dataesr/scientific-paragraphs-categorization huggingface dataset:
The a priori CH 4 fluxes used in the study are the same as used in  Houweling et al. (2014) , except for the anthropogenic fluxes. We use the v. 4. 2FT2010 version of EDGAR (European Commission, Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency), whereas  Houweling et al. (2014)  use version 4. 1. The a priori CO 2 fluxes come from CarbonTracker, CT2013B  (Peters et al. , 2007) , in which biosphere fluxes are based on the Carnegie-Ames-Stanford Approach (CASA) biogeochemical model, fire fluxes are based on the Global Fire Emissions Database v3. 1 (GFED), and ocean fluxes are based on  Jacobson et al. (2007). Fossil fuel fluxes in Carbon-Tracker are based on the Miller module  (Tans et al. , 2014). The a priori flux covariance matrix is constructed assuming relative flux uncertainties of 50, 84, and 60 % per grid box and month for the total CH 4 , biospheric CO 2 , and oceanic CO 2 categories, respectively.
TCCON TCCON is a global network of ground-based FTS instruments for measuring the total column abundance of several gases, including XCO 2 and XCH 4 , in the near infrared region of the electromagnetic spectrum  (Wunch et al. , 2011). These measurements are the standard for validating total column retrievals from greenhouse-gas-observing satellites such as GOSAT. We validate XCH ns 4 , XCO ns 2 , X ratio , XCO ma 2 , and XCO ct 2 with corresponding values of XCH 4 , XCO 2 , and XCH 4 : XCO 2 measured by TCCON at 12 sites using the GGG2014 release of the TCCON data set (see Fig. 1  and Sect. 3. 1). An albedo-based bias correction was applied to GOSAT-retrieved X ratio to account for the mismatch with TCCON X ratio (see Appendix A).
NOAA High-accuracy surface measurements of CH 4 and CO 2 were used from NOAA's GGGRN  (Dlugokencky et al. , 2015). The standard scale used for CO 2 is the WMO X2007 scale and for CH 4 is the WMO X2004 scale. Only the sites with continuous data coverage (on a roughly weekly basis) without gaps in the time period of 1 June 2009 to 31 December 2010 were included. A total of 8552 CH 4 observations and 7843 CO 2 (see Eq. 2). The numbers represent mean percentage differences with TCCON (weighted with TCCON + GOSAT error). A negative number means that the satellite retrieval is lower than TCCON. Data from these stations were used: Sodankylä  (Kivi et al. , 2014) , Białystok  (Deutscher et al. , 2014a) , Bremen  (Deutscher et al. , 2014b) , Garmisch  (Sussmann and Rettinger, 2014) , Karlsruhe  (Hase et al.
Aircraft measurements Airborne measurements from various aircraft measurement projects were used to test the inversion optimized model (see Sect. 3. 2. 5). The following projects have been used: 3. IPEN aircraft measurements over Brazil (referred as AMAZONICA) from  Gatti et al. (2014). HIPPO provides in situ measurements covering the vertical profiles of CO 2 and CH 4 over the Pacific, spanning a wide range in latitude (approximately pole-to-pole), from the surface up to the tropopause. We used data from the HIPPO 2 (26 October to 19 December 2009) and HIPPO 3 (20 March to 20 April 2010) campaigns. The continuous in situ measurements of CH 4 and CO 2 that were used have been bias corrected with flask-air samples that were collected during each flight and analyzed at NOAA  (Wofsy et al. , 2012b).
GOSAT-TCCON comparison TCCON measurements are used to investigate the errors in GOSAT-retrieved XCH 4. Each term on the right-hand side of Eq. (  2 ) contributes to the uncertainty in XCH proxy 4. To quantify these error contributions, we compare TCCON measurements of X ratio , XCH 4 , and XCO 2 to corresponding co-located GOSAT-retrievals. The validation is carried out for the time period of 1 June 2009 to 31 December 2013, for which both proxy data sets (XCH ma 4 and XCH ct 4 ) are available. Table  1  shows mean differences per TC-CON station, expressed as fractional differences to facilitate the comparison of quantities with different units. As expected, the largest differences between GOSAT and TC-CON are found for XCO ns 2 and XCH ns 4. In general, XCO ns 2 (mean = -1. 57 %) shows larger relative differences than XCH ns 4 (mean = -0.
Assimilation statistics Figure  2  summarizes the statistics of the modelmeasurement comparison. The prior X ratio mismatches typically fall in the range ±1 % (with mean = 0. 007 ppb/ppm and 1σ = 0. 043 ppb/ppm). The inversions reduce the average mismatch by about a factor of 10, and the variation of single column mismatches by about a factor of 2. The XCH proxy 4 of PR-CT and PR-MA have bimodal prior mismatches, because the a priori model overestimates the north-south gradient of CH 4. The bottom panels of Fig. 2  show mismatches between TM5 and surface flask measurements of CH 4 and CO 2. The CH 4 a priori measurement mismatch has a mean of -18. 30 ppb and a 1σ of 42. 30 ppb. The RATIO, SURF, PR-CT, and PR-MA inversions are all able to fit the NOAA data to a similar extent, reducing the a priori differences by more than a factor of 20.
CO 2 flask measurements are assimilated in SURF and RATIO. Both inversions reduce the a priori mismatch (mean = -2. 12 ppm, 1σ = 3. 88 ppm), with RATIO (mean = -0. 04 ppm, 1σ = 3. 69 ppm) fitting the CO 2 flask data as well as SURF (mean = -0. 06 ppm, 1σ = 3. 72 ppm).
The smallest differences are found in the Southern Hemisphere. Figure  6b  shows how this seasonal pattern propagates into the posterior CH 4 fluxes. The seasonal and latitudinal variation in the CH 4 flux difference follows the variation in the XCH proxy 4 difference, with an amplitude of 0. 5 Tg month -1 gridcell -1. The regions without satellite data coverage, i. e. , below 60 • S and above 60 • N, show smaller differences in the optimized fluxes. PR-CT and PR-MA yield different CH 4 fluxes in northern Africa and Australia (see Fig. 4 ). We plot these fluxes with the corresponding regional averaged XCH 4 values in Fig. 7. For northern Africa, the difference in XCH proxy 4 of up to 10 ppb around January 2010 gives rise to a difference in the monthly posterior flux of 1 Tg month -1. In Australia, XCH ma 4 and XCH ct 4 are in relatively good agreement with each other, with differences within 2 ppb.
PR-CT is able to improve the PRIOR κ of 6. 99 to 4. 56, followed in order of decreasing performance by PR-MA (4. 71),  SURF (5. 33), and RATIO (5. 47). The values of κ are larger than 1, which points to significant errors in all the inversion results. The RMSD of the different inversions are comparable. The large data set of CONTRAIL CO 2 measurements covers a much larger area, including flight tracks to Europe and southeast Asia. Our validation shows a mean error of 2. 23 ppm in PRIOR. The NOAA and RATIO inversions reduce this bias to -0. 43 and -0. 41 ppm, respectively. However, similar to the HIPPO validation, MACC (mean bias = -0. 2 ppm) and CarbonTracker-derived CO 2 (mean bias = 0. 11 ppm) fields are in better agreement with the CONTRAIL measurements than the inversions.
3 ppb for SURF, and only 7. 5 to 9. 8 ppb for the satellite inversions. A possible explanation is the double counting of surface data in the satellite inversions, as the satellite data have been bias corrected using an inversion that was already optimized using surface data. However, the bias correction is only applied as a zonal and annual mean. All inversions show similar reductions in the fluxes from eastern temperate Eurasia (mostly China) to match the NOAA measurements. However, the satellite inversions tend to compensate for this flux decrease over China by increased fluxes in India and the central part of temperate Eurasia.
Discussion We have demonstrated that the application of the ratio method to GOSAT data yields realistic solutions for CO 2 and CH 4 fluxes. Its performance is comparable, and may in some regions even be better than the proxy inversion method. This is an important finding because the X ratio retrieval approach provides a useful alternative to the full-physics method in that cloud filtering is less critical. In the case of GOSAT, it increases the number of useful measurements by about a factor of 2  (Butz et al. , 2010; Fraser et al. , 2014). At the same time, the RATIO inversion method avoids using the modelderived CO 2 fields as a hard constraint, which is an important limitation of the proxy method. The realistic performance of the ratio method is certainly not a trivial outcome, since it prompts the user for specification of new uncertainties, influencing the way in which mea- surement information is shared between CH 4 and CO 2.
74 %. An additional correction was applied to X ratio , XCH ct 4 , and XCH ma 4 to account for a bias between NOAA-optimized CH 4 fields in TM5 and TCCONobserved XCH 4 , amounting to -0. 76, -0. 80, and 0. 59 %, respectively. We optimized monthly CH 4 and CO 2 fluxes for the year 2009 and 2010 by assimilating GOSAT-retrieved X ratio data using the TM5-4DVAR inverse modeling system. Additional inversions, assimilating XCH proxy 4 and NOAA surface flask measurements, were performed in a similar setup for comparison. The posterior uncertainties of the fluxes are calculated with a Monte Carlo approach. Overall, the ratio and proxy inversions show similar results for annual CH 4 fluxes. Significant seasonal differences in CH 4 are found between the two proxy inversions for TransCom regions northern Africa and Australia, which can be traced back to differences in XCO model 2.
The CO 2 models show a systematic difference in the seasonal cycle of CO 2 , resulting in a seasonally varying mismatch in the northern tropics. The ratio method has the advantage that it allows adjustment of the CO 2 fluxes, whereas the proxy inversions can only account for this mismatch by adjusting CH 4. For Australia, the proxy inversions predict an anomalous CH 4 increase in the second half of 2010. This difference can be explained by errors in XCO model 2 , which does not account for the anomalous carbon sink reported by  Detmers et al. (2015)  for lack of surface measurement coverage. The ratio method has the built-in flexibility needed to attribute the anomaly to CO 2 instead of CH 4 , and is therefore is not affected. Inversions using satellite data show a better agreement among each other compared to the NOAA-only inversions, which use only surface data.
This is true in particular for temperate Eurasia, where the NOAA-only inversion reduces the annual CH 4 flux by as much as 55 Tg y -1 , relative to an a priori flux of 121 Tg y -1. This is traced back to a large overestimation of atmospheric CH 4 concentration in the prior model at NOAA sites in the region, especially at Shangdianzi, where the prior model overestimates the data by 179 ppb on average. When satellite measurements are assimilated, the CH 4 flux reduction for temperate Eurasia is www. atmos-chem-phys. net/16/5043/2016/ Atmos. Chem. Phys. , 16, 5043-5062, 2016 limited to 21 Tg y -1 , while accounting for the a priori mismatch in Shangdianzi. We validated the inversion-optimized atmospheric tracer fields, as well as the CarbonTracker and MACC CO 2 fields used in the proxy inversions, against three independent aircraft measurement projects.
Several other studies have reported similar biases and applied NOAA-based bias corrections, in addition to the TCCON-derived retrieval corrections in order to restore consistency between the observational constraints provided by surface and total column measurements  (Alexe et al. , 2015; Houweling et al. , 2014; Basu et al. , 2013). We use a similar procedure for X ratio and XCH proxy 4 data by comparing the TCCON-corrected GOSAT retrievals to the NOAA-optimized TM5 model. The mean difference is corrected using a linear function of latitude. This results in a mean adjustment of -0. 76 % in X ratio , -0. 59 % in XCH ma 4 , and -0. 80 % in XCH ct 4 (see Figs. A2  and A3 ). The Supplement related to this article is available online at doi:10. 5194/acp-16-5043-2016-supplement.
Acknowledgements. This work is supported by the  Netherlands Organization for Scientific Research (NWO) , project number  ALW-GO-AO/11-24. The computations were carried out on the Dutch national supercomputer Cartesius, and we thank SURFSara (www. surfsara. nl)for their support. Access to the GOSAT data was granted through the third GOSAT research announcement jointly issued by JAVA,  NIES , and  MOE. The funding for  AMAZONICA  project is provided by  NERC  and  FAPESP. We thank  S. C. Wofsy  for providing HIPPO data. We thank  Debra Wunch  and other TCCON principal investigators for making their measurements available. Edited by:  A. Stohl.
Data availability Level 2 XCH 4 and X ratio data from GOSAT/TANSO are calculated using the RemoTeC algorithm. The data are publicly available from the ESA's Climate Change Initiative website (http://www. esa-ghg-cci. org/). NOAA's GGGRN CH 4 measurements are publically available at ftp://aftp. cmdl. noaa. gov/data/trace_gases/ch4/flask/surface/. CarbonTracker CO 2 fluxes are provided by NOAA ESRL, Boulder, Colorado, USA from the website at http://carbontracker. noaa. gov.
The data collected, in France as well as in Australia through a Clinical Report Form (CRF), will include clinical data, radiological data, (neo) adjuvant therapies, surgical and pathological data, quality of life and distress level. At inclusion, 6 and 12 months, the evaluation of quality of life and distress level will be collected using validated selfreported questionnaires, the MOS SF36  [31] , FACT-C  [32]  and the Distress Thermometer  [33] , respectively. Patients will be followed in clinics according to he habits of each department. Usual follow-up is composed of clinical exam, CEA analysis, CT-scan or Chest Radiography with abdominal ultra-sound every 3-4months during the first three years and every 6 months for the last two years. 2) Blinded inter-country reading of pelvic MRI This experiment will consist of an inter-country reading of patients' pelvic MRIs, "blind" to the other country's decision.
All centres will use the same MRI protocol with an uniform report. The MRI shared will be the one based on which the curative (surgery) or palliative care management decision (no surgery) the treatment decision will be made, that is to say, the MRI done after possible neoadjuvant treatment. Pelvic MRIs will be transferred after anonymization (Dicom Cleaner software®). In the case of medical contraindication to undergo pelvic MRI, the appropriate diagnostic scan will be used to assess the care-decision concordance between both countries. 3) Qualitative analysis Qualitative data will be collected by a research psychologist, based on MDT meeting observation, semi-structured interviews and focus groups of practitioners (surgeon, oncologist and radiologist) involved with the therapeutic decisions of patients with PRC-bTME and LRRC.
Statistical analysis The data will be analysed by the biostatistician of the Methodology and Data Management Centre (Centre de Méthodologie et de Gestion des données -USMR). The analyses will be carried out with SAS® software (version 9. 2 and later versions). The quantitative variables will be described in terms of effective, average, standard deviation and confidence interval at 95 % of the average, median, range and interquartile range. The qualitative variables will be described in terms of effective, percentage and confidence interval at 95 % according to exact binomial law. The risks of first occurrence of events will be estimated by the non-parametric method of Kaplan-Meier and compared with the Log-Rank test. A model of proportional risks from Cox will be used in order to take adjustments into account if necessary.
The log-linear and risk proportionality hypotheses will be systematically verified. For qualitative data, in each country, the interviews, focus groups and verbal interactions during MDT meetings ("real" and "theoretical") will be submitted for thematic categorical content analyses (with the help of the NVivo9 software). This will allow for updating the principal obstacles and facilitators of operatory decisions in the framework of MDT meetings of patients with PRC-bTME and LRRC. The concordance on the indication to the resectability between France and Australia will be estimated by the Kappa coefficient of Cohen. The confidence interval of 95 % of this estimation will be calculated by Fisher's exact method based on distribution.
ATLAS and CMS have performed a large number of searches for physics beyond the Standard Model (BSM). The results are typically presented in the context of Simplified Model Spectra (SMS), containing only a few new particles with fixed decay branching ratios, yielding generic upper limits on the cross section as a function of particle masses. The interpretation of these limits within realistic BSM scenarios is non-trivial and best done by automated computational tools. To this end we have developed SModelS, a public tool that can test any given BSM model with a Z 2 symmetry by decomposing it into its SMS components and confronting them with a large database of SMS results. This allows to easily evaluate the main LHC constraints on the model. Additionally, SModelS returns information on important signatures that are not covered by the existing SMS results.
This may be used to improve the coverage of BSM searches and SMS interpretations. We present the working principle of SModelS, in particular the decomposition procedure, the database and matching of applicable experimental results. Moreover, we present applications of SModelS to different models: the MSSM, a model with a sneutrino as the lightest supersymmetric particle and the UMSSM. These results illustrate how SModelS can be used to identify important constraints, untested regions and interesting new signatures.
Introduction Simplified Model Spectra are an effective Lagrangian description, containing a limited set of new particles with fixed decay modes. The free parameters of Simplified Models are just the masses of the new particles, the decay branching ratios typically being set to 100%. A large number of Run 1 LHC searches for new physics have been interpreted in terms of SMS, giving upper limits on the production cross section as a function of the masses of the new particles in the SMS (typically in 2-dimensional mass planes). The interpretation of these limits in terms of complete BSM models is, however, not trivial. SModelS  [1, 2]  is an automated tool that decomposes a realistic model into SMS components and matches them to corresponding results in the database. The decomposition procedure is generic and can be used for any BSM model presenting a Z 2 symmetry.
Using this approach, one can test theory predictions against experimental results without running a Monte Carlo simulation. Another advantage is that SModelS comes with a large internal database and it is easy to add new results. The public database of SModelS v1. 0 includes 21 ATLAS and 41 CMS SUSY search results at 8 TeV. In addition, SModelS can identify signatures which are not covered by the SMS results in the database, labeled as "missing topologies". SModelS is a public tool, the program is available for download at http://smodels. hephy. at.
Alter- natively, if each stop decays via a chargino, t → b χ± , χ± → W χ0 1 , the topology is described as [[[b],[W]],[[b],[W]]]. Given this abstract notation of topologies, an input model specified e. g. in an SLHA file can be decomposed into SMS topologies. For each topology SModelS keeps track of the masses of the new particles involved and calculates a weight σ ×BR. All SMS components contributing to the same experimental result are combined, and the corresponding sum of weights can then be compared directly against the upper limit for the given mass combination. This procedure is illustrated in Fig. 1. A more detailed explanation can be found in  [1]. A topology which does not match any of the experimental results in the database is considered a missing topology.
Literature Search A comprehensive literature search of 3 data bases (PubMed, Ovid MEDLINE, and Ovid EMBASE) was designed and conducted by an experienced librarian with input from the authors. The keywords "intracranial aneurysm," "cerebral aneurysm," "aneurysm," "wide-neck," "bifurcation," "WEB device," and "intraluminal flow diverter" were used in both "AND" and "OR" combinations. The search was limited to articles published from January 1, 2010, to October 1, 2015, and was supplemented by contacting experts in the field for any additional studies. All studies reporting patients with aneurysms treated with the WEB device were included. Inclusion criteria were the following: 1) English language, 2) study reporting a consecutive series of aneurysms treated with the WEB device, and 3) series of at least 5 patients reporting angiographic and/or clinical outcomes.
Outcomes and Data Extraction For each study, we extracted the following baseline information: number of patients, aneurysm rupture status, patient sex, mean age, use of core lab in assessing angiographic outcomes, number of aneurysms treated, mean aneurysm size, mean aneurysm neck size, length of clinical follow-up, and length of angiographic follow-up. Outcomes studied included immediate and midterm (Ͼ3 month) angiographic outcomes (complete occlusion and adequate occlusion, which was defined as complete occlusion or neck remnant), aneurysm retreatment, intraoperative rupture, perioperative morbidity and mortality, thromboembolic complication, and treatment failure. Treatment failure was defined as a failed attempt at WEB device placement. Outcomes were calculated for the overall patient population with subgroup analysis by rupture status (ruptured versus unruptured).
2012c; Piotto et al. 2015). Photometric errors for the three UV filters are estimated via rms deviations from frame to frame. Uncertainties in the visual filters are derived from artificial star tests completed by the ACS Treasury Survey team. Pixel position errors are used as a probe of proper motion, and stars with large frame-to-frame position errors are removed in order to clean the photometry of the most obvious non-cluster stars. Photometric quality flags are used to remove stars with poor photometry (see  Piotto et al. 2015  for further processing details). We also remove horizontal branch (HB) stars from the photometry. Although the HB could provide valuable constraints, being particularly sensitive to changes in helium, HB models are not currently available in the Dartmouth Stellar Evolution Database (DSED;  Dotter et al.
2016; Wagner-Kaiser et al. 2016). We use the newly extended BASE-9 software to sample the joint posterior distribution of age, extinction, distance to a cluster, helium values for populations of stars in the cluster, and the percentage of stars in the first population. A large grid of isochrones is used to interpolate between models and we are able to achieve high-precision estimates for each parameter. The details and initial application of this methodology are established and explored in  Stenning et al. (2016)  and  Wagner-Kaiser et al. (2016). The software is available as open source code from GitHub (https://github. com/ argiopetech/base/releases) and via executables through Amazon Web Services. Installation and instruction may be found in the user manual for BASE-9 (von Hippel et al.
HST obtained at the  Space Telescope Science Institute , which is operated by the  Associations of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555. These observations are associated with program GO-13297. 23-A. This material is based upon work supported by the  National Aeronautics and Space Administration  under Grant  NNX11AF34G  issued through the  Office of Space Science , and through the  University of Central Florida's NASA Florida Space Grant Consortium. DS work was supported by  NSF  grants  DMS 1208791. DvD was partially supported by a  Wolfson Research Merit Award  ( WM110023 ) provided by the  British Royal Society  and by  Marie-Curie Career Integration  (  FP7-PEOPLE-2012-CIG-321865 ) and  Marie-Skodowska-Curie RISE  ( H2020-MSCA-RISE-2015-691164 ).