dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 2. Following the terms of reference of the EC mandate, the EFSA GMO Panel considered whether the GS methodology applied during the 2013 growing season of maize MON 810 differs from that followed in the previous PMEM reports on maize MON 810 (EFSA  GMO Panel, 2011a , 2012a , 2013 , 2014a). The EFSA GMO Panel assessed the new 2013 GS dataset on maize MON 810 (see section 3). In addition, the EFSA GMO Panel performed a literature search to identify relevant scientific publications not reported by the applicant, and subsequently assessed their relevance for the risk assessment and/or risk management of maize MON 810. In its assessment, the EFSA GMO Panel also considered the comments from Member States on the revised annual 2013 PMEM report. 9.
Farmer questionnaires 3. 1. In its revised annual 2013 PMEM report, the applicant submitted a survey completed between December 2013 and March 2014 based on 256 questionnaires received from farmers in four European countries: 190 in Spain, 46 in Portugal, 18 in the Czech Republic and 2 in Romania. The applicant concluded that the analysis of the questionnaires 'did not reveal any unanticipated adverse effects that could be associated with the genetic modification in MON 810'. The EFSA GMO Panel, in close collaboration with the EFSA AMU Unit, assessed the methodology followed by the applicant to analyse the farmer questionnaires. Alongside the methodological guidance for a systematic evaluation of farmer questionnaires, the evaluation of the overall 2013 farmer's survey (including, for example, sampling of farmers, types of questions, method of conduct interviews, data validation, method used for the design of the statistical analysis) is given in Annex A.
Boolean operators (i. e. OR, AND) were appropriately used to combine terms, while wild card symbols allowed to retrieve variant spellings (e. g. toleran*, protec*). The applicant used Web of Science SM12 as the only scientific literature database to identify relevant publications. The applicant identified 21 scientific publications related to maize MON 810 and/or the Cry1Ab protein published between June 2013 and the beginning of June 2014 (Appendix A). One publication was relevant to the molecular characterisation of maize MON 810, 8 publications were relevant for the food and feed (FF) safety assessment (specifically, animal feeding studies and human in vitro tests), and 12 publications pertained to the environmental risk assessment (ERA) or risk management (studies assessing the interaction of maize MON 810 with target organisms (TOs) and non-target organisms (NTOs)).
Two of these publications,  Campagne et al. (2013)  and  Twardowski et al. (2014) , had already been assessed by EFSA (2014) and its GMO Panel (EFSA GMO Panel, 2015) and did not reveal safety concerns. The EFSA GMO Panel noted that 12 relevant scientific publications related to Bt-maize/maize MON 810 and/or the Cry1Ab protein (published between June 2013 and beginning June 2014) were not reported by the applicant (see section 3. 3. 2). Therefore, the EFSA GMO Panel makes the following recommendations:  The literature search performed by the applicant should adhere to some fundamental principles of systematic review, i. e. methodological rigour and coherence in the retrieval and selection of scientific publications, transparency and reproducibility of the performed literature 11 The list of keywords used to query specific literature databases when performing the literature search are given in Table  1  of the revised annual 2013 PMEM report on maize MON 810, Appendix 5.
12 http://apps. webofknowledge. com/ (accessed on 10 October 2015). search  (EFSA, 2010). In this respect, the applicant did not provide information on the date of the search, the full list of retrieved scientific publications and clear criteria for exclusion/inclusion of relevant scientific publications.  In addition, the EFSA GMO Panel considers that additional scientific literature databases such as CAB Abstracts ®13 should have been used to increase the likelihood to retrieve all relevant scientific publications (EFSA, 2015b).  The protocol for the literature search by the applicant is revised accordingly and supplied to EFSA annually. The EFSA GMO Panel assessed all the scientific publications selected by the applicant, and acknowledged that these were adequately discussed and put into the context of the overall safety assessment of maize MON 810 in line with previous recommendations made by the EFSA GMO Panel.
Additional publications identified by the EFSA GMO Panel EFSA conducted two separate, independent, literature searches to identify additional relevant scientific publications. Several bibliographic databases were queried simultaneously to identify as many relevant peer-reviewed scientific publications as possible. These were BIOSIS Citation Index SM14 , CAB Abstracts ® , Current Contents Connect ®15 , Medline ®16 and Web of Science Core Collection TM17. One literature search was performed using the same search terms and Boolean operators as the applicant, and the second one with a combination of generic terms previously used by the EFSA GMO Panel (2012b). Both searches targeted scientific publications in the peer-reviewed scientific literature between June 2013 and June 2014, which was the time interval covered by the literature search conducted by the applicant.
The searches were subsequently refined by selecting only those publications (as 'document type') that were written in English. Both searches were conducted on 7 July 2015 and identified a total of 469 references. References from both searches were exported into a single EndNote X5 database (Thomson Reuters, New York, NY, USA). Then, duplicated references were deleted. The 312 remaining scientific publications were screened and assessed by title and abstract by EFSA. Only peer-reviewed publications containing evidence specific to the risk assessment and/or management of maize MON 810 were considered for further assessment. The EFSA GMO Panel identified a total number of 37 relevant scientific publications, 18 of which 25 were reported by the applicant in its revised annual 2013 PMEM report on maize MON 810, and/or which were previously discussed in EFSA GMO Panel scientific outputs  (EFSA, 2014; EFSA GMO Panel, 2015).
The outcome of the literature review confirms the previous conclusions on the safety of maize MON 810 made by the EFSA GMO Panel. However, the EFSA GMO Panel reiterates some of its previous recommendations, i. e. to adapt the IRM strategy for ECB and MCB populations and to use farmer questionnaires as a tool to give early warning of the potential for resistance evolution in populations of non-target lepidopteran pests. In addition, considering the relevant publications identified as missing, the EFSA GMO Panel advises the applicant to improve the methodology followed in the literature review to ensure that all relevant publications are identified and assessed (e. g. use of additional databases, provide inclusion/exclusion criteria). No information collected from existing monitoring networks in the EU was provided by the applicant.
Content validity Background data Background data relating to geographical location at country and county level, surrounding environment, soil type, crop rotations in the previous two years and fertiliser treatments and irrigation are collected by the questionnaire. It would be of value to take longitude and latitude measurements of the representative GM maize field; information of this nature would facilitate linkage with other spatial monitoring datasets. In addition, the questionnaire should record for how many years the farmer has been growing maize MON 810 on the farm, and the question on crop rotation should also record, for rotations in which maize was grown, whether this was GM or conventional maize.
Data on possible change in behaviour and performance of the GM crop The following characteristics were monitored to obtain data on any change in the behaviour and performance of maize MON 810: crop rotation, time of planting, tillage and planting technique, insect control practices, weed control practices, fungal control practices, fertiliser application, irrigation practice, time of harvest, germination vigour, time to emergence, time to male flowering, plant growth and development, incidence of stalk/root lodging, time to maturity, and yield. It is noted that information on plant protection products applied to the GM maize field was collected, but the same information was not supplied for the conventional field. In order to fully explain changes in plant protection product use, the products applied to the conventional field should also be recorded, and the quantities applied over the season to the GM maize field and the comparator field should be recorded.
Data on possible ecological/environmental impacts of the GM crop on the protection goals and measurement The following characteristics were monitored to obtain information on possible ecological/environmental impacts of maize MON 810 on protection goals: occurrence of MON 810 volunteers, disease susceptibility, insect pest control (Ostrinia nubilalis), insect pest control (Sesamia spp. ), pest susceptibility, weed pressure, occurrence of insects, occurrence of birds, occurrence of mammals. For some closed questions, e. g. on occurrence of insects, birds and mammals, the option 'Do not know' is included; however, it has been excluded in other closed analysis questions, forcing the farmer to make a clear assessment. Allergenicity in people handling the GM crop during production and harvesting could be an adverse effect: a question to assess this should be included in the questionnaire.
External consistency Comparison of the data reported in the survey with information from independent data sources provides a further opportunity to test the validity of the responses. The information on soil quality offers the opportunity to compare it with the information held in the Soil Profile Analytical Database for Europe (SPADE-2)  (Hollis et al. , 2006). Figure  1  shows the information on top soil organic carbon contained in this database. The MON 810 survey reports organic carbon content values between 0. 6 % and 6. 5 % with a mean of 1. 6 %. It can be seen that this range falls within that of the SPADE-2 range for organic carbon content. It should be noted that the SPADE-2 database provides a useful dataset for European soil properties but that the values are based on a limited set of soil samples for each EU country.
Validation procedures Section 2. 7 of Appendix 1 describes the data management and quality control procedures. It states that 'For not readable entries in the questionnaires, queries were formulated and the field representatives or farmers were asked for clarification. These entries in the database were corrected'. In section 3. 1 it is further explained that 'After the first quality and plausibility control, 8 farmers were contacted again to provide additional clarifications (2 from Spain, 4 from Portugal and 2 from Czech Republic). Examples of items that had to be clarified were incorrect variety names and missed answers (surrounding environment, weed and pest control practices in conventional maize). Two farmers were also asked to clarify some inconsistencies between weed and pest control practices in conventional maize compared to MON 810, and plant protection products used in MON 810.
Recommendations and conclusions From the data provided in the 2013 survey from the farmer questionnaire to monitor adverse effects associated with the cultivation of maize MON 810, no adverse effect can be identified. However, the revised annual 2013 MON 810 report provides limited information on the sampling methodology and the possibility of selection bias in the survey cannot be excluded. Therefore, the following improvements to the survey design and reporting are recommended:  Full details on the source of the sampling frame, the number of farmers and the major characteristics (e. g. previous cultivation of maize MON 810) of the farmers should be included in the survey report. The national registers set by Member States for the cultivation of GM crops would be the optimum sampling frame, if available.
Prehistoric Architecture The Frama-C architecture is from the origin based on a single fundamental principle: it is a plug-in oriented architecture à la Eclipse 1  in which each analyzer is a plug-in which uses a kernel. This design choice is nowadays the standard way to provide extensibility while helping to solve the homogeneity vs heterogeneity issue: the kernel ensures some homogeneity while plug-ins may develop their own concepts when required. Analyzer collaboration through API is ensured by the kernel which maintains a plug-in database storing the API functions. However, at the very beginning of the Frama-C development, this principle was more an essential principle than a perceptible reality expressed by Figure  1. Indeed plug-in developers have to modify themselves some parts of the kernel (particularly the plugin database and the Makefile) to register new analyzers, while it was not possible to plug or unplug them without recompiling the whole tool.
Acknowledgments Special thanks to  Virgile Prevosto  and  Boris Yakobowski  who spend a large amount of their time to review the huge Git branch implementing the new architecture. I would also like to thank  Francois Bobot  and  Loïc Correnson  for their constructive feedback and suggestions about it as well as  Florent Kirchner  who provides a continued support to this major change. Furthermore the anonymous referees contribute to improve the final version of this paper thanks to their fruitful remarks.
Summer maxima of daily temperature maxima In this paper, we focus on JJA (June-July-August) maxima of daily maximum temperatures over Europe. We work with two types of gridded data: E-OBS temperatures from 1950 to 2013 (0. 5°h orizontal resolution;  Haylock et al. , 2008)  and climate model runs from the CNRM-CM5 model (1. 4°horizontal resolution;  Voldoire and et al. , 2012). This climate model was used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and has 10 historical simulations over the period 1850-2005, 5 simulations over the future period (2006-2100) under the Representative Concentration Pathways (RCP) 8. 5 (for a total radiative forcing pathway leading to 8. 5 W/m 2 in 2100). In this paper, we refer to the E-OBS observation dataset as EOBS and to the historical and future ensembles as  HIST (1950 -2005 ) and RCP8.
5 (2006 -2100) , respectively. To compare spatial clusterings between observations and model outputs, EOBS temperatures are interpolated on the model grid using the remapcon command of the Climate Data Operators software (http://www. mpimet. mpg. de/cdo), a conservative regridding method. Simulated temperatures have first been linearly de-trended from the non-physical long-term changes (model drift) found in climate models  (Gupta et al. , 2013). This drift is estimated from the control simulation, a long simulation (850 years) in which the natural (solar activity and volcanic aerosols) and anthropogenic (greenhouse gases and aerosols) forcings remain constant at their pre-industrial 1850 values. In a second step, we de-trend observed and simulated temperatures from the long-term warming trend, following a two-step procedure.
First, we remove the multi-year climatological average from daily temperature maxima within every dataset. Then, from these temperature residuals, the historical and future trends are estimated by the HIST and RCP8. 5 ensemble average (respectively), for every grid point and each calendar day. These 10-and 5-member means (respectively) are filtered by a 91-day running average. The future trend is removed from the simulated temperatures of the RCP8. 5 ensemble, whereas the historical trend is removed from both observed temperatures of EOBS and simulated temperatures of the HIST ensemble. Removing the same trend from observations and simulations adds the assumption that the model correctly captures the mean response to anthropogenic greenhouse gases over Europe. However, our algorithm always works with the ranks of individual time series, not their absolute values (see Eq.
But averaging breaks the concept of max-stability, the mean of two maxima is not a maximum anymore and the interpretation with the extremal coefficient cannot be used within a k-means algorithm. As already noted by  Bernard et al. (2013) , the Partitioning Around Medoids (PAM) algorithm proposed by  Kaufman and Rousseeuw(1990)  has the advantage of preserving maxima in the sense that each cluster centers remains a time series of maxima, not an average of time series. The PAM algorithm divides a dataset of N objects into K clusters. Three pre-processing steps are needed before implementing PAM. First, the distance matrix defined by (1) needs to be computed. Second, the number of clusters K must be chosen and third, the algorithm requires an initial set of K medoids, which are randomly selected.
Clustering EOBS The clustering algorithm applied to the de-trended EOBS dataset of JJA seasonal maximum of daily maximum temperature is provided in Fig. 1  for the particular number of clusters K ¼ 15. As expected, the insignificant points are located mostly at the boundaries between two clusters, which is a positive sign concerning the robustness of our clustering algorithm. Medoids are not necessary located in the center of a cluster. Concerning the climate interpretation, the output with K ¼ 15 clusters show coherent structures and highlights regional climates, e. g. over the Iberia peninsula and the United Kingdom. These clear spatial coherent structures must have a physical cause, likely due to synoptic-scale weather patterns. However, no geographical information like latitudinal and longitudinal coordinates is used in the clustering algorithm, which is only driven by the ranks of time series of JJA seasonal maxima of daily maximum temperatures.
1. (For interpretation of the references to color in this figure caption, the reader is referred to the web version of this paper. ) Fig. 2. Silhouette coefficient statistics (median, 10th and 90th percentiles) of s K ( ) defined by Eq. (  5 ) as a function of the number of clusters from K¼2 to K¼ 50. A larger value corresponds to a more coherent clustering. The horizontal line indicates to the significance level of 0. 1 and the dataset corresponds to EOBS JJA maximum temperatures (detrended) for panel (a), and HIST JJA maximum temperatures (de-trended) for panel (b). # (around Serbia), 8 # (around Austria) and 15 # (Turkey), respectively. The maps show the locations of the cluster medoids (diamonds) and the cluster members (colored points), where only significant members are considered (s K 0.
Bernard Reducing the dimensionality of a dataset while preserving information about the extremes is a difficult task because it falls within the realm of extreme value theory where handling multivariate/spatial data can be challenging. The distance measure used here, has fundamental links with multivariate extreme value theory via the extremal coefficient, but does not rely on estimating any parameters or dependence structures. In fact, the measure is very straightforward to compute, and it is appropriate for clustering extremes. Moreover, the framework is couched inside the usual, and well understood, clustering framework; specifically, in this case, the Partitioning Around Medoids (PAM) technique. Another important contribution of this work is the handling of ensembles of data, which introduce unique challenges.
Models having a large number of members in their ensembles could be particularly useful for these issues. Indeed, the effect of the sampling has been highlighted in this study. Large ensembles would imply lower sampling effects, and a better description of the spatial correlation of extremes. supported by EDF and by the French National Research Agency (ANR) and its program Investissementsd'avenir ⪡ ⪢ under the Grant ANR-11-RSNR-0021, ANR-DADA, the LEFE-Multirisk, CHAVANA, AMERISKA and ExtremeScope projects, and also the National Science Foundation through Earth System Modeling (EaSM) Grant number AGS-1243030. The authors would like to thank the contributors of the statistical freely available software R http://www. rproject. org/. Analyses and graphics have also been done using the NCAR Command Language (Version 6.
In the United States, the incumbents' platforms are therefore considered to be "closed", as opposed to the "open" approach endorsed by the EU. The traditional debate over unbundling concerns whether the benefits of promoting intra-platform competition outweigh a possible reduction in investment incentives when incumbents are required to share their infrastructures. Although the questions tackled in this debate are of key importance to academics, policy makers and market regulators, there has not been much sound empirical analysis by the academic world. The lack of reliable studies is largely due to the paucity of data released by companies and regulators. In this paper, we propose an analysis of the unbundling experience in the United Kingdom, based on two unique data sets: one concerning broadband penetration, made available to us for the purpose of this study by Ofcom (the United Kingdom's communication regulator); and one regarding broadband speeds, obtained from a private company.
From the LE, lines are then further distributed locally to each dwelling where customers live or work, which tend to be within a few hundred meters of the LE. 3  For each one of the 5,500 plus LEs in the United Kingdom, we observe the number of broadband subscribers per operator, that is: BT, the LLU entrants who rent the lines from the incumbent and may invest in upgrades, and the cable operator who utilizes a different platform without being subject to any access obligations. The second data set contains information on broadband speed tests carried out by individuals in 2009. For each test, we observe the operator, the contract option chosen by the user, and the location (post code)-and thus the distance from the LE. We combine both data sets with a third data set on the demographic and geographic characteristics by LE.
Data Sets We combine three different data sets, available at a highly disaggregated geographical level: two unique data sets with information on the number of broadband lines and on broadband speed, and one census data set containing local demographic information. The first data set is provided by Ofcom, and contains the quarterly data supplied to the regulator by BT and Virgin Media over a five-year period from December 2005 to December 2009. Ofcom collects such data for its analysis of the wholesale broadband market. 7 BT is asked to provide, for each LE, all relevant information regarding the wholesale market, that is, the exact number of connections leased to each LLU entrant. Virgin Media is also required to provide figures for the number of subscribers for each of its central offices (the equivalent of LEs in the cable network).
One limitation is that we can only observe BT's total Bit-stream wholesale business; we cannot distinguish, in each LE, between BT's own retail business and the business catered for using Bit-stream technologies. It should be pointed out that the three companies within the BT group (BT Retail, BT Wholesale and Openreach) are separated, and constantly monitored, by the regulator. The second data set consists of information about the quality of broadband services sold across markets. The locus of competition might not be just price, but could also include product improvements, such as increased broadband speed. We therefore supplemented Ofcom's broadband penetration data with information about the characteristics and performances of those broadband packages offered by the incumbent, by the main entrants, and by the cable operator.