanology , the  Polish Scientific Committee on Oceanic Research , the  International Arctic Research Center , the  National Center for Scientific Research*Laval Takuvik Joint Laboratory , the  Alfred Wegener Institute for Polar and Marine Research and the Department of Fisheries and Oceans Canada. Popova E. E. , Yool A. , Aksenov Y. , Coward A. C. & Anderson T. R. 2014. Regional variability of acidification in the Arctic: a sea of contrasts. Biogeosciences 11, 293Á308. Popova E. E. , Yool A. , Coward A.
Kroon et al. (2011)  validated the retrieved ozone profiles with several satellite products and balloon-borne ozonesondes. As the summary in Table  1  shows, OMO3PR retrievals were in agreement with the Microwave Limb Sounder (MLS;  Waters et al. , 2006)  retrievals within ±10 % except for the Polar regions during the ozone hole seasons where differences up to ±30 % were found. In addition, the comparisons with MLS and other correlative data sets showed that the biases vary with altitude. Furthermore, they found that OMI overestimates tropospheric ozone abundances from 0 to 30 %, when compared with MLS, the Tropospheric Emission Spectrometer (TES;  Beer et al. , 2001)  and ozonesondes. In this work, we studied how different assumptions in the OMO3PR retrieval affect the results and searched for possibilities to improve the accuracy of the retrieval in the troposphere.
(2008). In their work,  Eremenko et al. (2008)  used ozone profiles retrieved with an analytical altitude-dependent regularization method from the Infrared Atmospheric Sounding Interferometer (IASI;  Clerbaux et al. , 2009)  data. IASI ozone profiles have been thoroughly validated for example by  Keim et al. (2009)  and  Dufour et al. (2012). In the second case study, we compared the operational and modified ozone profiles against ozonesonde data from North America for August-September 2006. The ozonesonde data were downloaded from the World Ozone and Ultraviolet Radiation Data Center (WOUDC, www. woudc. org). The main goal of this study is to examine if an UV instrument such as OMI could be used to retrieve tropospheric ozone abundance which has a significant effect on air quality.
Surface albedo parameterization As mentioned in Sect. 2, the operational version of the retrieval algorithm uses second-order polynomials to describe the dependency of surface albedo on wavelength at both UV1 and UV2 channels separately. To study the sensitivity of the retrieval to this assumption we varied the wavelength dependencies of the albedos. We tested constant, first-order (linear) and second-order polynomials for both channels separately. We processed all the pixels from one orbit (06704, 1496 × 30 pixels) and studied how the ozone profiles change when compared to the operational product on average and in six different latitude bands. In the comparison, we concentrated on tropospheric features. To our knowledge, there is no applicable surface albedo database for these wavelengths which could be used to select the most physical representation for the wavelength dependency.
The squared structures in Fig. 8  originate from the calculated tropopause heights. The heights are calculated with 1 km vertical resolution from   4. ECMWF data which have a spatial resolution of 1 × 1 •. The coarse vertical resolution combined with the spatial resolution of the ECMWF data causes large areas to have the same tropopause height and the transition from one a priori profile to another can cause artificial structures that are visible in the difference plots. To check how dependent the retrieved tropospheric ozone abundances are on the selection of the tropopause height we studied the averaged tropospheric ozone values as a function of tropopause height from the operational, modified (Trop_alb_covar_10) and IASI retrievals (not shown). The variability of ozone within 1 km height bins was large while the differences between the heights were smaller.
However, the photochemical production of ozone that causes the diurnal cycle takes place near the ground level and neither OMI nor IASI are sensitive there. Regarding the sensitivity of the instruments, the altitudes of maximum sensitivity of the OMI retrievals are always at the highest layer in the troposphere, while for the IASI retrievals the altitudes vary between 1 and 7 km. Comparison of a priori ozone abundances in the troposphere (see Fig. S5 ) shows that the differences between the a priori values of the OMI retrievals are much smaller than between the retrieved ozone values (Fig. 9d ). Moreover, as Daily data averaged on 1 × 1 grid. Statistics of the differences are given in Table  4. the upper panels in Fig. 10  show, the IASI a priori ozone values are significantly lower than the ones used with the OMI retrievals.
Secondly, there are fundamental differences in the IASI and sonde data sets. Sonde data are based on in situ measurements while the IASI data set is more similar with the OMI retrievals. Thus, not surprisingly, the evaluation results are highly dependent on the data set used in the evaluation. We also did the scatter plot comparison using averaging kernel convoluted sonde data (see Fig. S6 ). This comparison produced clearly better statistics (R 2 , offset, slope) for the operational retrieval than for the modified retrieval. However, this does not mean that the modified retrieval is not as good as the operational retrieval. This just shows that the operational retrieval is not sensitive in the troposphere and it mainly uses a priori information there. The averaging kernel convolution rejects the sounding information for which the satellite measurement is not sensitive and replaces it with the a priori information.
Acknowledgements. The  Dutch-Finnish built Ozone Monitoring Instrument (OMI)  is part of the  National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Aura satellite payload. The  OMI  project is managed by the  Netherlands Space Office (NSO)  and the  Royal Netherlands Meteorological Institute (KNMI). OMI vertical ozone profile data were processed at and obtained from the  NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). We thank the  World Ozone and Ultraviolet Data Center (WOUDC)  for making the routine ozonesonde measurements accessible. We also thank the associate editor,  T. von Clarmann  and the anonymous referees whose thoughtful comments helped to improve this paper significantly. Edited by: T. von Clarmann.
With the variety of measurement techniques available on the market today, fusing multi-source complementary information into one dataset is a matter of great interest. Target-based, point-based and feature-based methods are some of the approaches used to place data in a common reference frame by estimating its corresponding transformation parameters. This paper proposes a new linear feature-based method to perform accurate registration of point clouds, either in 2D or 3D. A two-step fast algorithm called Robust Line Matching and Registration (RLMR), which combines coarse and fine registration, was developed. The initial estimate is found from a triplet of conjugate line pairs, selected by a RANSAC algorithm. Then, this transformation is refined using an iterative optimization algorithm. Conjugates of linear features are identified with respect to a similarity metric representing a line-to-line distance.
In the case of airborne (ALS) or ground-based (MLS) mobile scanning platforms, offsets between overlapping areas of adjacent flying strips or surveys resulting from drifts of the INS or temporary loss of GNSS signals may occur. Finally, the wide variety of available measurement techniques provides different sources of 3D information requiring integration into one coherent dataset. Both rigid and non-rigid alignment methods exist. The classification of registration approaches in  [1]  divides algorithms into three main families. The first are multi-feature-based methods relying on the extraction of primitives, which achieve the alignment through prior correspondence of geometric features. As such, these algorithms depend on the existence of suitable primitives within the scene. Moreover, the data filtering and segmentation steps can take considerable amounts of processing time.
The pair-wise registration of datasets that are already geo-referenced and do not exhibit large changes, especially in rotation is thus focused. Indeed, a good a priori alignment between the point clouds should be assumed, meaning that it needs to be only refined by the co-registration. The objective is to develop an effective registration procedure for the co-alignment of the Mobile Laser Scanning (MLS) point cloud data. Therefore, a complete framework for the so-called Robust Line Matching and Registration (RLMR) is outlined. The remainder of the paper is organized as follows: after a short presentation of the existing optimization methods suitable to estimate the transformation parameters from line segment correspondences, primitives, which can be reliably extracted from the MLS data, are introduced.
Methodology The proposed two-stage procedure RLMR consisting of a rough followed by a fine alignment phases handles the registration (Figure  1 ). Assuming that there is no scale difference between the two overlapping point clouds, the transformation from the Data set (X) to the Model set (M) requires the estimation of three rotation angles and three translations. For this task one could implement one of the approaches from the state-of-the-art. The correspondences between two sets of lines are created twice with the algorithm described below. This correspondence-finding algorithm is an extension of the work presented in  [20]  by providing more details and presenting new results. An analysis of a line-to-line distance (score) leads to identification of conjugate linear features between two sets.
This score is composed of angular deviation and spatial separation values. First, a one-to-one correspondence Aini (approximate matching), which is used to find the initial guesses of the unknown transformation parameters should be made. Then, all newly matched line segments (within an explicit matching Af which allows both one-to-many and one-to-null relations between lines) are used to refine the transformation. Using the incorporated registration primitives, an iterative robust approach is proposed in  [17]  to eliminate the need for an initial estimate during the estimation process. Knowing that the FMFI algorithm may fail if in a pair of lines the shorter segment is not fully contained within the longer one, the FMII2 approach is implemented. In this case, linear features in the Data set are infinite while those in the Model set are finite.
Thus, the FMII algorithm has been slightly modified in order to estimate the rigid transformation. The FMII2 name is used to distinguish the procedure from its original variant. The transformation parameters for both registration stages are obtained from the FMII2 algorithm. To overcome the problem that Aini may contain erroneous pairs resulting from the distance between Data and Model, only three pairs of matched lines selected from Aini are considered when computing the initial transformation. The coarse registration is carried out using an efficient RANSAC algorithm coupled with FMII2 in order to provide the best available triplet of pairs within Aini. The resulting roughly-aligned datasets permit an explicitly matching Af due to their mutual displacement being substantially reduced. The rough alignment step is paramount because the correspondence-finding procedure is sensitive to distance, or conversely having the two sets close improves matching.
Line Matching The determination of correspondences is typically a challenging aspect of the overall registration process, which directly affects the quality of the estimated transformation parameters and has the potential to cause divergence of the optimization algorithm. To explain the conceptual basis of our correspondence-finding procedure, suppose the set Data = {t1,t2,…,ti,…,tp} need to be transformed so as to obtain the best alignment with the other set Model = {m1,m2,…,mj,…,mq}. Formally, a set is denoted by an uppercase letter and an individual element by a lowercase letter. The identification of conjugate lines involves assigning a dissimilarity measure d(t,m) based on geometric characteristics such as magnitude, orientation and location. This individual score, calculated for the total number of line pair combinations, is used to build a similarity matrix.
Similarity Matrix The main step of our correspondence-finding algorithm is to build a similarity matrix of dimensions p by q resulting from the cardinality of the Data and Model sets. Thus, the total number of possible matching line pairs will equal the number of lines in the first set (Data) multiplied by the size of second set (Model). Note that in this similarity matrix every column corresponds to a line from the Model set and every row corresponds to a line from the Data set. Each entry of the matrix, referred to as the score d(t,m) for a possible pairing of lines, is calculated according to Equation (1). This metric is inspired by the approach of  [23]  used to compare two sets of 2D edges, and adapted in  [20]  to assess the quality of 3D point clouds by means of linear features: d t , m = W • d t , m + d t , m + d ⟘ t , m (1) The angular deviation d α (t,m) and the two spatial displacements in the form of an overlapping indicator d II (t,m) and a perpendicular distance d ⟘ (t,m) are considered to measure the similarity d(t,m) between any two line segments.
Computing the Hausdorff Distance between Two Sets of Line Segments-Quality Indicator For the purpose of assessment, a quality measure, which describes the convergence between two sets of conjugate lines after the registration process, is defined. The metric used is based on the Hausdorff distance, which was originally proposed to measure the proximity of two sets of points or alternatively the similarity of shapes. Given two datasets Data and Model as introduced earlier, the classical symmetrical Hausdorff Distance (HD) is defined as  [24] : HD = max h(Data, Model), h(Model, Data) (6) The two components of the Hausdorff distance h(Data,Model) and h(Model,Data), also called directed distances, may have different values but use the same method of calculation: h(Data, Model) = max ∈ min ∈ d(t, m) (7) where d(t,m) denotes some underlying metric, most often the Euclidean distance.
Coarse-to-Fine Registration A coarse-to-fine point cloud registration method based on the optimization algorithm FMII2 is developed in this paper to achieve robust alignment using extracted line segments. An initial search for a 3D rotation matrix (R) and translation vector (T) is applied at the beginning to improve matching accuracy. This coarse registration process is carried out in three steps: (1) Creating preliminary line matching Aini between Data and Model sets; (2) Applying the RANSAC algorithm to the candidate correspondences Aini to select three inlier pairs for input into the FMII2 algorithm (this step is mandatory because several pairs obtained previously may be incorrect); (3) Estimating the transformation parameters. Following a rough alignment of the line segments, the final transformation parameters Ƭ = R , T are calculated for the newly matched line segments Af, and used to refine the overall transformation (fine alignment).
The impact of noise levels on the distance has been tested by analyzing the LHD value between two datasets, the first one containing noise-free 3D line segments, the other containing the same elements corrupted by noise. Specifically, Gaussian noise with mean zero and standard deviation σ is added to the coordinates of each line segment endpoint. The relationships between the noise level and the computed distance are shown in Figure  9. The σ is directly linearly proportional to OLHD (OLHD ∝ σ) as: (i, j) ∈ A : ∀ t ∈ X, m ∈ M , d(t , m ) ≤ γ = N (20) γ = 5. 8σ ± 0. 17 (21) Thus, if the Hausdorff distance between two sets of lines is known, the standard deviation of noise σ associated to endpoints of segments can be estimated and vice versa. A pseudo-code of our Matlab implementation is presented below in Procedure Line-based Registration.
Synthetic Data The synthetic data consists of two datasets, each of which contains 64 linear features (Figure  10 ). The Data set is obtained by transforming the Model set using a rigid motion, setting the rotation (R) as Euler angles (1. 0°; -1. 0°; 1. 0°) and the translation (T) to (-1. 0 m; 0. 5 m; 1. 0 m). Then, an additive white Gaussian noise with zero mean and isotropic standard deviation σ is added to the 3D coordinates of endpoints of the Model set. The noise level is successively increased from σ = 0. 00 m to σ = 0. 05 m with a step interval of 0. 001 m. In this way, 50 noisy Data sets from a given Model set are generated. The computed Hausdorff distance (LHD) between corresponding Data and Model line segments equals to 2. 892 m.
A significant improvement in the quality of correspondence identification is observed. The assumption of lines being initially matched in at most one-to-one fashion appears to be necessary. Thus, the generation of a large amount of false positive results (FP) can be avoided at the expense of increasing the false negative (FN) detections. Seeing as the RANSAC algorithm is time-consuming in practice, this step reduces the number of iterations needed to find an optimal triad of pairs for coarse registration. Also, by decreasing the mutual distance between two datasets, the proposed correspondence-finding algorithm can ensure an exact matching. Summarizing, the FMII2 optimization algorithm is preferable, especially when the linear features have significant associated uncertainty. Whenever the line correspondences are unknown, the RLMR procedure ensures a precise matching.
The specificity shows how good the matching algorithm is at identifying and rejecting non-existing pairs (TN). In our case there is on average a 99. 7% chance that the candidate pair will be correctly classified as negative. The accuracy is also quite high, with none of the values lower than 99. 5%. Accuracy describes the rate of the correspondence-finding algorithm obtaining true results, either TP or TN. Consequently, the proposed procedure provides an efficient and robust way to match two line sets. The verification of convergence between registered sets of linear features is performed using the LHD. The final distance values after both rough and fine alignments are provided in Table  3 , which depicts a progressive reduction of the spatial spacing between the processed datasets. Besides, the estimated transformation parameters (the rotations ω, φ, κ and translations Tx, Ty, Tz applied respectively to the X-axis, Y-axis, and Z-axis), as well as the CPU processing time needed by our Matlab implementation of the RLMR approach (the edge-detection method processing time is omitted) are included in Table  3.