dataesr/scientific-paragraphs-categorization huggingface dataset:
The authors thank the TAC of the MPG/ESO-2. 2m telescope for support of optical follow-up with WFI under Max Planck time. Observations were also conducted with ESO NTT at the La Silla Paranal Observatory. This research has made use of SDSS-III data. Funding for SDSS-III http://www. sdss3. org/ has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and DoE. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration. This research has made use of the following databases: the NED and IRSA databases, operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the NASA; SIMBAD, operated at CDS, Strasbourg, France; SZ cluster database operated by Integrated Data and Operation Center (IDOC) operated by IAS under contract with CNES and CNRS.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;.
However, as these data do not necessarily follow consistent standards for defining environmental characteristics and only rarely provide a high level of detail, field audits are often carried out  [17, 18]. In a typical field audit, assessors walk a predetermined route through a specific area and use a scoring form to assess predefined environmental characteristics. In order to save time and resources, many researchers have advocated the use of remote sensing facilities such as Google Street View (GSV) or Bing Maps to perform desk-based assessments of environmental characteristics  (4, 8, 14, 15-17-20). Other advantages of using remote sensing techniques for audits are that they can be performed anytime and anywhere, providing opportunities for large-scale neighbourhood audits without incurring the expense of travel or the risks of working in unsafe neighbourhoods  [4, 8, [18] [19] [20].
A number of studies used GSV to characterize environments  [8, 14, 15, [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28]. The majority of these studies were carried out in the United States, Australia or the United Kingdom, and mainly focused on physical-activity-related environmental characteristics  [8, 14, 15, [17] [18] [19] [20] [21] [22] [23] [24] , determinants of communicable diseases  [25, 28]  or pollution  [26, 27]. These studies indicated an accurate and consistent agreement between the interpretation of GSV data and regular field audits, suggesting that GSV is a valid medium for performing street audits  [4]. Although geographic data on food outlets from other sources (such as Yellow Pages or government sources) could be combined with existing virtual audit tools, the use of a tool that captures different types of environmental factors would enable harmonised data collection in varying settings and across multiple countries.
Additionally, performing a virtual audit in GE/GSV can be combined with the data collection of geographic locations of specific environmental elements, for instance, by simultaneously storing geolocations (coordinates) of places of interest. Such a comprehensive tool would therefore fill a niche for researchers aiming to map potentially obesogenic neighbourhoods, although the validity and reliability have not yet been determined. We developed the SPOTLIGHT-Virtual Audit Tool (S-VAT) as part of the larger EU-funded SPOTLIGHT project  [10]  to assess the obesogenicity of neighbourhoods. This tool is based on items from validated virtual and field audit tools, and is the first tool to combine physical activity and food related environmental characteristics  [12, 22, 24, [29] [30] [31] [32] [33] [34].
Setting In this study, four neighbourhoods that represented a variety in residential area density (RAD) and socioeconomic status (SES) were selected for assessing the validity and reliability of the SPOTLIGHT-VAT. These four neighbourhoods were part of the 12 Dutch urban neighbourhoods located in the 'Randstad', selected for assessment of obesogenic environments in the broader SPOTLIGHT project  [35, 36]. The urban agglomeration 'Randstad' encompasses the four largest Dutch cities and their surroundings in the West of the Netherlands. "Neighbourhoods" were defined according to administrative boundaries as made by the CBS (Statistics Netherlands, www. cbs. nl). Data on RAD were obtained from the Urban Atlas database  [35]. This atlas is a GIS database distributed by the European Environmental Agency, based on a compilation of satellite photographs covering Europe and providing high resolution land use data  [37].
Two classes of RAD were used: high and low residential area density (>80% and < 50% of areas covered by residential buildings, respectively). Socio-economic status data were based on median income data for each neighbourhood from the Netherlands census database  [36]. Two classes of SES were used: high and low SES (corresponding to the first and third tertiles). The combination of the RAD and SES levels allowed the definition of four categories (High RAD/High SES, High RAD/Low SES, Low RAD/High SES and Low RAD/Low SES). For the broader SPOTLIGHT project, three neighbourhoods in each category were randomly chosen. In this validation study, we selected one neighbourhood from each category based on feasibility and proximity to the study site. Furthermore, we made sure that the raters did not conduct audits in the city they lived in.
For a more detailed description of the individual items we refer to Additional files 1 and 2. A specific S-VAT data input form was created using Open Office open source software, with drop-down menu options for all responses (Figure  1 ). The data input form allows storage of images (screen capture) from GE. The form was designed to be viewed alongside the Street View feature in GE using a computer split-screen. To increase homogeneity between audits and to assist in clear and unambiguous scoring, a Standard Operating Procedure (SOP) was developed. The SOP describes information on the definition of street segments as well as the procedures for data extraction, data storage and defining environmental characteristics (Additional file 1).
Criterion validity In order to test the validity of the S-VAT, the virtual audits were compared with field audits for 40 streets in total (ten per neighbourhood). The same data input form and codebook were used as in the virtual audit. The first researcher (JRB) conducted the virtual audit of the same street segments as the field audit, but in reversed order and with an interval of at least ten days between the field and virtual audit. Consequently, the researcher was less likely to be biased by his previous experiences of auditing those street segments.
Statistical analysis The inter/intra-observer reliability and criterion validity were measured by using Cohen's Kappa (k). As low Kappa values can be seen despite reported high levels of agreement  [14, 38] , we also reported the proportions of agreement as described by de Vet et al. [38]. Kappa values of 0. 80-1. 00 were considered to represent high agreement, 0. 60-0. 79 as substantial agreement, 0. 40-0. 59 as moderate agreement, 0. 20-0. 39 as fair and 0. 00-0. 19 as slight agreement  [39]. As sensitivity analysis, we explored whether validity and reliability across neighbourhood types. However, as shown in the Additional file 3: Table  S1 , there was not enough variability in items to calculate accurate validity and reliability statistics between neighbourhood types. All analyses were performed using SPSS (version 22).
2012) , part of the Sloan Digital Sky Survey III (SDSS III  Eisenstein et al. 2011). In  Anderson et al. (2012) , we used reconstruction to provide a 1. 7 per cent distance measurement from the BOSS DR9 galaxies, the most precise measurement ever obtained from a galaxy survey. This measurement benefitted from a simple reconstruction procedure, that used the phase information within the density field to reconstruct linear behaviour and sharpen the BAO  (Eisenstein et al. 2007a ). In  Anderson et al. (2014)  we fitted moments of the anisotropic correlation function measured from the same data, providing distance constraints split into radial and anisotropic directions. We now extend and update the BAO measurements based on the BOSS galaxy samples to the latest dataset from the ongoing BOSS. This paper concentrates on the DR11 data set, comprised of SDSS-III observations through May 2013, which is scheduled for public release in December 2014 together with the final SDSS-III data release (DR12).
The errors derived from this matrix thus better represent the errors on the measured BAO; the data when presented as P (k)/Psm(k) are more independent and provide a more accurate visualisation of the measurements. Fig. 16  displays the measured post-reconstruction values of P (k)/P sm (k), for the BOSS CMASS sample in DR9, DR10, and DR11 (from top to bottom), showing the evolution in the signal-tonoise ratio of the BAO as BOSS has increased its observed footprint. In the DR11 sample, the third peak is clearly visible. In Fig. 17 , we display the DR11 post-reconstruction P (k)/P sm (k) for the two BOSS samples; the CMASS sample at z eff = 0. 57 is presented in the top panel and the LOWZ sample at z eff = 0. 32 is shown in the bottom panel. The LOWZ sample possesses a clear BAO feature, but the signal-to-noise ratio is considerably lower than that of the CMASS sample.
Anisotropic Clustering Estimates In Section 5, we detailed our analysis techniques (multipoles and wedges statistics), and demonstrated they recover un-biased estimates of the BAO scales both along and perpendicular to line-ofsight with similar uncertainties. We now apply these two techniques to BOSS CMASS sample (at z = 0. 57). Fig. 18  displays the multipoles, ξ0,2, of the DR11 CMASS sample correlation function preand post-reconstruction, using our fiducial binning choice, for the range of scales fitted (45 < s < 200 h -1 Mpc). For the quadrupole (ξ2), we see a dramatic change from the pre-to post-reconstruction results, as the reconstruction algorithm has removed almost all of the redshift space distortion contribution. Further, an apparent dip is now seen in the data on scales slightly larger than the peak in the monopole.
We thus turn to a discussion using αparametrization in the following discussion. Pre-reconstruction, the multipoles and wedges measurements in α and differ by less than 0. 25σ as shown in Table  10. Fig. 20  shows that, as reconstruction tightens the constraints from both methods, the central values shift slightly along the axis of constant α by 1. 5 per cent in. When we look at this comparison in our mocks, we find a r. m. s. difference in fits of 0. 007, indicating that the data is a 2σ outlier. 27 of 600 mocks have differences more extreme than ±0. 015. The other three cases (DR10 and DR11 pre-reconstruction) show smaller variations. We conclude that this event is consistent with normal scatter of the two estimators. Our tests on our fitting methodology, presented for the mock samples in Section 5.
Robustness Checks on Data We measure the DR11 post-reconstruction anisotropic BAO scale with various choices of methodology, in order to test the robustness of our anisotropic BAO measurements. Because of the tight correlation between results calculated from fits to either multipoles or wedges (see Section 5. 2), we only present robustness checks for fits to the multipoles. We have only summarized the results of the robustness that are of immediate relevance to this paper here, while the full robustness test of our anisotropic BAO fitting methodology is shown in  Vargas-Magana et al. (2013). We vary the choices when fitting to the data in the same way as we did when testing the results on the mock samples in Section 5. 2. The results are summarized in Table  11  and we can see that the differences in central fitted values when we consider different choices of fitting parameters are impressively small.
1 per cent. We have discussed this further in a companion paper  (Vargas-Magana et al. 2013) , which should be consulted for more details. Finally, it is also interesting to note that a fixed β parameter does not change the error or central values by more than 0. 1 per cent. To conclude, the variations of DR11 post-reconstructed data is well within the scatter predicted when the same varying choices are applied to mock galaxy catalogs.
DR10 Anisotropic BAO measurement Although our default results are for DR11, it is instructive to examine the results from the reduced DR10 data set, which allow us to follow the transition in data quality from our previous DR9 results to our new DR11 results. Consequently we present anisotropic BAO measurements from DR10 alongside the DR11 measurements in Table  10. For the mock catalogues, Fig. 7  showed that, for fits to the DR10 multipoles, the expected improvement of the measurement in α ⊥ and α with reconstruction is from 2. 8 per cent and 5. 4 per cent to 1. 9 per cent and 3. 6 per cent respectively. Using the DR10 data, we measure α ⊥ = 1. 039 ± 0. 024 and α = 0. 956 ± 0. 057 pre-reconstruction. Post-reconstruction, we measure α ⊥ = 1. 037 ± 0. 018 and α = 0. 975 ± 0. 058, showing remarkable consistency with the mock results.
The measurement using wedges, also presented in Table  10 , are similar and consistent. Thus the precision of the BAO measurements from the DR10 data are typical. This can also be seen in Fig. 7 , where the orange star representing the data results is within the locus of the blue circles representing results from the mocks. It is interesting that, for DR10, the error post-reconstruction is slightly larger for α , compared with the pre-reconstruction error. We can see this more clearly by looking at the α and pair preand post-reconstruction in DR10 in Table  10. It seems there is no improvement in σα, while there is some slight improvement in σ post-reconstruction. We compare this to the mocks to try and understand this behaviour. Figure  7  shows that not all mocks in DR10 have improved constraints on α after reconstruction, even though it is uncommon: ≈ 20 out of 600 mocks that do not improve.
(2012 Anderson et al. ( , 2014)). The differences though between the templates are quite small and are further explored in  Vargas-Magana et al. (2013). For comparison, we provide anisotropic fits made using the same "De-Wiggled" power spectrum template used for the isotropic fits in Table  10 , it is not surprising that the anisotropic results with the same power spectrum template provides more similar fits to those from the isotropic fits in both pre-reconstruction and post-reconstruction datasets (this is explored further in  Vargas-Magana et al. 2013). For DR11 post-reconstruction, which is our default choice for making cosmological measurements, we note that the isotropic power spectrum fits give lower values of α than the isotropic correlation function fits, pulling the isotropic consensus values of α down (see Table  9 ).
20 ). Therefore, we choose the anisotropic clustering measurements to be the default measurement of the CMASS measurement in our ) from both approaches. The multipoles provide slightly tighter constraints, the consensus contours follows a more elongated form aligned with the axis of constant α. We also show the central values from fits of D V /r d from isotropic and anisotropic clustering. We note the slight difference between the isotropic and anisotropic constraints on D V /r d and the slight shift in direction of the contours of D A (z) -H(z) compared to D V (z). We note that anisotropic clustering measurements provide stronger cosmological constraints than isotropic clustering measurements. We thus adopt CMASS anisotropic values as our best cosmological dataset (as discussed in both Sec 7.
7 that matches the CMB data varies by about 1 per cent for each 0. 1 in redshift. This would be a 0. 1 per cent shift for an 0. 01 change in effective redshift. Yet this much tilt in the distance-redshift relation is already disfavored by the BAO Hubble diagram and by the supernova data. Hence, we argue that errors in the effective redshift affect our interpretations at below 0. 1 per cent. Similarly, our mocks are based on a single redshift snapshot of the simulations, rather than light-cone outputs that track the exact structure at each redshift. This approach could create errors when we combine a broad redshift bin into one clustering measurement and interpret the acoustic peak as arising from a single, effective redshift. Note that the amplitude of galaxy clustering changes much more slowly than the predicted variation in the amplitude of the matter clustering, which limits the mismatch of combining different redshifts.
Moreover, within the usual class of CDM cosmologies, the CMB data sets tightly constrain r d. For example, the Planck Collaboration (2013b) results imply r d to 0. 4 per cent r. m. s. precision for the minimal ΛCDM model and extensions to spatial curvature and low-redshift dark energy. As this is somewhat tighter than our statistical errors on the α's, it is reasonable to choose a form of the results that emphasizes the absolute measurement of the distance scale. The effective redshift of CMASS is z eff = 0. 57, while that of LOWZ is z eff = 0. 32. Our fiducial cosmology is Ωm = 0. 274, H0 = 70 km s -1 Mpc -1 , Ω b h 2 = 0. 0224, ns = 0. 95, mν = 0 eV, w = -1, ΩK = 0, and σ8 = 0. 8. Using this cosmology, we obtain D V,fid (0. 57) = 2026. 49 Mpc, D A,fid (0. 57) = 1359. 72 Mpc, and H fid (0.
In particular, by fitting the full anisotropic clustering, these analyses are sensitive to the  Alcock & Paczynski (1979)  distortion of the broadband clustering, which gives additional information on the product DA(z)H(z). This requires modeling to separate from the redshift-space distortions. Reid et al. (2012)  model the monopole and quadrupole moments of the redshift-space DR9 correlation function above 25 h -1 Mpc and find DV (0. 57) = (2070 ± 46) Mpc when allowing f σ8, DA and H as free parameters in the fit. Kazin et al. (2013)  also use the correlation function, but fit to clustering wedges rather than the multipoles. They found consistent values. Sánchez et al. (2013b)  also analyzed the correlation function of the DR9 CMASS sample using clustering wedges, fitting to the data above 44 h -1 Mpc, but combined their constraints with those derived from other BAO measurements, CMB and SNe data.
While  Samushia et al. (2014)  uses the model suggested by  Reid et al. (2011 ), Chuang et al. (2013b)  uses a model suggested by  Eisenstein, Seo & White (2006) ,  Crocce & Scoccimarro (2006)  and  Matsubara (2008b). Sánchez et al. (2013b)  analyses the correlation function wedges together with external datasets to constrain a wide variaty of cosmological parameters. We compare the various results in Table  12 , finding good agreement with those of this paper. The agreement on α is close in most cases, while our BAO results differ by about 1 σ when split anisotropically. Perfect agreement is not expected: these analyses are gaining additional information on DA(z)H(z) from anisotropies in the broadband shape, but none of them use reconstruction. Given the difference in these treatments and the range of clustering statistics and template modeling, we are encouraged by this level of agreement.
Anderson et al. (2012)  compared the DR9 CMASS distance measurement to that from the acoustic scale measured by 6dFGS  (Beutler et al. 2011) , WiggleZ  (Blake et al. 2011 ) and from the BAO detections in SDSS-III imaging data  (Padmanabhan et al. 2007; Carnero et al. 2012; Seo et al. 2012). Our DR11 measurement remains in good agreement, within 1 σ, with these studies. The LOWZ measurements may be compared to previous work on the SDSS-II Luminous Red Galaxy sample, which covered a similar area of sky but with fewer galaxies. We find very close agreement with the results of  Percival et al. (2010)  and  Padmanabhan et al. (2012). The survey footprints of these studies overlap substantially, but not entirely, with those of DR11 LOWZ. Moreover,  Percival et al. (2010)  included substantial volume at lower redshift through the SDSS-II MAIN sample  (Strauss et al.
Data Sets and Methodology We next consider the cosmological implications of our distance scale measurements. From BOSS, we consider several different measurements. First, we have the DV (0. 57) measurement from CMASS galaxy clustering in each of DR9, DR10, and DR11. Second, we have the DV (0. 32) measurement from LOWZ clustering in DR10 and DR11. Finally, we have the DA(0. 57) and H(0. 57) joint measurement from CMASS in DR11. In all cases, we use the post-reconstruction consensus values. When not stated, we refer to the DR11 measurement. We adopt the CMASS anisotropic values as our best cosmological data set, labeling this as "CMASS", but also show results for the isotropic fit, labeling this as "CMASSiso". At points, we combine our CMASS and LOWZ measurements with two other BAO detections at different redshifts: the measurement of DV at z = 0.
Comparison of BAO and CMB Distance Scales in ΛCDM Results from the BAO method have improved substantially in the last decade and we have now achieved measurements at a wide range of redshifts. In Fig. 21  we plot the distance-redshift relation obtained from isotropic acoustic scale fits in the latest galaxy surveys. In addition to the values from this paper, we include the acoustic scale measurement from the 6dFGS  (Beutler et al. 2011)  and WiggleZ survey  (Blake et al. 2011; Kazin et al. 2014). As the BAO method actually measures DV /r d , we plot this quantity multiplied by r d,fid. The very narrow grey band here is the prediction from the Planck CMB dataset detailed in Sec. 9. 1. In vanilla flat ΛCDM, the CMB acoustic peaks imply precise measurements of Ωmh 2 and Ω b h 2 , which in turn imply the acoustic scale.
7. Moreover, the best-fit parameters for both the CMB and BAO datasets have shifted between DR9 and DR11. In fact, combining CMASS-DR9 with Planck instead of WMAP7 yields w = -1. 18 ± 0. 25. In that case, the BAO and CMB flat ΛCDM constraints have closer best fit α values. The left panel of Fig. 25  also demonstrates why the CMASS anisotropic constraints are more constraining than the isotropic ones, particularly for dark energy parameters. Variation in w at fixed CMB acoustic scale primarily shifts DA(0. 57), and the anisotropic measurements provide tighter constraints in that direction. Note that none of these extra parameters drive the expansion rate as high as our anisotropic best fit to H(0. 57). In order to explore our results on the full multi-dimensional parameter space in which we derive our cosmological constraints, we now describe the results of our MCMC chains.
Here we use our BAO measurements in combination with CMB results, and supplemented at times by SN data and other BAO measurements, doing the analysis in the context of different cosmological models. We first start by comparing constraints on the parameters Ωmh 2 , Ωm, and H0 from our different BAO datasets in Table  14. In this case we combine BAO with different CMB datasets: Planck, WMAP9, or eWMAP, in the ΛCDM, oCDM, or wCDM cosmological models. We find that all CMB+BAO combinations return similar cosmological fits in ΛCDM and oCDM models, with H0 around 68 km s -1 Mpc -1 , Ωm around 0. 30, and negligible spatial curvature. Somewhat more variation is seen in the wCDM case, because of a degeneracy between w and H0 that is described later in this section. However, these variations are accompanied by larger formal errors and are highly consistent with the ΛCDM fit.