dataesr/scientific-paragraphs-categorization huggingface dataset:
Net result of temperature algorithm modifications The NDACC algorithm contains such corrections as dead time, background, and transmission. The new algorithm improves upon the background correction and identification of bad profiles, and it introduces corrections for signal spikes, TES, identification of good profiles, and noise reduction, all of which have not previously been addressed by the NDACC algorithm. The LTA data are recorded and saved at 75 m resolution. The spike and TES corrections described in Sect. 3. 3. 1 and 3. 3. 2 are carried out at this resolution. Then the profiles are integrated to 300 m, at which point the remainder of the corrections in Sect. 3 are applied. Temperature profiles using the new algorithm are calculated at 300 m resolution for LTA and are plotted as the green line in Fig.
The remaining temperature difference between the improved lidar temperatures (green) and the satellites and model may be in part due to distortions in the satellite a priori for the geopotential vector. This possibility is explored further in the companion paper, and all coincidence criteria for the satellite comparisons are available therein  (Wing et al. , 2018). It is important to note that additional complications exist when comparing temperatures derived from ground-based lidars to temperatures derived from satellite data which have their own calibration concerns. We explore the issues of lidar-satellite comparison in part 2 of this paper  (Wing et al. , 2018). A co-located ground-based resonance Doppler or Boltzmann lidar would provide a better comparison data set as resonance lidars have high signal-to-noise ratios above 75 km  (Alpers et al.
20-year comparison of OHP lidar temperatures Conducting systematic intercomparisons between independent lidar systems is essential for assuring data quality and is a requirement for NDACC-certified instruments. Most comparisons are conducted on a campaign basis where two or more lidar systems are co-located and make coincident measurements. A good example of this type of work was the stratospheric lidar and Upper Atmospheric Research Satellite (UARS) validation campaign  (Singh et al. , 1996). The present study proposes a completely novel type of inter-lidar   study on the long-term stability of the Rayleigh lidar technique. The first step in our analysis is to compare the temperature profiles from the LTA and LiO 3 S systems. LTA temperatures were calculated using the OHP NDACC temperature code, and LiO 3 S temperatures were calculated using a modified version of the same code.
There are very few significant differences between these two codes. The most important difference involves the choice of parameters for melding the high-and low-gain channels for the two systems. Given the differences in the relative gain between the four lidar channels being considered, the melding of LiO 3 S often occurs at a lower altitude than LTA. The present study considers temperatures between 35 and 75 km to ensure that we are well above any contamination from aerosols and below any significant initialization errors. From Fig. 11  we can see that there is no significant difference in the temperature outputs of these two algorithms (black baseline and orange) or with the improved algorithm (green) below 75 km. We selected the data from 1993 to 2013 for the comparison as both instruments operated regularly and without significant design changes during this time.
Since the lidars are co-located and are operated by the same technicians, they often make measurements simultaneously. Figure  12  shows the average number of measurements per month made by the LTA and LiO 3 S which were included in this study as well as the average number of common measurements per month. We defined common measurement times based on more than 80 % temporal overlap, good-quality profiles in both systems, and good internal alignment of both lidars. Of the 2482 nights of LTA data and 3194 nights of LiO 3 S, 1496 nights met our criteria for coincidence. Figure  13  shows the nightly temperature differences between the two lidar systems. The 20-year data set contains 1496 coincident measurements lasting longer than 4 h. Black vertical rectangles indicate some of the time periods where the high-or low-gain channels were misaligned in one or the other lidar.
An ideal solution would be to have an independent method of monitoring mirror alignment during atmospheric measurements (e. g. installation of a small sighting telescope to measure the alignment coupled with an automatic fibre optic alignment system). With the existing data set from OHP extending back 2 decades, we unfortunately cannot retrospectively address such a hardware goal, but there may be opportunities in future to look into the effects of choosing different criteria to identify periods of misalignment. Figure  14  shows four curves depicting the average temperature differences as a function of altitude and year. The red curve is the average temperature difference between 65 and 75 km with an average standard deviation of 6. 6 K; the green curve is the average temperature difference between 55 and 65 km with an average standard deviation of 4.
We have done this by clearly and carefully outlining a rigorous and complete algorithm for the calculation of lidar temperatures in the UMLT. We have presented techniques for the detection of signal contamination, the selection of the best data for inclusion in the calculation, and criteria for where to initialize the inversion when assuming an a priori pressure at the top of the atmosphere, and we have demonstrated the benefit of photomultiplier cooling and narrow-bandpass filters to reduce lidar backgrounds. After applying our techniques, we have seen a systematic cooling of the high-altitude lidar temperatures which brings them into better agreement with the temperatures measured by both MLS and SABER (Fig. 11 ). It is also important to note that the large variance associated with these ensemble differences can partially be attributed to the lack of control exerted on the error contribution from the choice of a priori initial pressure for lidar data and a priori contribution and non-LTE effects for satellite data.
OHP lidar 20-year comparison We have conducted the first-ever decadal temperature intercomparison between a co-located 532 nm Rayleigh lidar and an ozone DIAL system calculating temperatures from a 355 nm line. We have shown the following: 1. Rayleigh lidar temperatures calculated from ozone DIAL non-absorbing 355 nm line are statistically equal to temperatures from a traditional 532 nm Rayleigh temperature lidar over a large altitude range. This finding is of particular interest for the NDACC lidar temperature database as temperatures from ozone lidars may also be available for validation and inclusion. 2. Further theoretical work must be done on algorithms for melding data from high-and low-gain photoncounting channels. The current techniques produce statistically identical nightly temperature profiles; however, a -0.
Acknowledgements. This work is supported by the  Atmospheric dynamics Research InfraStructure in Europe Project  ( ARISE 2 ), which is funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 653980. French NDACC activities are supported by  Institut National des Sciences de l'Univers/Centre National de la Recherche Scientifique  ( INSU/CNRS ),  Université de Versailles Saint-Quentin-en-Yvelines (UVSQ) , and  Centre National d'Études Spatiales (CNES). The authors would also like to thank  Caitlyn Wing  for graphics support and the technicians at  La Station Géophysique Gérard Mégie at OHP. Edited by: Markus Rapp Reviewed by: three anonymous referees.
Data availability. The data used in this paper were obtained as part of the Network for the Detection of Atmospheric Composition Change (NDACC) and are publicly available (see http://www. ndsc. ncep. noaa. gov/data/, CPC Team, 2018) as well as from the SABER (see http://saber. gats-inc. com/data. php, SABER, 2018) and MLS (see https://mls. jpl. nasa. gov, Microwave Limb Sounder, 2018) data centres for public access.
Data Collection The data on children's representation of coral ecosystems were collected via a three-phase protocol implemented in each of the schools (Table  2 ): -First phase. The children were asked to make two drawings following these instructions: first "Draw me the sea" and then "Draw me the coral reef, " the children were given 20 min for each drawing, one after another. When the drawings were finished, each child was interviewed in order to gather information, name the elements that were drawn, and also acquire explanations about the possible direct and indirect experiences of children with the coral reef. The reef is a place of high biodiversity; 3: The reef is a whole; species interact with each other and the environment, including humans; 4: Humans are part of the system; they interact with others species, and enjoy, break, repair and protect the coral reef ecosystem that provide many services.
Data Analysis The items recorded in the drawings of each child were transformed into qualitative and quantitative data transcribed in a matrix with 16 quantitative variables belonging to three categories, seven for living natural items, four for seascape (nonliving) items and five for human-related items (Table  3 ). The textual information, such as the list of the types of animal or plant items which the child had named during the census of items (belonging to the marine or terrestrial ecosystems), was saved in a database, together with the scans of the drawings needed for the interpretations of the results. Data analysis was conducted in three stages in order to analyze the differences in coral reef representations before and after the awareness campaign using MARECO. An initial stage describing the children's representations of marine ecosystems (sea or coral reef) consists in an exploratory multivariate analysis aiming at describing the structure of the data (individuals, i.
e. , the drawings, and variables) over the whole of the dataset (Sea-Reef1-Reef2). This analysis will notably enable the positioning of the drawings of the reef (before and after MARECO) compared to those of the sea, in order to analyze the children's relationships with nature as seen through their drawings. A Principal Component Analysis (PCA) is performed on the matrix describing the 248 drawings according to the three themes (sea and reef) and school (Thio, Noell, Boyer, Coula, Banian), from the log-transformed data of the 16 variables (Table  3 ), so as to make it conform more closely to normal distributions and obtain a better synthesis of the data on the factorial axes. With the technique of supplementary variables, the gravity centers of individuals (drawings) clustered in relation to explanatory variables (theme of the drawing-Sea-Reef1-Reef2or school) are indicated in the first factorial plane with an ellipse bounding 95% of points or based on the calculation of a confidence interval of 95% around the gravity centers.
To this end, an analysis is conducted of the normality of residuals, their homoscedasticity (homogeneity of variance of residuals according to the estimated variable) and their independence (absence of apparent correlation). Moreover, the residuals are checked against explanatory factors of the variable to be explained ("number of colors"), i. e. , the theme of the drawings, school, class level, and gender. Finally, the third stage of data analysis takes into account the impact of MARECO more directly via analysis of the indicator variable "number of colors" on a reduced dataset (Reef1 and Reef2, 170 drawings) to better understand the factors explaining the change or shift in representation in the coral reef drawings after the use of MARECO. This analysis is conducted via a multifactorial variance analysis in which the explanatory factors are: school criteria (socially-advantaged urban neighborhood, disadvantaged urban neighborhood, close to a marine protected area, fishing environment or rural environment), the drawing's theme (Reef1, Reef2), class level (two grades: first year of elementary school, second year of elementary school) and gender (male and female).
43; p = 0. 045, Table  4 ). For Reef2, after the use of MARECO, Boyer, Coula, Banian, and Thio schools present drawings with more colors than for Reef1. The difference on paired data (mean of deviation) is highly significant ( = -1. 43, p < 0. 001, Table  4 ). The number of colors (raw, log-transformed or square root data) do not correspond to a normal distribution. Despite the non-independence of the samples and the non-normality of the variable "color, " and considering the robustness of the variance analysis model, the effect of the factors "theme" and "school" is tested, followed by a multiple comparison test. The validation of the model (results not pictured) shows that the residuals on raw variables are normal (Kolmogorov-Smirnov test and a right-skewed Normal Q-Q plot on standardized residuals).
Once the data acquisition method has been validated and the data acquired, coding is an essential point in the analysis procedure  (Thomas, 2006)  as it orients the results obtained, but may also be a source of bias owing to errors made by the coders. The solidity of this point was reinforced by a double coding carried out by two people simultaneously. Finally a particular emphasis was placed on the validation of the statistical procedure which is based on the combining of exploratory and confirmatory approaches. Apart from PCA, a descriptive multivariate analysis enabling a global analysis of the dataset and the identification of the main sources of variability, the validating of the statistical models used were made respecting the recommended steps for data exploration and general linear model  (Zuur, 2012).
ACKNOWLEDGMENTS The authors would like to thank the school teachers for participating in this project and their availability:  Valérie Boussemart ,  François Lahitete ,  Marie-Stéphanie Maperi ,  Jean-Charles Meyi , and  Jean-Baptiste Welta. Our thanks to all the school children who participated enthusiastically as well as to the parents who took part in the exercise with the MARECO toolbox. Thanks to the  New Caledonian Directorate of Education , particularly  Vanessa Montagnat , and the school directors who helped us to intervene in the schools. Thanks to  Marlène Dégremont  and  Claire Levacher  who took part in the program RESO-ECORAIL in New Caledonia and provided the codes of the drawings. Special thanks also to  Claire Garrigue  for her help and availability during the implementation of the MARECO protocol and to  Celia Northam  for her valuable support in translating into English and proofreading.
This CV is empirically estimable and quantifies the short-term loss of utility of the consumer who adopts a recommendation. Thus, the CV measures the difficulty of adopting a recommendation. In this article, we used recent estimates of price elasticity of food demand available in France  [13]. They are computed for four groups of consumers defined in terms of income levels (modest, lower average, upper average, welloff) by using food purchases recorded in a representative sample of about 5000 households by Kantar Worldpanel. Data on food consumption are taken from the French Food Survey INCA2  1.
Acknowledgements Financial support from  DEFRA (UK) , the  Daniel and Nina Carasso Foundation (France)  and  MMM-MAKERA (Finland)  within the  EraNet SUSDIET Project  ( SUSFOOD Call 2014-2017 ) is gratefully acknowledged. Financial support (to PL and L-GS) has also been received from the  Nutriperso Project (U. Paris Saclay).
Remote sensing of gravel-bed patches and resulting highresolution digital elevation models (DEMs) allow for the identification of various spatial scales of surface roughness. Thus far, dimensions relating to grain and bedform roughness scales have been determined using semivariograms or equivalent structure/autocorrelation functions. However, it is difficult to clearly differentiate roughness scales and separate analysis of roughness properties is not possible. This study examines the use of moving-window detrending on gravel-patch DEMs for isolating grain and bedform roughness and their respective topographic signatures. An extensive dataset of water-worked gravel surfaces collected in both laboratory and field environments is used. The measured bed topography is separated into two distinct DEMs: one representing grains, the other representing bedforms, and roughness properties are determined separately for grain and bedform DEMs.
In this paper, we examine relationships between low-amplitude bed undulations developed by water working (which we call bedforms) and the relief created by sediment grains' arrangement. In an attempt to provide general findings applicable for both controlled and uncontrolled gravel-bed environments, a large dataset, consisting of 35 water-worked gravel-bed patches from both the laboratory (17 DEMs) and the field (18 DEMs), is used for the analysis. Moving-window detrending [e. g. ,  2, 12]  was applied to each of the 35 DEMs to separate bed undulations from the topography due to the grains, leading to two distinct surfaces: one representing grains (which we call the "grain DEMs"), the other representing small bedforms (called "the bedform DEMs"). The novelty of the study is the separate examination of grain and bedform roughness isolated from DEMs, allowing to determine roughness signatures for both grain and bedform DEMs and to examine inter-relationships between grain and bedform roughness and their formative controls.
Gravel-bed patches and the data examined The data examined comprise 21 gravel patches measured by the authors and 14 gravel patches collected by others and already presented elsewhere  [7, 9]. The data and more detailed analysis and results are presented in  [8]. All DEMs satisfied the precondition of highresolution data necessary for grain roughness analysis, as evidenced by a small DEM grid spacing (<4 mm) compared to the size of the bed-surface material. Characteristic grain sizes of the bed surface (e. g. , D50A, the grain size for which 50% of the bed surface material is smaller) were all determined using Fehr's line-by-number method, facilitating comparisons between datasets (Fig. 1 ). Data collection by the authors was made with digital photogrammetry  [10]  using a pair of Nikon D5100 cameras (16.
17 D84A -1. 31, R 2 = 0. 92) provided estimates of D84A. It confirms that sediment is coarser on bedform tops (i. e. , humps) and finer in the troughs (i. e. , hollows). Separate analysis of the laboratory data for which formative flow conditions are known indicates that the ratio of D84A between humps and hollows (Dhu/Dho) increases significantly with both formative discharge and Shields stress increases (R 2 = 0. 91 for both relations, p < 0. 01), suggesting a better horizontal sorting of the bed-surface material at high formative discharge. We also measured evolving gravel-bed topography collectively with the topography due to grain arrangements and the underlying bed undulations. σZ is strongly correlated to both σZ,G measured from the grain DEMs (R 2 = 0. 96) and σZ,B measured from the bedform DEMs (R 2 = 0.
Conclusion Remote sensing of water-worked gravel beds now allows the collection of high-resolution DEMs, from which the surface roughness can be determined. Previous work relying on semivariograms has shown that gravel-bed roughness is scale dependent. However, relationships between roughness scales have not be obtained. This study has shown the possibility of using a moving-window detrending technique to isolate grain and bedform roughness from patch-scale gravel-bed DEMs. Resulting from roughness scale separation, roughness signatures of grains and bedforms can be studied separately. The analysis was applied to an extensive dataset comprising 35 water-worked gravel surfaces of varying size and resolution, collected in either a laboratory flume or the field. The results demonstrate that measures of grain roughness are easier to determine at smaller DEM sizes than bedform roughness signatures, which requires larger DEM sizes.
Material and Methods Particle backtracking dispersal model. The oceanic simulations were performed at 6 localities in the Western Mediterranean (Fig. 1 ), using the UCLA version 22 of the Regional Oceanic Modeling System (ROMS). ROMS is implemented over a domain that encompasses the Western Mediterranean Sea, from 7. 8°W to 9. 2°E and from 33. 4°N to 44. 5°N. The horizontal grid is 681 × 574 points with a resolution of 2. 1 km (1/40°) and the vertical discretization considers 42 sigma levels, allowing the representation of mesoscale structures in this region. The bathymetry was acquired from 1′ topography database  23. The simulations were nested and initialized from   the Mercator PSY2V4R3 simulation  24. Hourly outputs from a regional configuration of the atmospheric model Weather Research and Forecasting (WRF  25  ); are used to force ROMS using a bulk formula  26.
Data analysis. Mitochondrial DNA (Cytochrome oxidase I). The 57 sequences amplified in the present study from 6 different sites of the Western Mediterranean (Banyuls, the Ebro Delta, Alicante, Murcia, Ibiza and Mallorca; Fig. 1 ) were aligned using Bioedit Sequence Alignment Editor v. 7. 2. 5  32. After adding 235 COI sequences available from the literature from 24 Pinna nobilis in the Tyrrhenian and Sardinian Seas and in the Eastern Mediterranean Sea (16, 17, 18; Fig. 1 ), we constructed two different datasets. The first one included 57 COI sequences of 590 bp obtained in the present study from the Western Mediterranean populations. The second one included 292 COI sequences of 243 bp (maximum overlap between sequences of the different studies previously published) from all over the Mediterranean (Table  S1 ).
Only localities with a minimum number of 9 individuals sequenced available in the literature  [16] [17] [18]  were chosen for this second analysis. Haplotype and nucleotide diversity and pairwise F ST values were calculated using ARLEQUIN v. 3. 11 software  33. The same program was used to perform Tajima's D 34 and Fu's F s  35  neutrality test and mismatch distribution analyses  36  and the Ramos-Onsins and Rozas' R2 test  37  was performed with DNASP 5. 10. 01  38. Significance was tested by using 10,000 random permutations. A statistical parsimony network of haplotypes was built with TCS software v. 1. 21  39. Microsatellites. Allelic frequencies, observed (H O ) and expected (H E ) heterozygosities were estimated using ARLEQUIN v. 3. 11  33. Deviations from Hardy-Weinberg equilibrium (HWE) were characterized by the inbreeding coefficient (F IS ) with GENETIX v.
4. 05  40  and P-values where calculated GENEPOP v. 4. 2  41. Putative scoring errors were checked with MICRO-CHECKER v. 2. 2. 3  42  and FreeNA v. 9. 3  43  was used to to evaluate possible differences between estimates of global and pairwise F ST values including and excluding null alleles (ENA method for estimating F ST values). The assumption of linkage equilibrium was tested in GENEPOP 4. 2  42. The quantification of genetic differentiation was established through F ST  44  with FreeNA v. 9. 3  33. However, due to recent concerns raised about the usage of FST in highly polymorphic systems, we also calculated D ST  45. Pairwise DST statistic estimates and their significance using 1,000 bootstrap replicates were obtained using the DEMETICS  46  package in R software  47. In addition, genetic differences were further analysed using a correspondence analysis (CA) of the allele frequencies to detect differences and similarities between populations, using the BiodiversityR  48  package in R software.