dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Three main contributions of this paper are highlighted. First, a discussion of several approaches to linear feature-based registration. Second, a new correspondence-finding algorithm to match conjugate line segments, accompanied by a quality indicator which allows measuring the proximity of two line sets. Finally, the RLMR framework for performing automated pair-wise registration of low-density point clouds. The linear feature extraction approach considers only crease edges. The proposed RLMR procedure consists of two steps: coarse and fine alignments. The coarse registration reduces the spacing between two datasets so as to improve the results of the subsequent matching process. In order to obtain a good initial transformation guess only a triplet of matched line pairs is considered.
For the purpose of rejection of outlier pairs, the well-known RANSAC algorithm is used. Given two roughly registered datasets, their alignment is refined through the FMII2, which employs the full set of matched lines. A novel solution for the problem of searching for conjugate matching lines was also developed. The proposed correspondence-finding algorithm exploits the positions and orientations of linear features to deduce their similarity and then pick the "same" lines within two sets. This approach may also be applied to match two sets of 2D line segments. The application of the RLMR procedure on experimental datasets yields reliably satisfying transformation results. This framework is robust to noise and efficient if one seeks to register two initially pre-aligned point clouds at the same scale.
The line extraction errors that generally comprise of laser measurement noise, occlusions, performance of the prior plane-based segmentation (over-segmentation and under-segmentation errors), do not affect the registration outcome significantly. It is due to the matching algorithm that provides a correct pairing, even if there are several linear features in one set corresponding to only one element in the other set. The quality of matching obtained in comparison tests on both real and synthetic datasets is very encouraging, provided that assumed proximity of datasets holds. This assumption is met through the coarse registration, which can considerably minimize the line matching errors as demonstrated in the result section. Thus, the rate of the proposed correspondence-finding algorithm obtaining true results is quite high, with an average value of 99.
A salient result of this study is that κ is a good predictor for the non-local rheology observed in the first phase. However, in the second phase, where we identified smaller but systematic non-zero values for κ, the non-locality does not manifest itself in the μ(I) rheology. This observation points on the possibility of a relevant hidden variable accounting for non-locality. This variable would be similar to the fluidity parameter as defined by Bouzid et al. [16] , but would relax faster to reach zero in the second phase. This research is supported by Fondecyt Grant No. 1140778 and Ecos-Conicyt Grant C11E04. E. R. acknowledges the support of a Becas CONICYT. B. A. is supported by Institut Universitaire de France. This work is funded by the ANR JamVibe and a CNES research grant.
1 model. In this paper, we first describe the datasets and basic processing (Sect. 2), then assess whether the results support the existence of distinct EN regimes (Sect. 3. 1), analyze the roles of oceanic (3. 2) and atmospheric (3. 3) nonlinearities, and analyze the temporal evolution and predictor variables  (3. 4). Finally, we discuss some implications of the results (Sect. 4) and then summarize the main conclusions (Sect. 5).
Observational data The monthly HadISST v1. 1 dataset (1870-2010)  (Rayner et al. 2003 ) is used for calculating SST anomalies and the E and C indices, as in Takahashi et al. (2011). For this, we consider the first two normalized principal components (PC) of the equatorial Pacific (10 • S -10 • N) SST anoma- lies and calculate the indices as E = (PC1 -PC2)/ √ 2 and C = (PC1 + PC2)/ √ 2. These indices are uncorrelated by construction and describe the SST variability in the eastern and central equatorial Pacific, respectively (Fig. 1a, c ). Daily surface (4 m) wind observations from the TAO/ TRITON buoy array  (McPhaden et al. 1998 ) for 1993-2013, when temporal and spatial sampling is approximately homogeneous, are used to calculate monthly pseudo-stress anomalies from the climatology for the same period and wind stress is estimated using a neutral drag coefficient of 1.
2 × 10 -3 (e. g. Harrison 1989 ). We also use the ship-based monthly wind stress anomalies from WAS-Wind v. 1. 0 (Tokinaga and Xie 2011)  for the  1950-2010 period (base period 1980-2009) , which has best continuity along ship tracks. To represent atmospheric deep convection, we use the monthly NOAA interpolated outgoing longwave radiation (OLR) anomalies for 1974-2013 based on the 1980-2009 climatology  (Liebmann and Smith 1996). Subsurface ocean temperatures were obtained from the SODA 1. 4. 2 reanalysis  (Carton and Giese 2008)  for the 1958-2001 period. This dataset is based on the assimilation of ocean temperature and salinity measurements into a global ocean GCM forced by observationally-based surface fluxes. Monthly anomalies were calculated based on the  1979 -2009 period (1979 -2001 for SODA data) for SODA data).
1 the threshold is also close to zero, but the nonlinearity ratio is much higher (5. 7 and 5. 5 for precipitation and wind stress, respectively) than in observations. In order to understand better the east-west differences in the nonlinearity, as well as depicting their nonlocal character, we repeat a similar calculation but for the target variables at different longitudes, instead of a single box. In this case, we use observations of surface winds from the TAO/ TRITON array data that has a shorter record (does not include the 1982-1983 EN) but has more homogeneous temporal and spatial sampling distribution than the shipbased WASWind. The results for the observations are consistent with the previous analysis, but now we can see that the degree of nonlinearity in the response varies longitudinally (Fig.
In particular, the details of Kelvin wave propagation and the reflection of Rossby waves from the eastern boundary are apparently highly influential. Thus, although it has been previously parametrized in simple models of the recharge-discharge family  (Jin et al. 2003; Timmermann et al. 2003; An and Jin 2004) , we believe that the neglect of wave dynamics could qualitatively affect the results (e. g. Su et al. 2010). A practical issue with assessing the role of nonlinear ocean advection is the limitations in the observational data. Although the ocean analyses provide comprehensive datasets that are heavily used almost as equivalent to observations, it has to be kept in mind that these can be strongly influenced by the numerical models used in data sparse regions, such as the far eastern equatorial Pacific, and for weakly constrained variables such as vertical motions associated with wave dynamics  (Su et al.
Experimental infection with OsHV-1 under controlled conditions The susceptibility of 44 FSF to OsHV-1 infections was tested through experimental infection under controlled conditions in October-November 2011 when the oysters were 7 months old. The average mean individual weight was approximately 5 g. The intramuscular route was selected for infectious challenge using a protocol already described  [26, 27]. For each family, 40 oysters were anaesthetized for 4 h in a solution containing magnesium chloride (MgCl 2 , 50 g/L) in seawater (1 v)/ tap water (4 v)  [28]. One hundred microliters of an OsHV-1 suspension (OsHV-1 μVar genotype, GenBank HQ842610-1) at 6 x 10 3 copies of viral DNA/μL were injected into the adductor muscle. Each family was then placed in four tanks (10 oysters per tank) containing 5 L of UV-treated and filtered seawater (1 μm) at 22°C with gentle aeration, without a food supply and without flow-through for 5 days.
Statistical analyses The studied trait was binary (dead = 0 and live = 1) for both field monitoring and experimental infection, and analyses were based on mortality at the endpoint for each method. The genetic analyses were performed with a generalized linear mixed model (GLMM) using the SAS Glimmix procedure with a logit link using the Laplace method to approximate the log likelihood  [31]. The data used and the SAS code are provided in S1 Logit ðY ijkl Þ ¼ m i þ m j þ m kðjÞ þ m i x m j þ m i x m kðjÞ where Y ijkl = observed mortality at the endpoint for the oyster l in the family, bred using the female k (1 to 48) nested within the male j (1 to 24) in method i (BF, LF MF and OsHV-1 challenge), μ i = fixed effects of the testing method, μ j = random effects of males, μ k(j) = random effects of female nested within the male μ i x μ j = random interactions between males and methods μ i x μ k(j) = random interactions between females nested within the male and methods The models used for each method were: For BF and OsHV-1challenge: Logit (Y jkln ) = μ j + μ k(j) + μ n(kj) For MF and LF: Logit (Y jkln ) = μ j + μ k(j) + μ n where the parameters are defined above and μ n(kj) = random effects of replicate nested within female and male (n = 2 for BF, and n = 4 for OsHV-1 challenge) and μ n = fixed effects of the replicate (n = 3).
Additionally, heritabilities for mortality were calculated with a mixed model using the SAS Mixed procedure for binary data. Variance components and heritability were obtained on the observed scale, and the latter was then converted to the underlying scale  [33]. The SAS code is given in S1 Text and the results are presented in S2 Table. Genetic correlations for mortality between methods were also calculated from the estimates of breeding values (EBV) using best linear unbiased prediction (BLUP) analysis and the results are presented in S3 Table.
Acknowledgments We wish to thank the hatchery, nursery and genetic teams of the  Laboratory of Genetics and Pathology ,  Ifremer La Tremblade  and  Ifremer Bouin  for their assistance in oyster production, in particular,  Philippe Haffner ,  Delphine Tourbiez ,  Max Nourry  and  Christian Pénisson. We also thank  Jean Luc Seugnet  for his help in field operations, and  Elise Maurouard  and  Caroline Yonneau  for labelling the oysters and their precious contributions to the field trials. Similarly, we also thank the pathology team for technical support for the challenge in laboratory conditions. We also acknowledge  Florence Rivet  for her help with bibliographic research.
> DATA COLLECTION The study was based on invertebrate samples collected with artificial substrates during a period of 32 years. The sampling design included over 1500 samples collected four times per year (i. e. one campaign per season) and per station. This method consisted in introducing an "empty" habitat into the environment for it to be be colonized by the local invertebrate fauna (see  Khalaf and Tachet, 1980). The composition of the artificial substrates had to be as close as possible to the substrate types naturally present in the sampled environment. If the method did not allow a direct evaluation of macroinvertebrate densities per surface unit (the artificial substrate itself represented the "catch per unit effort", CPUE), its robustness was ensured by both the high reproducibility of the results and the high number of samples performed during the study period.
Two types of artificial substrates were used during this study: (i) CTGREF-type substrates composed of a stack of parallel cement disks with synthetic brushes (Bugey, Cruas and Tricastin) and (ii) IBGA-type substrates originally developed for the monitoring of the site of Saint-Alban, and now used for the whole monitoring network of the French large and deep rivers by Water Agencies, and composed of sisal string and stones. These data have been archived in a database with Access software. Parallel to biological data, water quality was evaluated each year in four seasonal sampling campaigns, in particular the parameters describing organic matter (O 2 content and BOD 5 ) and nutrients (nitrogen and phosphorus). Water temperature and Rhône discharge were also evaluated at each site. Water temperature was recorded hourly by automatic recording stations upstream from the NPP.
Taxonomy-based approach Taxa with only one occurrence or less than five individuals over the whole sampling design were removed from the analysis to avoid giving excessive weight to rare taxa. As a result, only 222 taxa were included in the analyses. Individuals were identified at the species level whenever it was possible. Only the genus level was identified for several invertebrate samples from Cruas and Tricastin. The Diptera were identified only at the family or subfamily level. Abundances of taxa were log-transformed (x i replaced by log (x i + 1), with x i being the abundance of taxon "i") so as to reduce differences in the weight of abundant vs. rare taxa. A Correspondence analysis (CA) was applied to (i) the whole data array, and (ii) the array including the mean composition of station assemblages per year.
ACKNOWLEDGEMENTS This work was supported by  Electricité de France (EDF)  in the framework of the research programme called " Étude Thermique Rhône Phase 4 " ( ETRP4 ). Financial support was also provided by the  French Water Agency Rhône Méditerrannée & Corse and the Council of Europe. Data were taken from monitoring programmes carried out each year in the vicinity of EDF nuclear plants located along the Rhône River. Many thanks to two anonymous reviewers whose comments and suggestions have greatly improved the early version of the draft, and to  Professor Eric Pattee  for english corrections.
AATSR ORAC The Oxford-RAL Retrieval of Aerosol and Cloud (ORAC V1) algorithm is an optimal estimation (OE) retrieval scheme designed to provide estimates of aerosol optical depth and effective radius, cloud top pressure, height and temperature, cloud particle effective radius, cloud optical depth and cloud type (generally liquid water or ice) from multispectral imagery  (Thomas et al. 2009 , Poulsen et al. , 2011; Sayer et al. , 2011). The method fits all the shortwave forward and nadir radiances simultaneous using a forward model based on the DISORT radiadive transfer code  (Stamnes et al. 1998). It is worth noting that the simultaneous retrieval of all state parameters provided by the OE method ensures that a physically consistent and numerically optimal estimate of the state is produced.
The quality of fit to the radiances allows the quality of the retrieval to be judged a posteriori. In addition the error in the retrieved aerosol parameters is estimated by propagating both the measurement and forward model uncertainties into state space. Note that the dataset described here was produced by the ORAC V1 algorithm an a priori surface BRDF is set using MODIS MCD43B BRDF products  (Jin et al. , 2003)  over land and an ocean surface reflectance model over the ocean  (Sayer et al. , 2010). More recent processing with an updated surface model is currently under evaluation but initial indications show a substantial improvement when compared to V1.
In step 2 the parameters retrieved in the first step are used to simulate spectra for the same set of 36 different aerosol mixtures with the same radiative transfer code after spatial integration to the larger pixels of the spectrometer. A least square fit of these calculated spectra at 10 wavelengths to the measured spectrum delivers the correct AOD value (the one AOD retrieved in step 1 for the aerosol type selected in step 2) and -if a uniqueness test is passed -the most plausible spectrum and its underlying aerosol mixture. (Holzer-Popp et al. 2002a; 2008). Using a combination of 2 instruments with different scan patterns SYNAER can only provide global cloudfree coverage every 12 days and with large pixels of 60x30 km 2. However the combination of the 2 instruments has the potential to provide aerosol type information (qualitatively shown in  Holzer-Popp et al.
Validation and evaluation For validation of the retrieval algorithms used in Aerosol-cci, to evaluate their improvement, to select the most suitable algorithm for ECV production, and to assess the achievements as regards meeting user requirements, independent and objective methods are needed leading to quantitative scores. These scores are obtained by comparison with independent data sets, in this case these are provided by the ground-based sun photometer network AERONET  (Holben et al. , 1998)  as described in the introduction. All satellite results, both those participating in the Aerosol-cci RR and the reference satellite data sets, are evaluated versus AERONET. Three principal methods are used based on statistics for Level 2 (L2) and Level 3 (L3) products. In Aerosol-cci L2 products are the daily products as produced by the retrieval with a spatial resolution of 10x10km 2 and L3 are daily or monthly aggregates (also referred to as mean or averaged data) provided on a spatial scale of 1 o x1 o.
Zieger et al. , 2011). These exercises were not part of the current RR and will not be reported here. For the intercomparison of Aerosol-cci data sets, and for the comparison of Aerosol-cci data sets to other data sets in the ICARE archive (e. g. , MODIS, model results, etc. ), a multi-sensor visualization and analysis tool has been developed. All key parameters of each sensor product can be selected independently for visualization. For each product, a link to the product documentation is provided. For a given parameter, a unique colour scale is used for direct visual inter-.
L2 statistics AOD and Ångström exponent of L2 data sets were compared with AERONET data using scatter plots and least squares fits to the data. The comparisons were made for collocated satellite and AERONET observations, i. e. satellite pixels were selected within a spatial threshold of +/-35 km and a time frame of +/-30 minutes from AERONET measurements. Where available (ORAC, ADV and SYNAER), quality flags or confidence indicators in the products were taken into account to select best pixels. Furthermore a distinction was made between retrievals over land and water. Round Robin MERIS datasets do not have a water/land flag, therefore the pixels over land and ocean for MERIS Standard and MERIS BAER were selected using the ORAC water/land flag.
L3 scoring For L3 scoring, an evaluation routine has been developed to determine for a test data set a performance error, for cases when trusted reference data are available. Here the test data are daily L3 satellite data, the reference data are AERONET observations within half an hour of the particular satellite overpass. To simplify comparisons, all sun photometer data were gridded on the spatial 1 o x1 o resolution of the satellite data. Although in theory satellites should locally offer more than 100 samples for the four months, the available number of valid data points is smaller due to the presence of clouds. The number of samples is further reduced due to a limited swath (e. g. AATSR and MISR), stringent quality control measures (e. g. SU) or due to limited temporal coverage (e. g.
Each of the three sub-scores is based on statistics. Hereby, valid sub-scores require a minimum number of samples. Given sufficient data-pairs for test-data D and reference-data R, the bias score E B compares sums of associated (value-) ranks of an array that contains all elements from both D and R. If the rank sum associated with elements of D (D SUM ) is similar to the rank sum associated with elements of R (R SUM ), no bias is determined. However, if the two rank-sums differ, then a bias is identified, in strength and sign. E B = w* [(D SUM -R SUM ) / (D SUM +R SUM )], w=[ IQ-R D+ IQ-R R]/[ IQ-A D+ IQ_A R] (3) Based on the average interquartile range (IQ-R) to interquartile average (IQ-A) ratio of both datasets, a variability factor w is defined. The factor w is applied as weight to the bias error, to avoid an error overemphasis, in the case that all individual values are close to their average.
In step 1, temporal error and bias error are determined at each location, by applying available time-series data pairs at the smallest temporal resolution. In step 2, spatial error and bias error are determined by exploring data pairs in their spatial context for each time step. The final bias error is averaged from both processing steps. Since properties usually vary with longitude / latitude and surface conditions, the evaluation is regionally stratified. Later these regional scores can be easily combined via average weighting into a single global score. Thus, this method offers an assessment via a single global (or regional) score, while still maintaining regional diagnostics on bias the ability to match temporal and spatial variability.
Level3 validation using AEROCOM methods The L3 validation of daily gridded products using AEROCOM tools is applied to the nearest satellite pixel value on a 1 o x 1 o grid corresponding to daily mean AERONET values excluding mountain sites. The evaluation with the AEROCOM tools provides bias, histograms, scatter plots, time series graphs, zonal mean comparisons, and score tables. This analysis includes all pixels regardless of quality flags or confidence indicators. A specific focus was put into common data point filters between the AATSR algorithms. The ORAC land / sea mask was used for all retrievals to differentiate between land, coast and sea cases.
Round Robin exercise The Round Robin exercise was set up for an independent and objective evaluation of the global retrieval results (AOD, AE) provided by each of the algorithms indicated in Section 3. The versions of the algorithms used to provide these products were selected by each of the retrieval groups based on the exercises described in Holzer-Popp et al. (  2013 ) and summarized in Section 4. The results were evaluated using the tools described in Section 5. Based on these results, the independent validation team (Section 2) provided an advice as regards the statistical quality. Other considerations were data coverage and spatial patterns. In addition, the same tools (Section 5) were applied to data from MODIS Aqua, MODIS Terra and MISR, for intercomparison and as a measure of how well the Aerosol-cci algorithms are performing in comparison to other satellite data sets which are often used in climate studies.
Level2 validation For the L2 evaluation of AOD and AE provided by the participating algorithms statistical measures evaluated were Pearson correlation coefficients, linear fit parameters, standard deviations (from linear fits and from AOD difference histograms), average differences, and number of AERONET sites and satellite pixels used. Examples of scatterplots between the satellite-retrieved AOD and AE vs. AERONET data are shown in Figures  1  and 2 , together with the fit parameters. These figures illustrate that there are differences amongst the several AATSR algorithms, both over ocean and over land, and between the AATSR and PARASOL results over ocean. In all cases over ocean satellite AOD is reasonably well correlated with AERONET, although outlyers are observed for ORAC, which may be due to insufficient cloud screening.
For AATSR, all three algorithms using only AATSR data (ADV, ORAC, SU) show good performance, although there are regional and seasonal differences. However, there is not one algorithm which performs best everywhere, as shown by the rankings provided in Section 6 from each of the three different methods. Overall, ADV appears to provide the best scores, and compares most favourably with the reference satellite data sets, but it does not provide any retrieval over highly reflecting surfaces. SU does provide retrievals over highly reflecting surfaces but the number of data points is very small, mainly due to the application of stricter quality control than ORAC and ADV. However, this stricter quality control does not lead to the highest scores everywhere. ORAC is potentially the most consistent algorithm in a mathematical sense, however both statistically and as regards the reproduction of features it performs less well than ADV and SU.