dataesr/scientific-paragraphs-categorization huggingface dataset:
The right three columns show the number of parameters, out of 12, for which the values assumed in constructing the synthetic data are within the ±1σ, ±2σ, and ±3σ credible regions. The PT-emcee results are consistent with statistical expectations (see Appendix A for the full posteriors). In contrast, the credible regions for both MultiNest runs are too narrow, although with more live points the sampling is more thorough. boundary, and thus that this boundary does not bias our radius estimates. When we explore the implications of results for the EOS in Section 5, we include information from nuclear theory and previous measurements.
Fits to Synthetic NICER and XMM-Newton Data Joint analyses of NICER and XMM-Newton data have not previously been performed in the context of neutron star mass and radius estimation. It is therefore important to demonstrate that our method yields the expected results when similar synthetic data are analyzed, because for synthetic data we know the parameter values used to construct the data and can therefore judge the quality of the fit. To generate the data we started from a good model of the PSR J0740+6620 NICER and XMM-Newton data, including all backgrounds. We then produced the synthetic data by drawing integer numbers of counts from our (in general non-integer) model expectation in each phase-channel bin for NICER, and in each energy channel for each of the three XMM-Newton cameras. We emphasize that this leads to synthetic data with fully realistic backgrounds; previous synthetic data analyses (e.
01 is better but the credible regions are still too narrow: 1 of the 12 true parameter values is excluded at > 3σ, and 4 of the 12 are excluded at > 2σ. It is possible that, with more live points and/or lower efficiency, MultiNest analysis of our synthetic data would lead to the statistically expected outcomes, but results such as these form part of our motivation to use PT-emcee to determine posterior probabilities. In summary, even with realistic backgrounds and even when analyzing jointly the NICER and XMM-Newton synthetic data, our analysis method yields statistically acceptable results. In this section we present fits to (1) just the NICER data (see Table  6  in Appendix B for the best fit and uncertainties, and the left hand panels of Figure  1  for the mass and radius posteriors), (2) the NICER data and the data from the three XMM-Newton instruments, assuming the nominal calibration for XMM-Newton (see Table  7  in Appendix C for the best fit and uncertainties, and the right hand panels of Figure  1  for the mass and radius posteriors), and (3) the NICER data and the data from the three XMM-Newton instruments, with an additional energy-independent factor 0.
ACKNOWLEDGMENTS This work was supported by NASA through the NICER mission and the Astrophysics Explorers Program. The authors acknowledge the University of Maryland supercomputing resources (http://hpcc. umd. edu) that were made available for conducting the research reported in this paper. The authors also thank Felix Fuerst for facilitating the XMM-Newton director's discretionary time observations of PSR J0740+6620, and Brad Cenko for early Swift observations. MCM thanks the Radboud Excellence Initiative for supporting his stay at Radboud University, and thanks Gordon  Baym, Cecilia Chirenti, and   Facility: NICER  (Gendreau et al. 2016) , XMM-Newton  (Strüder et al. 2001; Turner et al. 2001) , Swift  (Gehrels et al. 2004)  Software: emcee (Foreman-Mackey et al. 2013), MultiNest  (Feroz et al.
Sanjay Reddy for comments on an earlier version of this manuscript. WCGH appreciates use of computer facilities at the Kavli Institute for Particle Astrophysics and Cosmology and acknowledges support through grant 80NSSC20K0278 from NASA. TTP is a NANOGrav Physics Frontiers Center Postdoctoral Fellow funded by the National Science Foundation award number 1430284. IHS is supported by an NSERC Discovery Grant and by the Canadian Institute for Advanced Research. Support for HTC was provided by NASA through the NASA Hubble Fellowship Program grant #HST-HF2-51453. 001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc. , for NASA, under contract NAS5-26555. SMM thanks the Natural Sciences and Engineering Research Council of Canada for funding.
g. Nussey et al. , 2008). We investigated within-and among-individual variation in three innate immune parameters-haemagglutination titre, haemolysis titre and haptoglobin concentration-in an extensively studied population of common terns Sterna hirundo with the aim of offering new insights into immunosenescence using a longitudinal analytical approach. We repeatedly sampled individuals of known sex and age across 11 years and compiled a dataset comprising 1,023 measurements from 396 individuals, sampled between 1 and 10 times (mean = 2. 67, Appendix 1, Figure  S1 ), at ages ranging between 3 and 27 years (mean = 10. 2, Appendix 2, Figure  S2 ). We analysed this dataset using random regression models to (a) quantify how immune K E Y W O R D S ageing, immunity, immunology, innate immune system, natural antibodies, ontogeny parameters vary among individuals, both overall and in relation to sex and (b) describe within-individual age-specific changes in, and potential trade-offs between, immune parameters.
To do this, one should ideally formulate models that include age, as well as the biological terms 'age at first reproduction' to assess selective appearance and 'age at last observation' to assess selective disappearance (van de  Pol & Verhulst, 2006). In our case, because only 141 out of 396 birds can be assumed to have died (because they were not observed in the colony for >1 year;  Bouwhuis et al. , 2015) , we used an alternative approach and further checked the conclusions drawn from the final models including the average and delta age components by formulating models that included age, age at first measurement (AFM) and age at last measurement (ALM) of immune parameters as explanatory variables. These models test for selective appearance in, and selective disappearance from, the immunological dataset, respectively, while also testing the average within-individual age effect (van de  Pol & Verhulst, 2006) , which was our main objective.
, 2011). We assessed whether models with both random intercepts and random slopes better fitted the data than models with random intercepts only using the Akaike information criterion (AIC;  Burnham & Anderson, 2002). All univariate analyses were performed in R 3. 6. 1 (R Core Team, 2014) using the function 'lmer', implemented in the package lme4  (Bates et al. , 2015)  with restricted maximum likelihood estimates of the parameters. Assessment of model assumptions was performed by checking the residuals' independence, homogeneity and normality, and testing for temporal autocorrelation. We used the 'sim' function from the r-package arm to simulate values from the posterior distributions of the model parameters  (Gelman & Su, 2020). Based on 5,000 simulations, we extracted 95% credible intervals (CI) around the mean.
We also modelled initial lysis and pre-scan absorbance as fixed effects associated with haemagglutination titre and haptoglobin concentration, respectively. We initially fitted an unstructured (co)variance matrix for the three random effect components: bird identity, year of sampling and residual components. Bivariate models were fitted using a Bayesian framework with the r-package mCmCglmm  (Hadfield, 2010)  in R 3. 6. 1. For all models, we used parameter-expanded priors  (Hadfield, 2010). The number of iterations and thinning interval were chosen to ensure that the minimum MCMC effective sample sizes for all parameters equalled 1,000. Burn-in was set to a minimum of 5,000 iterations. The retained effective sample sizes yielded absolute autocorrelation values lower than 0. 1 and satisfied convergence criteria based on the Heidelberger and Welch convergence diagnostic  (Heidelberger & Welch, 1981).
| Among-and within-individual correlations between immune parameters Our bivariate model revealed that there was no correlation between haemagglutination titre and haptoglobin concentration at the among-individual level (R among = -0. 02, 95% CI = -0. 78 to 0. 72; Appendix 6, Table  S3 ), although we may lack the statistical power to detect it because of having to use a reduced dataset, and because both immune parameters harbour very low amongindividual variance, hampering the possibilities for a covariance between the two traits. At the within-individual level, however, the correlation between haemagglutination titre and haptoglobin concentration was positive (R within = 0. 13, 95% CI = 0. 04 to 0. 24, Figure  2 ).
ACK N OWLED G EM ENTS We are indebted to Peter H. Becker for setting up the long-term common tern study and warmly thank Juliane Riechert, Nathalie Kürten and Götz Wagenknecht for assisting with the management of fieldwork and maintenance of the long-term dataset, as well as all field workers and students who helped with data collection. We also thank Jean-Michel Gaillard, as well as the associate editor and two anonymous reviewers for constructive discussion of our work. M. M.
The flight simulation is controlled by the Paparazzi autopilot module  (Hattenberger et al. , 2014) , which makes it possible to simulate the behavior of an RPA within the LES. The autopilot module and the LES are combined with the module CAMS (Cloud Adaptive Mapping System). The payload module, which simulates a cloud instrument, is embedded in the Paparazzi autopilot module to detect the presence of a cloud using a threshold of LWC ≥ 10 -2 g m -3. If the LWC threshold is exceeded, the RPA begins its rosette pattern by conducting a straight line until it exits the cloud. The geometric center (red point in Fig. 3 , t 0 ) is calculated using a weighted sum of the LWC after each passage through a cloud. After exiting the cloud (LWC ≤ 10 -2 g m -3 ), the RPA turns back toward the cloud center (Fig.
Data availability. Data for the static case are currently being archived and will be accessible online. Due to size of the data for the dynamic simulation (1. 5 TB), please contact the corresponding authors. The cloud identification method co-developed by Najda Villefranque is available from GITLAB website (https://gitlab. com/ tropics/objects/, last access: 7 January 2022;  Villefranque and Brient, 2022, contact author: najda. villefranque@lmd. ipsl. fr). The Paparazzi module is available from Wiki page website (https://wiki. paparazziuav. org/wiki/Main_Page, last access: 7 January 2022; Paparazzi UAV, 2022; contact author: gautier. hattenberger@enac. fr). The CAMS module is available from LAAS website (https:// redmine. laas. fr/projects/nephelae-devel/wiki, last access: 7 January 2022; LAAS-CNRS, 2022; contact author: simon.
lacroix@laas. fr). Author contributions. NM conducted the analysis of the data and wrote the paper. GCR supervised the project, verified the analytical methods, and edited the paper. FC carried out the simulation, verified the analytical methods, and edited the paper. NV co-developed the cloud identification method. TV and GH have designed RPA patterns for the Paparazzi autopilot. PN and SL conceived and developed the Cloud Adaptive Mapping System (CAMS) module. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. The simulations were performed using the supercomputer  Beaufix of Météo-France in Toulouse, France. Financial support. This research was supported by  Agence Nationale de la Recherche  (grant no. ANR-17-CE01-0003 ),  Météo-France ,  Aerospace Valley , and  Région Occitanie. Review statement. This paper was edited by  Szymon Malinowski  and reviewed by two anonymous referees.
A close-up on the ν  1 + ν 3  and 2ν 2 + ν 3 (alternatively named ν 1 + ν 3 Fermi resonances 1  and 2 ) is shown. These combination modes were selected to follow the evolution of the process of clathration, because the gas phase absorptions are not fully saturated during pressurisation, and the caged CO 2 infrared signature can be observed within the comb of the rovibrational transitions, as shown in Fig. 1. In addition to the cell temperature diode measurement, the gas phase CO 2 temperature can also be monitored via the observed comb. We modelled the gas phase spectrum using the HITRAN database  (Kochanov et al. 2016 ) for a 200 µm path length, a CO 2 pressure of 60 mbar, and a gas temperature of 165K, and convolved using the transfer function of the spectrometer. The result is shown in the lower trace in Fig.
Conclusion We measured the temperature-dependent diffusion-controlled formation of a carbon dioxide clathrate hydrate in the 155-210 K range by direct monitoring of the caged CO 2 specific clathrate hydrate infrared signatures. The diffusion coefficient associated with the clathrate diffusion-controlled nucleation kinetics was deduced. It proceeds with an activation energy of E a = 24. 7 ± 9. 7kJ/mol, which is lower than the one found at higher ice temperatures. Combining these measurements with previous kinetics measurements, the data are now available to implement the kinetics of CO 2 hydrate formation from ice in models, for temperatures ranging from the clathrate-solid CO 2 crossing point around 130K, by slight extrapolation, up to the water triple point. Looking at the low-temperature-dependent diffusion coefficient associated with clathrate formation, carbon dioxide clathrate hydrate nucleation is principally expected in planets or satellites, and may build up with moderate pressures and higher temperatures from simple hydrates in comets as they enter the inner Solar System.
Acknowledgements. Part of the equipment used in this work has been financed by the  French INSU-CNRS program  ' Physique et Chimie du Milieu Interstellaire ' (PCMI). We wish to acknowledge past years technical support on the experiment cell conception from  A. Arondel ,  M. Bouzit ,  M. Chaigneau ,  N. Coron ,  B. Crane ,  T. Redon.
This proxy was consequently adopted to quantify the energy seascapes that enable juvenile European honey buzzards to survive longer sea-crossings compared to their earlier migrating adult conspecifics  [19]. Yet, whether ΔT is a meaningful correlate of upward moving air has not been quantitatively tested. In this study, we investigate sea-crossing behaviour at the global scale to assess the role of uplift and wind in shaping the energy seascapes for terrestrial bird migration. To do this, we use bio-logging data collected for five raptor species that perform long sea-crossing journeys. These species differ in size, morphology and stop-over strategies and their overall dependence on soaring flight to cover long distances. However, they are all facultative soaring migrants and are expected to preferentially use soaring over flapping flight whenever possible, including flights over open water.
Methods (a) Bio-logging dataset We compiled a bio-logging dataset containing migratory trajectories of birds that regularly perform sea-crossing during autumn. We did not include spring migration in this study, due to the limited amount of data for spring migration compared to autumn. Our dataset was comprised five species: the Oriental honey buzzard Pernis ptilorhynchus and the grey-faced buzzard Butastur indicus in the East Asian flyways, the osprey Pandion haliaetus and the peregrine falcon Falco peregrinus, in both the African-Eurasian and the American flyways, and the Eleonora's falcon F. eleonorae in the African-Eurasian flyway. These birds are all facultative soaring birds. Their dependence on uplift varies, with the falcons and the osprey being less dependent on uplift than the buzzards  [11].
θ [K] is the virtual potential temperature at the height of the boundary layer top (z) and was approximated using the wet adiabatic lapse rate (typical for oceanic boundary layers) as u ¼ T 2 m þ 273:15 þ 0:006z: We used data from the ERA-interim reanalysis database (spatial and temporal resolution of 0. 75 degrees and 3 h, respectively) provided by the European Center for Medium-Range Weather Forecast (ECMWF; https://www. ecmwf. int). We accessed these data through the EnvDATA service  [32]  on Movebank (www. movebank. org), to get the values for z, T 2 m, w'T' and w'q' (with some unit conversions). Overall, we closely followed Bohrer et al. [33]  to calculate w*, but including the contribution of the surface water vapour flux to the surface virtual potential temperature flux, and using the wet adiabatic laps rate to estimate the potential temperature at the height of the boundary layer top.
(c) Spatio-temporal modelling of ΔT To show the spatio-temporal variation in ΔT at the global scale, we used 40 years of temperature data. We focused on five regions where regular long-distance sea-crossing is performed by facultative soaring birds, namely South-east Asia, the Indian Ocean, the Mozambique Channel, Europe and the Americas. We downloaded sea surface temperature and temperature at 2 m above the sea for these regions for 1981-2020 from the ECMWF ERAinterim reanalysis dataset (spatial and temporal resolution of 6 h and 0. 75°, respectively). We chose to use this dataset instead of the higher resolution ERA5, to reduce the time and memory needed for the analysis. We spatially filtered the data to exclude lakes, as we were only interested in the open seas and oceans. To include a proxy for the time of day, we calculated the solar elevation angle for each data point.
We then created a categorical variable with three levels, night, low sun elevation and high sun elevation, corresponding to sun elevation angles below -6, between -6 and 40 degrees, and over 40 degrees, respectively. We loosely followed Nourani et al. [19]  to construct energy seascapes. In brief, we modelled ΔT as a function of latitude, longitude, day of year and time of day using the generalized additive mixed modelling (GAMM) approach. Five models were constructed in total, one per region. We extracted the timing and location of sea-crossings from our bio-logging dataset. We did not have empirical data for migration over the Indian Ocean and therefore consulted the relevant literature to extract the spatio-temporal pattern of the Amur falcon's Falco amurensis sea-crossing over the region  [34, 35].
Each model included two smoothers, one cyclic cubic regression splines smoother for the day of the year and a spline on the sphere for latitude and longitude. For both of these parameters, one smoothing curve was estimated for each level of time of day. Year was added as a random intercept in the models to control for annual variations in ΔT. We also included a variance structure in the models to account for the heteroscedasticity caused by higher ΔT variance in higher latitudes. Models were fitted using the mgcv package  [36]  in R v. 4. 0. 2 [37]. We used each model to predict energy seascape maps for the autumn migration season (August-November). We spatially interpolated the prediction rasters to a 1 km resolution for visualization purposes.
(d) Route selection analysis We investigated route selection by fitting a step selection function  [38]  to relate the probability of presence over the sea with atmospheric conditions. Every two consecutive points along a track were considered a step. Atmospheric conditions were compared between the observed step and a set of alternative steps that were available to the birds in space and time. The greyfaced buzzard was excluded from this analysis because of the low resolution of the satellite-tracking data. We filtered our dataset to include only points over the open sea. Trajectories that intersected land (e. g. islands) were broken into sea-crossing segments. Segments shorter than 30 km, and those that included fewer than three tracking points, were removed. To ensure a uniform temporal resolution and to reduce spatio-temporal auto-correlation, we re-sampled all data to one-hourly intervals (with a tolerance of 15 min) using R package Move  [39].
We prepared a stratified dataset: along with each sea-crossing segment, for each step, we generated 50 spatially alternative steps. Based on the distribution of step lengths (gamma distribution) and turning angles (von Mises distribution) estimated using over-water tracking data, fitted separately for each speciesflyway combination. Diagnostics plots were used to assess the fit of the distributions. All data were then annotated using the ENV-data track annotation service  [32]  provided by Movebank. Each point was annotated with u (eastward) and v (northward) components of the wind, sea surface temperature and temperature at 2 m above the sea, all provided by ECMWF ERA5 reanalysis database (temporal and spatial resolution of 1 h and 0. 25°, respectively). We selected the bilinear and the nearest-neighbour methods of interpolation for the wind and temperature data, respectively.
We then calculated wind support  [40]  and ΔT using the annotated data. Additionally, to investigate whether the predictability of atmospheric conditions affected the sea-crossing route choice, we annotated each point with long-term variances (over 40 years; 1981-2020) for wind support and ΔT. We checked the annotated dataset for multicollinearity and only used variables that were not highly correlated (r < 0. 6). Prior to analysis, we centred and scaled the predictors to mean zero and units of standard deviation (i. e. z-scores) to ensure comparability among predictors. Step selection functions were then estimated using the integrated nested Laplace approximation (INLA) method using the INLA package  [41]  in R v. 4. 0. 2 [37]. We constructed a multilevel model with fixed effects for ΔT, wind support, long-term variance of wind support and an interaction term for wind support and ΔT.
Results We found a positive relationship between the convective velocity scale, w* and ΔT (figure  1 ; r2 (adj. ) = 0. 31; p < 0. 05). The sea-crossing data from all species showed overnight flights (figure  1 ; electronic supplementary material, figure  S4 ). There was no clear difference between the pattern of correlation between w* and ΔT in different times of the day or for different species (figure  1 ). royalsocietypublishing. org/journal/rspb Proc. R. Soc. B 288: 20211603 The spatio-temporal pattern of sea-crossing in the six terrestrial bird species (the five species in our bio-logging dataset plus the Amur falcon) corresponded with positive uplift potential over the open sea (figure  2 ; see subplots for within-year and within-day variations in each region; see electronic supplementary material, table S2 for detailed GAMM outputs).
Theoretical and high-resolution multi-sensor bio-logging studies can investigate this for birds with different morphological characteristics and under different wind conditions. Although we show that it is possible to estimate w* using publicly available weather data, it is not an ideal proxy for measuring uplift under all circumstances over the open sea. First, w* can only be estimated for upward moving air. This means that there will be no values estimated for subsidence. ΔT does not have this limit, as it provides positive values for  uplift and negative values for subsidence. Second, calculating w* requires at least a basic knowledge of meteorological concepts and units. Different reanalysis datasets provide different variables for potential and water vapour fluxes, making our method of calculating w* difficult to adapt for other studies.