dataesr/scientific-paragraphs-categorization huggingface dataset:
Salinity innovation The same calculations are performed for salinity as for temperature. Figure  15  is a time series of the RMS of salinity profile innovations for the last 6 months of experiments. Run-Ref, Run-Argo2, Run-NoArgo and Free Run time series are represented here. The RMS error is greatest at the surface and decreases with depth. From 0 to 2000 m the more salinity data are assimilated the closer to the observation the forecasts become. There is no significant increase of the innovation RMS during the 6-month experiment for Run-Ref and Run-Argo2 as there is in Run-NoArgo and Free Run. This increase becomes visible at around 300 m. This result demonstrates the importance of Argo observations for constraining salinity in the PSY3 system. Figure  16a  shows the global mean absolute and normalized profiles of the RMS of salinity innovations for the different experiments.
In the 0-300 m layer, the RMS innovation improvement depends on the quantity of Argo data assimilated by the system. Figure  16b  shows that the RMS innovation is reduced by 20 % when the first half of Argo profiles is assimilated, compared to the RMS innovation without Argo data assimilated. The assimilation of the second half of the Argo data set reduces it by a further 5-10 % relative to the best scores (Run-Ref in blue). In the 700-2000 m layer, the increase of the quantity of Argo data assimilated, together with SLA and SST, induces a decrease of the RMS misfit in salinity. This again shows the need for a good coverage of in situ profiles to estimate a coherent T and S 3-D correction from data assimilation of SLA and SST. The improvement corresponding to the assimilation of 50 % of the Argo array is from 20 to 40 % in that layer, compared to Run-NoArgo.
SST and SSH innovations To validate the assimilation process, we briefly look at the impact of the assimilation of temperature and salinity profiles on other model-forecasted variables. It allows checking the physical consistency of the increment with the model physics. An "unbalanced" increment will destroy model equilibriums. The global RMS of the SST innovations does not differ in the simulations with and without Argo data assimilated. The mean RMS is close to 0. 6 • C over the last 6 months of the OSE experiments. The temperature in the surface layers of the PSY3 system is highly constrained by the assimilation of the Reynolds SST maps. This can be seen in the RMS in-V. Turpin et al. : How essential is Argo for ocean forecasting? novation profiles of in situ observations that are small and close to the surface even when no Argo observations are assimilated.
It also shows that globally the surface temperature observations from SST and in situ observations are coherent. The global RMS of the SSH innovations also does not differ significantly between the OSEs with and without assimilated Argo T and S profiles. They stay close to 7 cm in RMS. Although the assimilation of in situ observations leads to a better 3-D estimation of the T and S fields, it does not change globally the fit to the SSH. Regional incoherencies were identified in our system between the information on dynamical height brought by the altimetry referenced to a MDT and the in situ data set. The projection of the altimetry data at depth on T and S fields is changed when Argo data are assimilated. Further investigation is required to study the complementarity of the in situ and SLA data set in our system and possible incoherencies.
Conclusion Observing system experiments were carried out with the Mercator Ocean 0. 25 • global ocean system to quantify the impact of Argo data assimilation. We considered the effect of Argo data assimilation on the 0-2000 m layer, focusing on the 0-300 m layer and on the 700-2000 m layer, where Argo observations are almost the only in situ observing system. The different OSEs cover the year 2012. The quality of the 3-D temperature and salinity analyses without Argo observations was first assessed. This highlighted the system's weaknesses when only SST, SLA and non-Argo in situ data are assimilated. Without Argo data assimilated, large errors are found in the western boundary currents, Antarctic Circumpolar Current, the Mediterranean and Red Sea outflows and in the tropics. The effect of Argo data assimilation was then assessed through the comparison of the analyzed temperature and salinity fields over the last 6 months from the different experiments.
Finally, we evaluate the impact of the Argo profile assimilation in the PSY3 forecasting system by computing the RMS differences between in situ observations and forecast fields for the different OSEs. Through Argo assimilation, the differences between observation and forecast fields are reduced by about 20 % in the 0-300 m layer and by between 20 and 65 % in the 700-2000 m layer. Results at depth show the importance of the global spatial coverage of Argo assimilation for constraining the temperature and salinity 3-D correction, deduced from data assimilation of SLA and SST, for more realistic results. We show that the changes seen in the analyzed PSY3 temperature and salinity fields when Argo is assimilated correspond to an improvement of the analysis and forecast fields in terms of innovation and residuals to in situ observations.
This shows the ability of the data assimilation system to take advantage of the Argo observations. The progressive improvement of the system's forecasting skills from assimilation of half of the Argo array to the full Argo array also indicates that all observations are needed to constrain our system. These results highlight the major importance of Argo data assimilation for operational oceanography. A decrease in the existing coverage of the Argo array would lead to a degradation of the PSY3 global ocean analysis and forecasts. Finally, it is important to bear in mind that results from OSEs depend on the modeling and data assimilation system used. Our study takes place in the context of short-term realtime ocean analyses and forecasts. The conclusions could not be generalized to other ocean reanalyses without further investigation.
The impact of Argo data assimilation on the other model variables also has to be further investigated. Here we focus on the impact of Argo observations on the reconstruction of 3-D temperature and salinity fields. Furthermore, we did not keep any in situ independent data sets as we stayed close to the real-time PSY3 system. General statements about observing systems should only be made with caution unless consistent results based on several systems are obtained. This is the approach promoted by the GODAE OceanView OSE/OSSE (observing system experiment/observing system simulation experiment) task team.
Acknowledgements. The research leading to these results received funding from the European  FP7 program  under the  E-AIMS  project. The authors would like to thank  Jean-Michel Lellouche  and  Olivier Le Galloudec  for providing support on the use of the operational system,  Charly Régnier  for providing the diagnostic tools and  Gilles Garric  and  Yann Drillet  for comments on an earlier version of the manuscript. The altimeter products were produced by  Ssalto/Duacs  and distributed by Aviso with support from  CNES  (http://www. aviso. altimetry. fr/). MDT_CNES-CLS09 was produced by the  CLS Space Oceanography Division  and distributed by Aviso, with support from  CNES  (http://www. aviso. altimetry. fr/). Edited by:  P. Chapman Ocean Sci. , 12, 257-274, 2016 www. ocean-sci. net/12/257/2016/V.
Vocalization Extractions Vocalizations from 250 h of recording were automatically extracted using in-house software. These programs were written in python (http://www. python. org) by authors H. A. S. and M. S. A. F using open-source libraries. This software accuracy was validated and used in previous studies  (Elie et al. , 2011; Perez et al. , 2015). Vocalization detection was a pipeline of three stages. The first process was a simple threshold-based sound detection based on a high-pass filtered energy envelope (1024 samples FFT; 441 Hz sampling; cut-off frequency: 500 Hz). During the second stage, each sound whose peak was extracted was reconstructed by exploring the two sides of the sound and keeping area with energy higher than 10% of the peak. Thus, each event was either lengthened or shortened to obtain the same amplitude range during the event.
Stastistics All statistical tests were performed using R software (R Core Team, 2014). Linear mixed models were built with the lmer function (lme4 R package), and generalized mixed models were built with the glmer function (lme4 R package)  (Bates et al. , 2014). Models outputs from "Anova" (car library)  (Fox and Weisberg, 2011)  and "summary" functions are presented.
Model Validation Before being interpreted each model was checked, paying particular attention to its residuals. For generalized linear models with a Poisson family, overdispersion was tested with the "overdisp. glmer" function of the "RVAideMemoire" package  (Hervé, 2014) , and if the model presented overdispersion we used a negative binomial family. The model validity was also checked with the plotresid function from the "RVAideMemoire" package before interpreting the model results.
Cross Correlation First we built a model in order to compare the cross-correlation between group types (2Juv2Ad and 4YAd): cc∼GroupType * Sex1Sex2 + Dist + Dist : GroupType +Dist : Sex1Sex2 + (1|GroupID) + (1|Day) + (1|Bird1ID) +(1|Bird2ID) + (1|StartTime) The distance between two birds could be 1 or 2 (1: birds were on the same edge of the square, 2: birds were placed on the diagonal). The factor Sex1Sex2 had three levels: FF, MM, or FM and represented the sexes of both birds from which we computed the cross-correlation. As the interaction between the group type and the sex was significant we first separated the dataset by group type and analyzed them separately: We then separated the dataset by sexes to assess the difference between the cross-correlations of two juveniles and two young adults.
Markov Analysis We first built a model to compare the maximum transition probabilities between group types (2Juv2Ad and 4YAd): MaxProba ∼ GroupType * Sex1Sex2 + Dist + Dist : GroupType + Dist : Sex1Sex2 + (1|GroupID) + (1|Day) + (1|Bird1ID) + (1|Bird2ID) + (1|StartTime) As the interaction between GroupType and Sex1Sex2 was significant we analyzed the group types separately, as we did for Model statistical results are shown. Linear mixed effect models ("lmer" function from "lme4" R package) were built. Number of observations in the dataset for each fixed effect is given. We present the results from the R "summary" function. the cross-correlation. Juveniles only : MaxProba ∼ Sex1Sex2 * Dist + (1|GroupID) + (1|Day) + (1|Bird1ID) + (1|Bird2ID) + (1|StartTime).
Generalized linear mixed effect models with negative binomial family ("lmer" function from "lme4" R package) were built. Number of observations in the dataset for each fixed effect is given. We present the results from the R "summary" function. because males gave 24. 8% more vocalizations than females. Second, the two adults with the juveniles were always two adult females. Juvenile males may vocalize more than juvenile females in the presence of adult females (and not adult males). A previous study analyzed the response of zebra finch juveniles (aged of 56. 5 ± 2. 4 days) to the playback of calls of familiar adult females  (Mulard et al. , 2010). However, the authors found no difference between the sexes in their response to adult female calls (number of calls and latency of response). Still, the vocal response to a playback and to real vocal interactions is probably different.
However, cross-correlation and maximum transition values were similar between young adults in the second phase and adult females in the first phase, even though not all young adults were familiar with each other. Furthermore, we did not observe an increase in average cross-correlation or maximum transition values between the first and second recording days per phase, although all four birds were presumably becoming more  Model statistical results are shown. Linear mixed effect models ("lmer" function from "lme4" R package) were built. Number of observations in the dataset for each fixed effect is given. We present the results from the R "summary" function. familiar with each other as they remain together in the same room. Overall, familiarity is therefore unlikely to fully explain our results.
ACKNOWLEDGMENTS This work was supported by an  ANR  grant to CV ( French Agence Nationale de la Recherche , project " Acoustic Partnership "), a  joint NSF/ANR e CRCNS grant  " AuComSi " for MF and HS, an  IUF grant  to  CV (Institut Universitaire de France). We are grateful to  Colette Bouchut  and  Nicolas Boyer  for their help at the  ENES lab. We thank  Ingrid Boucaud  for the zebra finch drawing. MM was supported by a  Fyssen Foundation  and an Alfred Deakin Postdoctoral Research fellowships.
The representative sequence of each OTU was assigned the taxonomic classification with the Ribosomal Database Project Classifier with a minimum bootstrap threshold of 80%. Rarefaction curves were generated with Faith's phylogenetic diversity (PD_Whole tree), Chao 1 and Observed Species method in QIIME. Raw data from 127 samples generated by  McNulty et al. , (2011)  were obtained and processed in QIIME v 1. 8 (OTU were picked against the May 2013 Greengenes database) in order to generate taxonomy profiles and beta diversity matrixes. Lactococcus relative abundance was calculated as the average of the Lactococcus-assigned OTUs through the FMP administration period (that is, weeks 5, 6, 8, 11 as per the McNulty et al. , study) or wash-out periods (that is, weeks 13, 15 as per the McNulty et al.
Owing to the poor recovery of S. thermophilus DNA, the dynamics of this strain could not be further investigated. We thus focused on the other FMP strains. We quantified the abundance of the FMP species during the FMP intervention and observed a high intra-individual variability (that is, within a two-log range) when assessed at four time points (Supplementary Figure  S1 ). These data suggest that fecal abundance of the FMP strains during FMP administration could not be a reliable marker of colonization resistance. Subsequently, we focused on the elimination kinetics of the FMP species during the wash-out period. We verified that the GI transit time was comparable for all animals by measuring the fecal abundance of G. stearothermophilus spores, a commonly used experimental GI transit marker.
Acknowledgements We thank  Jean-Christophe Piard  for kindly sharing the pDelA plasmid,  Chloë Béal  for her technical help,  Nathan McNulty  and  Tanya Yatsunenko  for generously sharing clinical study data,  Timothy Swartz  for his critical reading of the manuscript. We thank  Thierry Haddad ,  Sacha van Hijum  and  Miaomiao Zhou  for their kind contribution to the LocateP analysis of our strain (Zhou et al. , 2008). We wish to thank  T Meylheuc  (INRA-MIMA2 platform: www. jouy. inra. fr/mima2)for SEM analysis. The quantitative reverse transcription PCR used was operated under Yakult License (YIFSCAN technology).
e. , the volume fraction of spherical particles  (Dubovik et al. , 2000a (Dubovik et al. , , 2002a (Dubovik et al. , , 2006;; Sinyuk et al. , 2007). A series of assumptions are made to perform the inversion of those parameters. They can be found in the AERONET version 2 inversion product descriptions  (AERONET, 2016). All the inversion products spectrally resolved are given at 440, 675, 870 and 1020 nm. The data shown in this work are based on AERONET level 2. 0, cloud-screened and quality-assured data  (Smirnov et al. , 2000)  inverted with the AERONET version 2. 0 retrieval algorithm  (Holben et al. , 2006). All AERONET data are downloaded from the AERONET web page at http:// aeronet. gsfc. nasa. gov/. In practice, the main differences between version 1. 0 and version 2. 0 are the additional criteria applied in version 2.
Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of twopopulation clusters, and also show model misspecification can potentially be identified. As a proof of concept, we analyze the two stellar populations of globular cluster NGC 5272 using our model and methods. (BASE-9 is available from GitHub: https://github. com/argiopetech/base/releases).
To recover star cluster parameters from photometric data, BASE-9 includes sophisticated MCMC routines for model fitting. BASE-9 is available as open source code from GitHub (https://github. com/argiopetech/base/releases), and is also available as executables through Amazon Web Services. Additional technical details can be found in the  BASE-9 Manual (von Hippel et al. 2014). For main sequence and red giant stars, BASE-9 gives users a choice of the state-of-the-art models by  Dotter et al. (2008, and updated  at http://stellar. dartmouth. edu/∼models/) and the com-monly used models of  Girardi et al. (2000)  and  Yi et al. (2001). Other models are available for white dwarfs, as well as for the initial-final mass relations that bridge the stages of stellar evolution. These models are not pertinent to the current discussion because our analyses of two-population globular clusters are limited to main sequence through red giant branch stars.
Notation For each star in a dataset we obtain calibrated photometric magnitudes using at least two filters. Following  DeGennaro et al. (2009 ), van Dyk et al. (2009)  and  Stein et al. (2013) , we refer to the observed photometric magnitude in filter j for star i as x ij for j = 1,. , n and i = 1,. , N , where N is the number of stars in the dataset and n is the number of filters. The observed photometric magnitudes for star i are tabulated in the column vector X i = (x i1 ,. , x in ) , and the known (independent) Gaussian measurement errors in the (diagonal) variance-covariance matrix, Σ i. As discussed in Section 1, our statistical model is based on a hierarchy of parameters. We refer to the parameters that are common to cluster stars-specifically age, metallicity, distance, and absorption-as cluster parameters.
2009 ). For the cluster parameters, Θ, we incorporate ancillary information to specify informative (i. e. narrow) prior distributions when available, and use relatively diffuse prior distributions when such information is lacking. In particular, for θ [Fe/H] , θ m-M V , and θ A V , we use Gaussian prior distributions (truncated to be positive in the case of θ A V ), with means set according to previously published values from independent datasets and standard deviations chosen to be reasonably large. For age, θ age , we use a uniform prior distribution truncated to the reasonable range of 1 Gyr to 15 Gyr, which includes all Galactic globular clusters. Because the population parameters, Φ = (φ Y 1 , φ Y 2 , φ p1 , φ p2 ), are the primary parameters of scientific interest, we use uniform prior distributions subject to physical constraints on their ranges.
This is illustrated in Figure  2 , which displays the matrix of two-dimensional scatterplots of 25,000 posterior draws from P (Θ, Φ|X). (The data we used to construct these plots are photometric magnitudes from NGC 5272. Details are provided in Section 5. ) Based on results in  Gelman et al. (1996) , the optimal proposal distribution for a Gaussian posterior distribution with a d-dimensional covariance matrix Υ is itself a Gaussian distribution with covariance matrix (2. 38) 2 /d Υ. Because the actual form of the posterior distribution is unknown, for iteration l + 1 we use a multivariate t proposal distribution with 6 degrees of freedom † † , centered at the current value of (Θ, Φ) and with scale equal to (2. 38) 2 /7 Ξ (l). Here, Ξ (l) is the empirical variance-covariance matrix of {(Θ (1) , Φ (1) ),.
Age The efficiency of our AM algorithm is demonstrated in Figure  3. There, we compare the performance of our AM algorithm to that of a standard Metropolis algorithm for sampling the same posterior distribution. The standard Metropolis sampler is identical to the AM sampler except Ξ (l) is fixed at Ξ (1) throughout. Both algorithms are implemented with the same starting values, the same tuning period, and use the same data as in Figure  2. The proposal distribution in the AM algorithm begins adapting at iteration 1001, after which there is an obvious difference in performance between AM and standard Metropolis. Standard Metropolis struggles as the MCMC chain repeatedly becomes stuck. While AM also sticks initially, adapting the proposal distribution quickly frees the chain and leads to increased efficiency.
ANALYSIS OF NGC 5272 In this section we apply our method to photometric observations of NGC 5272 in order to provide a proof of concept. Our main objective is to estimate the difference in helium abundance between the two postulated stellar populations, as well as the proportion of stars in each. A secondary objective is to evaluate the underlying stellar evolution model by examining how well the fitted models agree with the observed data. We are of course also interested in estimating the other cluster parameters. The observed data are HST UVIS photometry  (Piotto et al. 2015)  in five filters per star: F 275W , F 336W , F 438W , F 606W , and F 814W. A detailed description of the data collection and processing appears in  Piotto et al. (2015). The data for NGC 5272 consists of 179,330 observed stars; its CMD appears in Figure  6.
Because accurate photometric errors are not yet available for this UV photometric dataset, we construct approximate errors using the HST exposure time calculator and adopt a conservative minimum error of 0. 01 magnitude. As with our simulated clusters in Section 4, the errors are a function of both filter and wavelength. Additional discussion is provided in  Wagner-Kaiser et al. (2015). For model fitting we assume all stars are singletons, which saves significant computation time and should offer a reasonable approximation because the expected percentage of binaries is only about 5%  (Milone et al. 2012b). The prior distributions for θ [Fe/H] , θ m-M V , and θ A V we use are θ [Fe/H] ∼ N (-1. 5, 0. 05 2 ), θ m-M V ∼ N (15. 07, 0. 05 2 ), and θ A V ∼ T N (0. 031, 0. 01 2 ; 0), where N (µ, σ 2 ) is a Gaussian (i.
We find that the four separate chains quickly converge to the same apparent stationary distribution. In addition to estimating the difference in helium abundance in NGC 5272, a secondary objective is to examine the model fits and investigate properties of the underlying stellar evolution model. In general, we find that the fitted models do not agree with the observed data in CMDs involving the F 438W filters. This disagreement is perhaps not surprising as model development follows observations, and data are only recently available in some of these HST passbands. While we cannot conclude solely on the basis of our analysis of NGC 5272 that mismatch between fitted models and observed data is due to systematic errors in the computer model, further examination is warranted. If this pattern persists with additional clusters and with verified photometric errors it may be that the morphologies in the computer model differ systematically from those in observed data; such a discrepancy has been discussed for fainter main sequence stars  (van Dyk et al.
This material is based upon work supported by the  National Aeronautics & Space Administration  under Grant  NNX11AF34G  issued through the  Office of Space Science. In addition, this project was supported by the  National Aeronautics & Space Administration  through the  University of Central Florida's NASA Florida Space Grant Consortium. DS was supported by  NSF  grant  DMS 1208791  and the  European Research Council  via an  Advanced Grant  under grant agreement no. 321323-NEOGAL. 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 ) Grants both provided by the  European Commission.