dataesr/scientific-paragraphs-categorization huggingface dataset:
(f ) Effect of conflict between morphology and molecules To tease apart the DRA effect caused by a vague FBD prior and the part caused by the difficulty of reconciling the morphological data and fossil ages with the extant tree, we fixed the tree topology using the ending tree from one of the informative FBD prior analyses (the rapid diversification prior), and then inferred the divergence times with morphological data excluded. When topology was unconstrained (except for the rooting constraint) and morphology was included, vague priors placed the placental origin at approximately 118 Ma, whereas informative priors placed the origin at around 85 Ma (figures 5a and 6a). The topologically constrained analysis under the vague FBD prior and without morphological data placed the origin at around 88 Ma.
Bimodal distributions correspond to cases where there are alternative likely placements for key fossils. rstb. royalsocietypublishing. org Phil. Trans. R. Soc. B 371: 20150136  [30] , Rhodocetus, Artiocetus and Basilosaurus, are pulled deeper down into the tree in our analysis, forming a group of stem artiodactyls together with Mesonyx, a traditional mesonychid sister taxon to cetaceans. Most other fossil placements would seem to be in line with expectations. Leptictis, for example, is sister to crown placentals, which is more consistent with the traditional view that its closest affinities are with Late Cretaceous eutherians, such as Gypsonictos  [31]. However, it seems that the large number of cranial and dental characters in the dataset might have resulted in the grouping of Mesonyx with fossil cetaceans, which is contradicted by major basicranial and ankle character complexes.
Indeed, it is easy to see a number of serious potential shortcomings in the standard Mk model for the evolution of discrete morphological characters, which we used here. First, the Mk model does not accommodate directional evolution, which is readily shown to occur in some morphological datasets over the timescales involved here  [32]. Perhaps more importantly, the variation in the rate of morphological evolution across characters and lineages is poorly understood and has probably been inadequately modelled so far. For instance, we explored models assuming that morphological and molecular clocks are either the same or completely independent. In reality, however, the morphological and molecular rates are likely to be neither identical nor independent but correlated. Furthermore, we used an uncorrelated relaxed clock model even though there is much to suggest that a significant component of rate variation across lineages is constrained by long-term autocorrelation, and failure to accommodate for this is likely to bias inferred divergence times.
This results in a push towards older dates, giving more evolutionary time for unlikely events to occur. If this interpretation is correct, then we would expect DRA to be more severe the larger the morphological dataset, with massive phenomic datasets like the one analysed here representing the worst case as they are likely to include large numbers of correlated characters. It is obvious from our results that a failure to account for diversified sampling of extant taxa can also cause severe DRA in the dating of higher-level phylogenies. This is consistent with results for the dating of the early radiation of Hymenoptera (ants, wasps and bees)  [5]  and an earlier analysis of the placental radiation  [15]. Thus, accounting for tip sampling biases is important in addressing DRA. The smaller the fraction of taxa sampled for the analysis, the more important it probably becomes to model the sampling biases correctly.
Our estimates for the timing of the subsequent Palaeogene radiation of placentals come even closer to the dates inferred from the fossil record according to O'Leary et al. [8]. Our estimate for the timing of the primary interordinal radiation of placental mammals that Meredith et al. [9]  date at approximately 83 Ma, falls close to the 66 Ma K -Pg boundary, with 95% highest posterior densities of several major clades-including Euarchonta, Eulipotyphla, Afrotheria, Euungulata (artiodactyls, perissodactyls) and Ferae (carnivorans, pangolins)overlapping this boundary. We note that our divergence time estimates agree fairly well with a recent phylogenomic study using a rich set of 'soft' node calibrations  [10] , but our estimates tend to have more uncertainty associated with them. The increased uncertainty is not due to the fact that we used a smaller molecular dataset than dos Reis et al.
Acknowledgments This research was supported by the  National Natural Science Foundation of China  (no. 41404075 ), the  CAS/CAFEA International Partnership Program for Creative Research Teams  (no. KZZD-EW-TZ-19 ), and  China National Science and Technology Support Program  ( 2012BAK19B03-5 ). The authors are very grateful to  Yves Leroy  for providing many advices in the calculation and paper writing. They thank  Manuel Pubellier  and  Alexandra Robert  for discussion. They thank two anonymous reviewers for their critical review and suggestion to improve the content and presentation of the paper.
(1993). This model has been extended to dissociated dislocations and successfully applied to Mg 2 SiO 4 ringwoodite by  Ritterbex et al. (2015). It is adopted in the present work to handle kink-pair formation on dissociated dislocations as they occur in wadsleyite. Dislocation mobilities are finally determined from the stress dependence on the nucleation rate of kink-pairs. Single slip constitutive equations describing the temperature dependency on the critical resolved shear stress (CRSS), will be derived by solving Orowan's equation as a function of steady-state strain rate. This will be compared to recent data of experimentally deformed wadsleyite. The results enable us to address the role of lattice friction on the dislocation mobility in wadsleyite under pressure and temperature conditions of the upper transition zone.
Implementation in Elmer/Ice The modified mechanical equations are implemented in the Elmer/Ice finite element software  (Gagliardini et al. , 2013). The correct implementation of the mass conservation was checked by comparing different 2. 5-D simulations with the Vialov-type profiles  (Vialov, 1958)  computed for different diverging tubes. The expression of the Vialov profile in the case of a power-law varying flow tube is presented in Appendix A.
Code availability The presented simulations were performed using the Elmer/Ice v. 7. 0 rev. 7016 finite element model. The source code of the 2. 5-D model has been available in the distribution since v. 8. 0 rev. d9d4a2f, implemented in the AIFlow solver. The link to the source code is: https://github. com/ElmerCSC/ elmerfem.
Swarm data selection The algorithm we employ is sketched in Fig. 1. It has been constantly modified since the beginning of the launch of Swarm and is applied to its measurements for the first time. The numerical protocol begins with the Swarm data selection (process 1). We select the 1-Hz level 1b measurements of Swarm A and C between March 1, 2014, corresponding to the satellites insertion into their final orbits, and December 31, 2015. This dataset is downsampled by taking only one vector and scalar measurement every 5 s. We use the magnetic data with baseline 0408 for Swarm A and baseline 0409 for Swarm C. We select vector measurements of Swarm A and C measured by the vector field magnetometers (VFM) and the scalar measurements acquired by the absolute scalar magnetometer (ASM) in nominal mode only.
However, due to an anomaly of the ASM on Swarm C [see  Fratter et al. (2016) ], we select the ASM scalar data in nominal mode only up to November 2014 and thereafter use the field strength measured in nominal mode by the calibrated VFM magnetometer. Measurements of satellite B, taken at an average altitude of 510 km, are not used as they are not as sensitive to the lithospheric magnetic signal as the Swarm A and C satellites. Middle-and high-latitude measurements are processed separately in order to account for the different statistical properties of the external source fields. High-latitude measurements, defined as those covering regions of poleward magnetic latitudes larger than |±52°|, are selected when the Sun is at least 10° below the horizon, regardless of the local time (LT). In contrast, low-latitude measurements in the magnetic latitude range of -52° to 52° are selected for LT between 23:00 and 5:00.
For the purpose of the along-track pre-processing (detailed hereafter), we construct a provisional datasets where the latitude limit of the selected measurements extends up to |±62°| for low-latitude measurements and down to |±42°| for the polar measurements. These extra measurements are rejected after the dedicated corrections. We select vector and scalar measurements only when the hourly Dst index is lower than ±5 nT, with variations lower than 5 nT over the three previous hours, in order to minimize the external fields generated by the ring current. Similarly, measurements associated with Kp index lower than 2 -during the considered time interval and the previous 3-h one are selected. The measurements corresponding to reduced magnetic field perturbation are selected using the interplanetary magnetic field (IMF) values of |IMFBy| <2 nT and IMFBz >0.
Correction with SH models In a second stage of the processing, we correct the measurements for the main and external magnetic fields (process 2a, 2b, and/or process 3 in Fig. 1 ). Explicit models relying on the parameterization of the main internal and external fields in SH are most appropriate for such data correction. The Swarm processing chains developed within the SCARF consortium  (Olsen et al. 2013 ) provide various models for these source fields  (Hamilton 2013; Rother et al. 2013; Sabaka et al. 2013). However, data corrections based on models derived from different dataset Fig. 1  Flowchart of the processes runs to generate a lithospheric field model from the level 1b Swarm magnetic field measurements may lead to bias and errors (e. g. ,  Sabaka and Olsen 2006). For example, the CI core field model relies on a selection that uses both night and day data, which is different from the strict LT and Sun elevation selection criteria we use here.
At mid-latitudes, this processing reduces considerably the offsets between the adjacent satellite tracks but not perfectly particularly along the magnetic equator and South East Asia (see Fig. 2 ). However, initial tests with this Swarm dataset demonstrated that more complex data processing along the satellite tracks would distort the lithospheric field structures and lead to models with less power than expected. At polar latitudes, this SH along-track analysis is also imperfect. The data selection process minimizes the ionosphere diurnal variations but cannot avoid all ionospheric field contributions. In particular, the field produced by the polar electrojets persists at night times. It is confined within the auroral oval (between 55° and 75° absolute latitude) during magnetically quiet times, and along-track correction with low SH degree models is not as efficient.
Once this series of dedicated correction are made, the lithospheric field model from SH degree 16 to 45 that was first subtracted is added back and the measurements temporarily selected above magnetic latitude |±52°| in the mid-latitude dataset and below magnetic latitude |±52°| in the polar dataset are removed. Across-track vector and scalar differences are next computed by selecting Swarm A and C data measured at the same universal time (UT) with the additional condition that the distance between the measurements does not exceed 1. 4° in great circle distance (this corresponds to the angular distance between Swarm A and C at the equator crossings). These across-track differences approximate the gradient of the measurements. However, we do not divide the difference by the actual distance between the measurements so that vector, scalar, and "gradient data" are all in units of nanotesla.
Differences along each satellite A and C tracks that approximate the NS gradients at equator crossing are computed by selecting data measured by the same satellite with a time stamp difference of 20 s. This time difference corresponds to about 1. 2° or 140 km (at 460 km altitude) spacing between the measurements. This distance is chosen smaller but close to the angular EW separation of Swarm A and C of about 1. 4° at the equator. Kotsiaros et al. (2015)  performed a retrospective analysis of the last 2 years of the CHAMP satellite measurements and concluded that computing NS gradients from measurements separated by not more than 30 s is close to optimum. Contrary to the across-track gradients, we found that uncorrected external fields were enhanced at high latitudes when including the satellite A and C alongtrack gradients, particularly in the auroral ovals.
Data weighting The scalar, vector, and gradient data we use are statistically different and undergo various types of processing in the algorithm. Defining which weight should be affected to each dataset is therefore not trivial. For example, it is known that a Sun-related perturbation affects the VFM vector measurements on all three Swarm satellites and that an empirical model is currently being used to correct and calibrate the official 0408 and 0409 vector data, using an approach similar to the one initially proposed by  Lesur et al. (2015)  (see https://earth. esa. int/web/ guest/swarm/data-access/dataset-history and related documents for more details). In addition, because of an anomaly affecting the ASM instrument on Swarm C, no more absolute scalar data are being acquired on that satellite since November 5, 2014, and calibration of Swarm C data must also rely on absolute scalar ASM measurements made on the nearby Swarm A satellite.
Results In the DLFI algorithm, the modeling of the selected and corrected Swarm measurements is performed in two steps. The first step involves the iterative regional modeling over the Earth at the median altitude of the measurements. Table  1  shows the Huber weighted misfit and mean residuals between the data and the regional models for the different data types (at the end of process 5 in Fig. 1 ). The data misfit is below 1 nT at mid-latitudes and does not exceed 3. 1 nT at high latitudes. As expected, the noise level in polar regions is larger for the scalar and vertical field components than for the gradient data. The scalar gradient misfit is particularly low at mid-latitude, thus confirming the good quality of the measurements. The mean residual values are close to zero for all components.
The MF5 model was derived from single CHAMP satellite data, using a similar number of measurements and at a median altitude comparable to the one of the Swarm measurements considered here. In this respect, it is better suited for comparison purposes than more recent models, such as MF6  (Maus et al. 2008 ). MF7, finally, is used as a reference because it was derived from data acquired toward the end of the CHAMP mission, at very low altitudes (about 300 km on average), and displays the best lithospheric signal-to-noise ratio (thanks also to geometric amplification of the lithospheric signal with downward altitudes). The Lowes-Mauersberger power spectra of the DLFI (blue curve in Fig. 5 , left) and MF7 (black curve) models are similar up to SH degree 70 (correlation larger than 0. 85) and compare visually well to that of the SIFM (red), even though we observe a small offset of the DLFI spectrum with respect to the two other spectra.
Conclusion The DLFI model, which uses 22 months of Swarm satellite measurements, suggests that the data collected so far are (globally) sensitive to crustal field variations at least up to degree 80 with some geographic disparities. The lithospheric map we produced compares well with those derived from previous CHAMP and Swarm models. Differences are strongest in the northern and southern polar regions where rapid and significant non-lithospheric contributions in the measurements lead to arbitrary offsets between adjacent tracks that persist despite dedicated corrections and filtering. The perturbation of the small scales is particularly problematic when using regional modeling functions that were purposefully designed to image these structures. At present, a cleaner separation of the lithospheric signal from ionospheric and magnetospheric noise sources cannot be done simply.
On the one hand, increasing the data volume is contradictory with the strategy of the DLFI which is to identify and select the least disturbed satellite tracks by relying on drastic selection criteria. On the other hand, applying heavier processing or damping increases the risk of systematic errors. The explicit use of the Swarm gradient configuration substantially improves the situation by allowing lighter corrections to be applied on the vector and scalar data along the satellite track than would have been needed with a single satellite mission. Figures  4  and 6 , however, show that the DLFI model should still be regarded with some caution at the Earth's mean radius and auroral latitudes. Future significant improvements can nevertheless be expected from the progressively decaying altitudes of the Swarm satellites, which will increase the signal-to-noise ratio, and from data accumulation at all LTs, which will better average out the contributions of external magnetic field perturbations in polar regions  (Maus et al.
2006b). From a methodological viewpoint, we find promising the application of the singular spectrum analysis (SSA) to correct the measurements in the polar regions. In this study, SSA was applied on a track-by-track basis. But patterns common to adjacent satellite paths could be identified by using instead multitaper SSA. However, improvements of the lithospheric field model with datasets similar to the one considered in this paper will remain challenging because the RMS of the crustal field beyond SH degree 80 is <0. 1 nT at 460 km altitude (the median altitude of the Swarm measurements after 2 years). This value is comparable to the instruments error budget and is smaller than the data calibration uncertainties  (Lesur et al. 2015). Therefore, any systematic error larger than this threshold is likely to produce a map at the Earth's mean radius with biased structures at short length scales.
Description of the experiments, meteorological and traffic data RST and atmospheric measurements were obtained using a vehicle parked in the selected street with an on-board data acquisition system (Fig. 6a ). The instruments were primarily devices dedicated to meteorological parameters (T air , relative humidity, wind direction and speed). on the roof of the vehicle, and data collected every 2 s. A radiometer and an infrared camera were dedicated to RST without and with traffic, respectively. The radiometer was installed in a compartment at controlled temperature, attached to the front bumper of the car, also with measurements every 2 s. The infrared camera was installed in a compartment on the vehicle roof. Thermal images of the pavement submitted to traffic were taken every 60 s. An illustration of instruments is given in Fig.
Weather and urban data inputs for TEB Meteorological data used as forcing input for the TEB surface model come from the Nancy weather station located 2800 m away from the measurement site. Measurements available and used from this station are air temperature at 2 m height ( • C), air relative humidity at a height of 2 m (%) (the specific humidity used for forcing was calculated from this relative humidity), wind speed at a height of 10 m (m s -1 ), direct and diffuse solar radiation (W m -2 ), rain and snow precipitation (mm) and air pressure (Pa). In the absence of coupling with an atmospheric model, TEB can be forced with meteorological parameters at 2. 5 m. It was therefore consistent to take meteorological measurements available at 2 m as forcing data. Direct and diffuse radiation was calculated by the TEB model on the basis of global radiation data, assuming 80 % as direct and the 20 % remaining as diffuse.
110 Several TCCON target stations are near or in urban regions with varied topography and emissions sources: Pasadena is in the South Coast Air Basin of California, which contains the city of Los Angeles and is home to 17 million residents. Tsukuba is in a highly urbanized city near Tokyo (pop. ~228,000). The Paris TCCON station is located 4. 5 km from the Eiffel Tower in the heart of the city (pop. ~2. 24 million), and the Karlsruhe TCCON station is in a smaller, more isolated urban region 115 (pop. ~300,000) surrounded by forest. There are several target locations that are not TCCON stations (Fig. 3 , orange stars), and although data from those targets will not be analysed in this paper, the data will help assess bias. Railroad Valley is a heavily instrumented radiometric calibration site  (Kuze et al.
125 Target locations are selected a day or two in advance, based on the weather forecast, the operational status of the TCCON station (if the target is a TCCON station), the importance of the projected data loss in nadir or glint mode from performing the target-mode operation, and the historical statistics of successful target-mode measurements over that site. The projected data loss depends primarily on whether the nominal mode for that orbit was nadir over land, nadir over ocean, glint over land, 130 or glint over ocean. If the nominal mode is nadir over ocean, little data loss occurs, as nadir measurements over ocean are usually too dark in the near infrared for successful retrievals: in this case, the target is almost always selected given a reasonable weather forecast. This has mostly been the case for Réunion Island, which has been targeted regularly from OCO-2 nadir orbits.
For the other three cases, there will be some loss of regular science data to accommodate a target-mode opera-135 tion. In these cases, the historical statistics of acquiring good target-mode data and weather forecasts are weighted more heavily before enabling the target. Often, if the weather forecast is not ideal, no target-mode measurements will be selected. As of April 30, 2016, 227 targets have been selected, 195 of them over TCCON stations (Fig. 4 ). Of the 195 targets of TCCON stations, about 90% were "successful", in that both OCO-2 data 140 and TCCON data were recorded. This 10% loss was largely due to poor weather forecasts, resulting in completely overcast skies, during which the TCCON station was unable to record any data. All current space-based X CO2 measurements have systematic biases caused by uncertainties in the spectroscopy, by limitations in the information content of the measurements and uncertainties or oversimplifications in the optical properties of the atmosphere and surface, and by uncertainties in the instrument characterization and calibration (e.
190 Figure  5  shows the OCO-2 X CO2 target-mode median data comparisons with coincident TCCON data. The best fit lines were computed using a method that accounts for uncertainties in the dependent and independent variables as described by  York et al. (2004). Panel (a) shows the results prior to applying the parameter-dependent bias correction and has a correlation coefficient of R 2 = 0. 75. Panel (b) shows the relationship after the correction has been applied and an improved correlation 195 coefficient (R 2 = 0. 83). The improvement indicates that the parameter-dependent bias correction is effective at removing spurious variability in the OCO-2 data. The slope in panel (b), which has a y-intercept that is forced through 0, is used to derive the constant scaling factor between TCCON and OCO-2 target observations (m = 0.