dataesr/scientific-paragraphs-categorization huggingface dataset:
Accurate information about uncertainties is required in nearly all data analyses, e. g. , inter-comparisons, data assimilation, combined use. Validation of precision estimates (viz. , the random component of estimated uncertainty) is important for remote sensing measurements, which provide the information about atmospheric parameters by solving an inverse problem. For the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, this is a real challenge, due to the dependence of the signal-to-noise ratio (and thus precision estimates) on stellar properties, small number of self-collocated measurements, and growing noise as a function of time due to instrument aging. The estimated ozone uncertainties are small in the stratosphere for bright star occultations, which complicates validation of precision values, given the natural ozone variability.
Introduction Nearly all data analyses, e. g. , data comparisons, data assimilation, and combined use, require information about data uncertainty. Validation of precision estimates (viz. , the random component of the estimated uncertainty) is needed when the measurement uncertainty cannot be fully characterized or is based on assumptions. This is especially important for remote-sensing measurements, which use retrievals of atmospheric parameters by solving inverse problems. Precision of the remote sensing measurements is usually estimated via propagation of instrumental noise through the inversion algorithm. These precision estimates can be imperfect due to incomplete forward models or retrieval approximations. This paper is dedicated to validation of stratospheric ozone precision estimates for the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument.
, 2010). First, atmospheric transmission data from every tangent height are inverted to horizontal column densities (along the line of sight) for gases and optical thickness for aerosols (spectral inversion). Then, for every constituent, the collection of the horizontal column densities at successive tangent heights is inverted to vertical density profiles (vertical inversion). Although the measurements are performed during night and day, only nighttime measurements have been used in scientific studies so far, as the scattered solar light significantly degrades the quality and altitude coverage of daytime occultations. In this paper, only nighttime measurements (with solar zenith angles larger than 107 • at the tangent points) are discussed. GOMOS has performed about 150-200 nighttime occultations per day with global coverage.
Typical examples of GOMOS data coverage and distribution over the globe are shown, for example, in  Bertaux et al. (2010) ,  Kyrölä et al. (2010) ,  Sofieva et al. (2013) , and  Tamminen et al. (2010). The vertical resolution (including the smoothing properties of the inversion) of GOMOS ozone profiles is 2 km below 30 km and 3 km above 40 km; it is the same for all occultations. In this paper, we discuss challenges in the validation of GOMOS ozone precision estimates and propose a simple method that allows for such validation in the stratosphere. The paper is organized as follows. Section 2 outlines the principles of the GOMOS precision derivation. In Sect. 3, we review the existing methods for validation of precision estimates and discuss their applicability to GOMOS measurements. In Sect.
Specifics of GOMOS measurements and challenges for precision validation The stellar flux recorded by GOMOS -and thus signal-tonoise ratio and precision of retrieved profiles -depends on the magnitude and spectral class of the observed star, which means that the measurement precision varies substantially over the data set. Precision of retrieved profiles depends slightly on obliquity of occultation due to the influence of scintillations  (Sofieva et al. , 2009). The GOMOS error estimates also slightly depend on ozone concentration, but this dependence is much weaker than the dependence on stellar magnitude and the spectral class. In addition, GOMOS uncertainty estimates grow with instrument age  (Tamminen et al. , 2010). These features present some challenges for the validation of the GOMOS precision.
Description of the method If the measurements are selected in a region of small and slowly changing natural variability, then the sample variances corresponding to different data sets i (for GOMOS, corresponding to different stars) can be written as s 2 i = σ 2 nat + σ 2 i. (4) If the precision estimates are correct, then the difference in sample variance will be equal to the difference in precision estimates, s 2 i -s 2 j = σ 2 i -σ 2 j. The term σ 2 nat cancels out because it is assumed to be the same for both samples. At the same time, s 2 i provides the upper limit for experimental estimates of measurement precision, as s 2 i > σ 2 i. If σ i → 0, then s 2 i → σ 2 nat. Important conditions for successful application of this method (which we call "differential method" hereafter) include the following: a.
The only exception is the residual scintillation error, which depends on obliquity of occultation (and does not depend on stellar properties); that is, it is additive. However, the residual scintillation error is relatively small, and it has an accurate parameterization in GOMOS retrievals  (Sofieva et al. , 2010). Therefore, it should not disturb the application of the differential method to GOMOS data (this is consistent with the results presented above). The analysis of GOMOS occultations in the tropical stratosphere has shown that the difference in sample variances for different stars at altitudes 25-45 km is well explained by the difference in precisions, if stars are not dim (visual magnitude less than 1. 9). Since (i) this is observed for several stars and (ii) χ 2 norm ≈ 1 in these occultations in the stratosphere, we can conclude that GOMOS precision estimates are close to reality for such measurements.
Extension for other instruments The extension of the differential method for precision validation by including data from another instrument is quite straightforward: it can be considered as one more sample with corresponding precision estimates. As an example, we compare the natural variability in the tropics, as estimated by GOMOS data for the seven brightest stars and by the MIPAS instrument on board the Envisat satellite. MIPAS is a Fourier transform spectrometer operating in the infrared, which provides vertical profiles of ozone and other trace gases. For our illustration, we have used the data processed with KIT IMK/IAA version V5R_O3_220/221 research processor  (von Clarmann et al. , 2003 (von Clarmann et al. , , 2009)). MIPAS provides ozone profiles in the altitude range from 6 to 70 km with a vertical resolution of 2-5 km.
9). For dim stars, the uncertainties of retrieved ozone profiles are overestimated. The reason for this overestimation is now understood: it is related to improper accounting of the uncertainty associated with dark charge correction in the GOMOS error budget. This issue will be corrected in future GOMOS data processing (the correct error budget affects precision estimates mainly, while the changes in ozone values themselves are insignificant). The application of the differential method to other altitude ranges and to other constituents is not readily possible due to violation of assumptions needed for the method to work. An extension to use of other instruments is quite natural, as illustrated here with GOMOS and MIPAS measurements. Estimates of ozone natural variability in the tropics from MIPAS and GOMOS using the seven brightest stars are very close to each other; thus, this provides additional confirmation of the correctness of the corresponding precision estimates, for both instruments.
A similar analysis of collocated MIPAS ozone data close to the North Pole in winter has shown a very similar behavior of experimental and estimated uncertainties (not shown here). However, when analyzing collocated MIPAS data at the North Pole in summer, the experimental error estimates converge practically down to the precision estimates, as shown in  Laeng et al. (2014). This is a clear indication that the ozone natural variability, even at small scales (a few hundreds of kilometers), is not zero in winter polar regions. The magnitude of this small-scale natural variability s 2 12 /2 -σ 2 is very similar in analyses of GO-MOS and MIPAS data; it is ∼ 3 % at 20-30 km increasing up to 5-6 % at 40-50 km. Absence of collocated GOMOS data in the regions of low natural variability does not allow a full validation of precision estimates according to Method 3, whose application is illustrated here.
Acknowledgments We would like to thank  Mina Aganagic ,  Clay Cordova ,  Tudor Dimofte ,  Oliver Fabert ,  Sergei Gukov ,  Daniel Jafferis ,  Juan Maldacena ,  Christoph Schweigert , and  Nick Warner  for useful discussions. We are grateful to the  Simons Center for Geometry and Physics  for hospitality during part of this project. IB is supported in part by the  DOE  grant  DE-FG03-84ER-40168 ,  ANR  grant  08-JCJC-0001-0. The work of MG is supported by the  German Science Foundation (DFG)  within the  Research Training Group 1670  " Mathematics Inspired by String Theory and QFT. ".
Contribution to the integrated evaluation of farming systems The economy theme contributed to the integrated analysis of 15 farming systems  (Bonneau et al. , 2014b) , with the six primary indicators. In six systems, the data were not available as the farmers were reluctant, or simply refused to reply to economic and financial questions. The primary indicators from these farms were then estimated through more global sources. Data from the FADN were used to characterize production levels of conventional systems as well as prices (pigs, feed, etc. ). The FADN (http://ec. europa. eu/agriculture/ rica/) is an official European Community instrument for evaluating the income of agricultural holdings and the impacts of the Common Agricultural Policy throughout Europe. These data were combined with Interpig results, furnishing valuable information on productivity results, production costs, slaughter weights as well as financial details.
Interpig is a European workgroup whose aim is to provide comparable technical and economic information in the main European pig producing countries; yearly production cost reports are published by the BPEX, cofounding member of the group (http:// www. bpex. org/prices-facts-figures/reports/InterpigReports. aspx). BPEX is the pig section of the British Agriculture and Horticulture Development board. These results for the conventional systems were combined with expert information for the differentiated systems. The data estimates were based on the number of animals present on each farm (sows for farrow-to-finish units and number of pigs produced for fatteners).
Acknowledgements The authors gratefully acknowledge from the  European Community  financial participation under the  Sixth Framework Programme for Research, Technological Development and Demonstration Activities , for the  Integrated  Project  Q-PorkChains FOOD-CT-2007-036245. The views expressed in this publication are the sole responsibility of the author(s) and do not necessarily reflect the views of the  European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use, which might be made of the information. The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability.
After 4 min (At 24 min), it becomes 100% (A) during 4 min. The data were stored and processed by an HPLC Chemstation (Dos Series) (HewlettePackard). Phenolic compounds in olive pulp extracts were identified by comparison of their retention times with the corresponding standards. Resorcinol (purchased from SigmaeAldrich, St. Louis, France) was used as internal standard for the quantification of identified phenols and results are given as mg g À1 of d. w.
Tocopherols determination by HPLC analysis Tocopherols in olive pulp were extracted according to the method described by  Chun, Lee, Ye, Exler, and Eitenmiller (2006)  and the extracts were evaluated by HPLC analysis. HPLC separation was carried out on HewlettePackard system (Waldbronn, Germany) comprising a HP-1100 pump, a Rheodyne model 7725 injector (Cotati, CA, USA, loop volume 20 ml), a UV detector during 18 min. The data were stored and processed by an HPLC Chemstation (Dos Series) (HewlettePackard). Tocopherols in olive pulp extracts were identified by comparison of their retention times with the corresponding standards (obtained from Sigma-eAldrich, St. Louis, Mo. , France). Typical HPLC chromatogram is given in Fig. 1  a-tocopherol acetate (SigmaeAldrich, St. Louis, Mo. , France) was used as internal standard for the quantification of tocopherol isomers and results are given as mg g À1 of d.
Acknowledgments This study was supported by the  Ministry of Higher Education and Scientific Research in Tunisia. We express our sincere thanks to the members of the ONH for their technical assistance. Special thanks go to the members of LR-NAFS/LR12ES05 « Nutrition-Aliment Fonctionnel et Sant e Vasculaire » and also to the  Olive Tree Institute (Sousse, Tunisia)  for its support to this research.
Methods Establishing the repertoire of M. abscessus phages The genomes of 48 M. abscessus mycobacteria available in June 2013 were downloaded from Genbank (Table  1 , Additional file 1). As for 47 unfinished genomes, the contigs were reoriented based on the M. abscessus type strain genome (GenBank GCF_000069185. 1) used as reference using MAUVE software  [24]. The prophage regions were detected using PHAST software  [25]. Protein sequences were predicted in all genomes using prodigal software  [26]  in order to normalize prediction. M. abscessus panproteome was annotated using BlastP search with a cutoff E-value < 0. 001, percentage similarity > 30% and an alignment length > 50 amino-acids against a home-made database (including PHAST database, Mimivirus, Marseillevirus and additional mycobacteriophage proteins).
We further analyzed the M. abscessus complex genomes for Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) using CRISPRs finder program  [27]. Determining M. abscessus phage phylogenies M. abscessus mycobacteria tree M. abscessus genomes were aligned using Muscle aligner implemented in Mauve software  [24]. Mauve alignment generated an identity matrix which the identity scores range between 0 and 1, where 0 indicates that no identical homologous nucleotides were found, and 1 indicates that every homologous nucleotide was identical. This matrix was then used to construct M. abscessus split network using Neighbor-Net algorithm in the package SplitsTree4  [28].
M. abscessus phage tree The annotated viral and phage proteins were classified using OrthoMCL software  [29]. Only protein sequences > 50-amino acid residues were considered for further analyses. Homologous sequences were selected using the all-against-all BlastP algorithm  [30]  with an E value of <10 -5. Then, clustering of the orthologous sequences was analyzed using the Markov Cluster algorithm  [31]. The inflation index of 1. 5 was used to regulate cluster tightness (granularity). The resulting orthologous groups were used to construct a whole-genome network using the Neighbor-Net algorithm based on a gene content matrix. The similarity between two species is defined as the number of phage genes in common divided by the total number of genes of the two species. [32, 33]. Using this matrix, we constructed also a heatmap clusterization using R package [http://www.
Discussion Analysing 171 prophage regions in 47 M. abscessus complex genomes indicated that M. abscessus complex has been infected by at least 17 different mycobacteriophages, including Araucaria, the sole available mycobacteriophage that we recently isolated from M. bolletii  [23]. Noteworthy, M. abscessus M154, an isolate from Malaysia  [38]  is the only strain lacking any evidence for phage. Our previous analysis revealed no unique genes in this strain  [39] , which nevertheless encodes four possible CRISPRs, one cmr1 family and one cmr4 family, with potential immunity against phage infection  [40]. Alternatively, no phage was detected in this strain because of database limitations. For example, Araucaria did not yield significant homology with any other M. abscessus complex phage, as confirmed by phylogenetic studies using whole viral and phage proteins clusterization and TMP protein sequence based tree.
Elements requiring significantly more details: p. 4. l. 86: The authors report that 'M. abscessus proteomes were aligned using Mauve software  [24] '. I am not familiar with ProgressiveMauve, but does this software really align proteomes, or is it rather a tool to align genomes based on their content and gene order? If so, the next sentence in the MS is hard to understand Authors' response: M. abscessus genomes were aligned using Mauve software. Progressive Mauve uses Muscle or clustlW to perform alignment. Here we used Muscle. The authors corrected and explained the methodology (P. 4 line 86). 'Then M. abscessus tree was constructed using Neighbor-Net algorithm in the package SplitsTree'. What distance matrix was provided to Splitstree? What was this distance reflecting? Authors' response: Mauve alignment generates an identity matrix file which calculates the identity score range between 0 and 1; 0 indicates no homologous nucleotides and 1 indicates that every homologous nucleotide was identical.
Mesoscale model The research mesoscale version of the non-hydrostatic Weather and Research Forecasting WRF-ARW model  (Version 3. 3; Skamarock et al. , 2008)  has been used for the simulation periods of 36 h (in cases C1-C3, Table  1 ) and 60 h (case C4) starting at noon of the previous day to reproduce chosen cases. The initial and boundary conditions were taken from the global ECMWF analysis and the lateral boundary conditions were updated every 6 h. The ECMWF analysis resolution was not constant: in the four case studies analysed here, it was T511 (0. 351 • horizontal resolution) before 1 February 2006, and T799 (0. 225 • horizontal resolution) after. The vegetation and land-use data come from the USGS 24 category data set at a resolution of 30. Three domains Table  1. Selected simulated WRF cases with the Cb clouds over Istria in which the SB interacts with one of the dominant types of large-scale (LS) wind (NE, NW, SE, SW).
Weak storm intensity in C3 provokes the question: how much is C3 really a typical thunderstorm case for the NE Adriatic?  Mikuš et al. (2012)  stressed that the SW wind regime on the front side of low pressure systems dominated in lightning climatology as atmospheric background for thunderstorms. They defined a thunderstorm day as a day with at least 10 lightning strokes per hour (regardless of time of day) across the NE Adriatic. As daytime Cb development is the focus here, we performed the additional (re)analysis of weather types and wind regimes based on the same methodology as in  Mikuš et al. (2012). Accordingly, we have selected 100 days with daytime Cb and/or only daytime lightning (see Sect. 2. 2) in the warm period from 2000 to 2009. The reanalysis of thunderstorm database showed that when the potential SB-Cb interaction occurred, NW and NE wind regimes were almost always products of remote low pressure systems and were much more frequent as atmospheric conditions (than in  Mikuš et al.
Acknowledgements. This work has been supported by the  Ministry of Science, Education and Sports  (grants " BORA " no. 119-1193086-1311 ; " AQCT " no. 119-1193086-1323 ; " Storms and Natural Disasters in Croatia " no. 004-1193086-3036 ) contributing also to the  HyMeX programme. We are very grateful to the  Slovenian Environment Agency  for providing radar data. The second author would also like to thank  Igor Tomažić  for satellite images. Useful comments and suggestions from two anonymous reviewers are greatly appreciated. Topical Editor  V. Kotroni  thanks two anonymous referees for their help in evaluating this paper.
7 meters6239. 06 (SE)) but were mostly located in semi-natural habitats that were subject to less intensive management. We assigned 200 meter circular buffers around each station and calculated the proportion of semi-natural habitat (grasslands, wetlands and woodlands) within the buffer using a regional land use database  [14]. We sampled at different distances from hedgerows. We calculated the distance from each station to the nearest hedgerow (4 classes of distance: 0-24, 25-49, 50-99 and 100 or more meters from the hedgerow) using a regional hedgerow database  [15].
For each station and each session, we recorded one 30 minute sound sample. This sampling occurred randomly during the period of bat peak activity that begins 30 minutes after sunset and spans 4 hours  [17]. It was only performed if weather conditions were favorable, i. e. , no rain, wind speed lower than 10 km/h and temperature higher than 12uC. Nebulosity (i. e. , cloudiness) (mean = 4. 160. 2 octas), temperature and wind speed data were retrieved from a local weather station  [18]. To reduce the influence of weather conditions between sampled stations, we recorded 10 stations per night (5 stations simultaneously). Species calls were identified using Syrinx software version 2. 6  [19]  for spectrogram analyses and playback. Each contact was identified to the species level, except for Plecotus austriacus and Plecotus auritus, which were pooled in the Plecotus spp.
,  [26] ). Based on the nature of the response variable (counts of bat calls), we expected a non-normal distribution. To identify the best model we built 4 GLMs for each tested species: one with a Poisson error distribution (GLMP), one with a negative binomial distribution (GLMNB) and 2 with a zero-inflated hypothesis (one with a Poisson error distribution (ZAP) and one with a negative binomial (ZANB) with the R package pscl  [27] ). The zero inflated models used were hurdle models (ZAP and ZANB) that consider presence and absence data (with a binomial function) and analyze the presence data in a second step with a count model (Poisson or a negative binomial)  [28]. To identify the best error distribution, we used an AIC approach and examined the pattern of residuals, as proposed by Zuur et al.
Assessment of bat activity We recorded 205,036 calls belonging to 15 species. In particular, we obtained a large amount of data for 7 species (from at least 20 stations) (Table  1 ). All of the species recorded during the reproductive period (the first survey period) were also found during the second period. However, certain species, e. g. , Barbastella barbastellus, were only detected during the second period (August-September). The most abundant species in both periods was Pipistrellus pipistrellus (Table  1 ).
Acknowledgments Many thanks to  Lucille Brianceau  for assistance in the field and to Lea Noe ¨l for her help with the sonogram analyses. We are grateful to CORELA ( Conservatoire Regional des Rives de la Loire  et de ses Affluents: http://www. corela. org/)for the land use database and the hedgerow database. This study was supported by EDF company. We are grateful to EDF R&D and to EDF Cordemais for their interest in our work and for providing access to the industrial site. We thank reviewers for their useful comments which helped to improve the manuscript significantly. Our thanks also go to American Journal Experts for the language revisions.