dataesr/scientific-paragraphs-categorization huggingface dataset:
9 σ and 5. 1 σ ). This remains true even when the emission from  the cluster cores is excluded, see Table  D1  , Figs 13 (b), 14 (c) and 14 (d). While the measured slope of the L r500 X, 52 -T r500 X differs, it is only significant at the ≈2 σ level. There is very little change in the richness scaling relations. The current data are not sufficient to draw a firm conclusion as to the cause of these dif ferences. Ho we v er, the y are unlikely to be due to a systematic in the XCS analysis methods, i. e. whereby biases in measured L X or T X values are correlated with location on the detector: LD11 studied the effect of measuring temperatures for the same clusters that were detected at a high off-axis position and then re-observed at the on-axis aimpoint. LD11 found a 1:1 relationship between the measured temperatures, finding no systematic offset (see fig 18  in LD11  ).
S U M M A RY In this paper, we detail the X-ray analysis of SDSSRM clusters using data products from the XMM Cluster Surv e y (XCS). In summary: (i) In total, 1189 SDSSRM clusters fall within the cleaned XMM -Newton footprint. This has yielded 456 confirmed detections accompanied by X-ray luminosity ( L X ) measurements. Using an updated version of the XCS Post Processing Pipeline ( XCS3P ), we have extracted 381 X-ray temperature measurements ( T X ) from these 456 clusters. This represents one of the largest samples of coherently derived cluster T X values to date. We have also shown that the reliability of derived T X values impro v es when low quality spectra are remo v ed from joint fits. (ii) We find that the SDSSRM clusters in the XMM footprint that were not detected are primarily lower richness systems (75 per cent at λ < 30).
A combination of selection biases is a likely, but as yet unpro v en, reason for these differences. (v) We have used our data to probe recent claims of anisotropy in the L X -T X relation across the sky  (Migkas et al. 2020 ). We find no evidence of anistropy, but stress that this may be masked in our analysis by the incomplete declination co v erage of the SDSS DR8 sample. The methods outlined in this work have further been employed in the analysis of large cluster samples, such as those constructed from the RM analysis of the Dark Energy Surv e y data  (F arahi et al. 2019 ; Zhang et al. 2019  ). Although optically selected samples are free from X-ray selection biases, when matching to available X-ray data, future archi v al studies should consider only the use of serendipitously detected X-ray clusters to a v oid observer biases.
Comparison to the data given in Oguri et al. ( 2018 )  is shown in plot (c), with the LIRA fit to the data given by the purple dashed line and 68 per cent uncertainty given by the light purple shaded region. Note, the richness is estimated from CAMIRA, denoted N mem(CAMIRA). Comparison to the data given inFarahi et al. ( 2019 , dark-red triangles)  is shown in plot (d), with the LIRA fit given by the dark-red dashed line and the 68 per cent uncertainty given by the red shaded region (note the agreement between the fits obscures much of the comparison, and in this case the T X values were measured in a r 2500 apertures).
AC K N OW L E D G E M E N T S PG, KR, RW, DT, and  EU  recognizes support from the  UK Science and Technology Facilities Council  via grants  ST/P000525/1  and  ST/T000473/1  (PG, KR),  ST/P006760/1  (RW, DT),  ST/T506461/1  (EU), and  ST/N504452/1  (SB). MH acknowledges financial support from the  National Research Foundation. PTPV and LE were supported by Funda c˜ ao para a Ci encia e a  Tecnologia (FCT)  through research grants  UIDB/04434/2020  and  UIDP/04434/2020  (PTPV), and  SFRH/BD/52138/2013  (LE). TJ acknowledges support by the  U. S. Department of Energy, Office of Science, Office of High Energy Physics , under Award Numbers  DE-SC0010107  and  A00-1465-001. We thank  K. Migkas  for useful discussions regarding cluster isotropy. We thank  M. Sereno  for useful discussions on the use of the LIRA fitting package.
DATA AVA I L A B I L I T Y The data underlying this work can be found at: http:// users. sussex. ac. uk/pag22/SDSSRM-XCS/ sdssrm-xcs-sam ple-data. csv , along with a table description: http:// users. sussex. ac. uk/pag22/SDSSRM-XCS/ column names. t xt.
Methods This Section provides details concerning the samples, Raman setup, (AIMPRO) ab initio method and linear dielectric approach used to acquire, support and formalize the discussed data in the main manuscript. Complementary experimental and numerical data, obtained by high-pressure Raman scattering, high-pressure X-ray diffraction, solid-state nuclear magnetic resonance measurements and (SIESTA) ab initio calculations are reported in the Supplementary Sects. I and II. Samples. High-quality Zn 1-x Be x Te single crystals needed to perform high-pressure Raman and X-ray diffraction measurements were grown from the melt by mixing high-purity ZnTe (5 N quality, i. e. , 99. 9995) and Be (2 N quality, i. e. , 99. 5) materials using the high-pressure Bridgman method  15  , at various Be contents (x = 0.
Acknowledgements We acknowledge assistance from the CRISTAL beamline of synchrotron SOLEIL for the high-pressure X-ray diffraction measurements (Proposal No. 20110018-standard, led/co-led by A. P. & O. P. ) and from the  Nuclear Magnetic Resonance platform of Institut Jean Barriol  ( CNRS FR 3843 ,  Université de Lorraine -https:// www. ijb. univ-lorra ine. fr). Teddy Tite ,  Kwiseon Kim ,  Peter A. Graf ,  Oleg Maksimov  and  Maria C. Tamargo , co-authors (together with O. P. ) of Ref. 7 (https:// doi. org/ 10. 1088/ 0953-8984/ 18/2/ 016 ), are gratefully acknowledged for allowing us to use some of the Raman spectra taken with Zn 1-x Be x Te epitaxial layers published therein, for comparison with the currently studied Raman spectra of bulk crystals (see Fig. S6 for Supplementary Sect. III).
This study had the following aims: (1) Determine whether subject age affects repeatability of TEE, and (2) examine if TEE is associated with changes in weight or body composition. We used The International Atomic Energy Agency (IAEA) DLW database (https://doubly-labelled-water-database. iaea. org/home, database version 3. 1. 2) which pools DLW data across multiple studies  27. The database contains 6,787 measures of TEE spanning individuals from 23 countries. All measures of TEE were estimated using a common calculation method  28  removing variation introduced by choice of equation. We included all individuals that were at least 1 year old and for which repeated TEE measurements were available (N = 696 TEE measurements of 348 adults and 114 TEE measurements of 47 children). We estimated repeatability 'R', also referred to as the intra-class correlation coefficient (ICC), using a mixed effects model framework, where R describes the relative partitioning of variance into within-group and between-group sources of variance  [29] [30] [31].
This analysis allowed us to differentiate between combined, reversible changes in traits that occur within an individual (r e ) and genetic and permanent environmental effects that are responsible for the association between the traits (r ind ). We used two datasets: the entire dataset of adults 20-60 y (N = 267 subjects; Models 1 + 2) and a subset of individuals (N = 53 subjects) for which the time between measurements exceeded 4 weeks (Model 3 + 4; Table  1 ). These models showed the same result: there is no within-individual (r e ), among-individual (r ind ), or phenotypic (r p ) correlation between TEE and body fat (both as FM and body fat percentage; Table  1 ). In a second approach, we calculated an adjusted TEE, accounting for the covariation of TEE with FFM, FM, age and sex (see Methods).
001 ± 0. 003, t = 0. 396, df = 265, P = 0. 692, adjusted R 2 = -0. 003). Neither average adjusted TEE nor the difference in adjusted TEE between    2d ). We obtained similar results in the analysis restricted to subjects with more than 4 weeks between TEE measurements. Adjusted TEE1 was not associated with change in body weight (estimate ± SE: -0. 004 ± 0. 002, t = -1. 704, df = 51, P = 0. 094, adjusted R 2 = 0. 035; Supplementary Fig. 2a ) or body fat percentage (estimate ± SE: -0. 0005 ± 0. 006, t = -0. 076, df = 51, P = 0. 940, adjusted R 2 = -0. 019; Supplementary Fig. 2b ) in these subjects. As in the full dataset, the difference in adjusted TEE between measurements was positively associated with changes in body weight (estimate ± SE: 0. 010 ± 0. 002, t = 4. 278, df = 51, P < 0. 0001, adjusted R 2 = 0.
The environmental, genetic, and behavioral contributions to maintaining high or low adjusted TEE remain unclear. While we lack independent measures of physical activity for subjects in this dataset, changes in FFM suggest exercise has a relatively modest effect on the magnitude or maintenance of adjusted TEE. For adults 20-60 y, changes in FFM, an indirect proxy for strenuous physical activity, were positively associated with changes in adjusted TEE. However, there are numerous forms of physical activity that do not lead to changes in FFM and thus, objective measures of physical activity are needed to further examine the effects of behavior on the maintenance and repeatability of adjusted TEE. Typically, repeatability sets an upper limit to heritability  36, 37  , and thus the high repeatability of adult TEE may indicate some degree of heritability and genetic influence.
The multi-response models showed that TEE and body fat (both FM and body fat percentage) were not correlated at the within-or among-individual level. Models using the entire dataset of adults 20-60 y (N = 267 subjects) and those using a subset of individuals (N = 53 subjects) for which the time between measurements exceeded 4 weeks showed the same results. These results are further strengthened by our analyses of the relationship between changes in body composition and weight and TEE, after accounting for its covariation with FFM, FM, age, and sex. If greater TEE was protective against gaining fat, then subjects with greater adjusted TEE, or positive changes in adjusted TEE, should have experienced less weight and fat gain. Instead, we found a positive relationship between the difference in adjusted TEE between measurements and change in body weight (in both datasets), and no relationship between any measure of adjusted TEE (time 1, difference between measures, or average) and change in body fat percentage (in both datasets) among adults in our sample.
The similarity between the results in both datasets indicates that these findings are not an artefact of measurement error or short time intervals. It is noteworthy that there was a trend towards a negative relationship between adjusted TEE1 and change in body weight (P = 0. 094) in the subset of individuals for which the period between repeated TEE measurements was longer than 4 weeks, but none of the other relationships indicated that TEE is associated with changes in body weight or composition in this subset. Therefore, we cannot rule out the possibility that TEE would be associated with small changes in body weight over much longer timeframes. But the results of the current study are consistent with those of previous work conducted on adults  13, 14  and children  [9] [10] [11] [12]  which reported no relationship between TEE and change in body fat percentage.
Nonetheless, the observed relationship between TEE and weight changes, which is positive in our sample, clearly challenges the expectation that decreased TEE is associated with weight gain. This study is limited by the lack of additional physiological or behavioral measures. Objective measures of physical activity would enable us to examine the contribution of changes in daily activity to observed changes in TEE and body composition. Similarly, measures of organ size or resting expenditure would enable us to investigate whether age-related changes in organ size and activity affect repeatability in TEE, particularly among children. As the DLW database expands, the addition of more associated variables such as organ size and activity will strengthen its utility. Humans, like other species, exhibit a substantial degree of variation in TEE.
Methods Criteria for inclusion of individuals from the IAEA DLW database. We included all individuals in the IAEA DLW database (https://doubly-labelled-waterdatabase. iaea. org/ home) which had their TEE measured at least twice and that were at least 1 year old. Only healthy individuals and those not involved in athletic competition or training were included. This resulted in 696 TEE measurements of 348 healthy adults and 114 TEE measurements of 47 children (22 girls and 25 boys). These repeated TEE measurements were collected from 225 women (age range at the first TEE measurement: 21-81) and 123 men (age range at the first TEE measurement: 22-82). No repeated TEE measurements were available between the ages of 7-20 years. For children, 32 measurements were collected at age 2, 41 measurements at age 4, and 41 measurements at age 6).
For 20 children, three repeated measurements were available. We excluded TEE measurements from children younger than 1 year because the relationship between TEE and FFM in this age group appears to differ from that for older children  11. Mean time interval between two TEE measurements of the same individual was 1. 9 ± 2. 88 y (range: 0. 04-8. 2 y). 66. 0% were repeated TEE within 1 y, 14. 2% were repeated 2-4 y after initial TEE measurement, and 19. 8% were repeated 5-8 y after initial TEE measurement. All measurements (body weight, TEE, FFM, and FM) were obtained directly from the IAEA DLW database. Dilution space (a measure of the total body water pool) and FFM hydration (water content of FFM) are an integral part of the determination of body composition and TEE, and we corrected measurements for age-and body weight-related variation in dilution space and FFM hydration.
FFM measurements included in this study were estimated via the isotope dilution method, and FM and body fat percentage were determined by subtracting FFM from body weight. All of the studies that provided data into the IAEA DLW database were locally ethically reviewed and approved. The present paper is based on a secondary analysis of these compiled data and such analyses do not require ethical permission. Repeatability estimate 'R'. We estimated repeatability R as R = V G /(V G + V R ), where V G = inter-individual variance and V R = intra-individual variance  30. The estimate R is more generally also referred to as the intra-class correlation (ICC). This estimate has previously been used to estimate the reproducibility of metabolic rate measurements in animals  18, 34, 43, 44.
Statistical analysis Repeatability estimate R. All analyses were conducted using R version 3. 6. 2  45. Repeatabilities are tested at the boundary and tests are therefore typically onetailed, and all other tests were two-tailed. We estimated repeatability R using a mixed effects model framework in the R package rptR  30. We included FFM, FM, sex, and age as fixed factors. Controlling for these fixed effects removes the influence of their variance from the estimate R 30. Thus, we estimated the adjusted repeatability of TEE, after controlling for the body composition variables, age, and sex as sources of variation in the dataset. We ln-transformed the response variable TEE and the fixed factors FFM and FM, and used individual ID as random factor. In a first step, we estimated R of TEE for all individuals older > 1 y (N = 395 individuals) together.
Permutation of residuals randomizes the grouping factor against the response variable, followed by refitting the model to the randomized data. R varies from 0 to 1 and we considered TEE to be repeatable if the 95% confidence interval around R did not include zero. Is TEE associated with short-term changes in weight or body composition? We restricted this analysis to a subset of individuals aged 20-60 y (N = 267). We excluded children from this analysis due to their continued somatic growth and expected increase in TEE during aging  11  , and we excluded subjects older than 60 y because TEE, FFM and FM are all known to decrease at this age  46, 47. We used two approaches to address this question. In a first approach, we evaluated covariations among TEE and FM (Model 1) and among TEE and body fat percentage (Model 2) by fitting two multivariate Bayesian mixed models using the package MCMCglmm  48  , including individual ID as random factor.
We estimated the correlation between both traits by comparing the variance-covariance divided by the square root of the product of variances  52. We used inverse gamma priors, and MCMC sampling scheme of 900,000 total iterations with a 30,000 iteration burn-in and sampling (thinning) interval of 250. This yielded Monte Carlo Markov Chains with a sample size of 3480. We estimated the level of non-independence between successive samples in the chain using the 'autocorr' function in the coda package  53. For all models, we ran three independent chains and assessed MCMC convergence and mixing visually by plotting the traces and densities of sampled values across iterations, and confirmed convergence using the Gelman-Rubin convergence criterion (all < 1. 1) using the coda package  53. In a second approach, we calculated a body size-and composition-adjusted TEE, which accounts for the covariation of TEE with body size and composition.
Moreover, we tested whether the change in adjusted TEE per week was associated with future changes in body weight and body fat percentage using linear models. We analyzed the full 20-60 y dataset (N = 267) as well as the subset of subjects for which the time between measurements exceeded 4 week (N = 53). The latter analysis of longer-duration measurements reduces the potential effect of measurement error on the calculated rate of FFM and FM change. Short-term variations in body weight are mainly due to changes in FFM and to a smaller extent due to changes in FM  54. Small errors in the determination of FFM, either due to measurement imprecision, variation in the hydration of FFM (used to calculate FFM from isotope dilution), will lead to errors when determining the rate of change (kg/wk) for both FFM and FM.
Acknowledgements The DLW database, which can be found at https://doubly-labelled-waterdatabase. iaea. org/homeor https://www. dlwdatabase. org/, is generously supported by the  IAEA ,  Taiyo Nippon Sanso  and, SERCON. We are grateful to  David Ludwig  and  Cara Ebbeling  for their contributed data. We are grateful to these companies for their support and especially to  Takashi Oono  for his tremendous efforts at fund raising on our behalf. The authors also gratefully acknowledge funding from the  US National Science Foundation  ( BCS-1824466 ) awarded to  Herman Pontzer.
Data availability All data supporting the analyses and results in this paper are available from the Doubly Labeled Water Database (https://doubly-labelled-water-database. iaea. org/home, https:// www. dlwdatabase. org/) upon reasonable request. Because of human study participant confidentiality the database is not open access. However, access to components of the data is freely available to perform novel and approved analyses. Details of the application process are available at https://doubly-labelled-water-database. iaea. org/ dataAnalysisInstructions. Data published in this paper will be provided normally within 3 weeks of receipt of the request. Such data has unrestricted use except we ask users not to share the data with others or post it on social media or other internet sites. If users to publish analyses of such provided data, we ask that they adhere to the procedures established to ensure fair credit for those contributing the data into the DLW database.
Since then their physical origin, frequency of occurrence, amplitude, and energy distribution have been studied using ground-based and space-borne data  7. The strongest measured SEP event (called ground-level-enhancement GLE #5) took place on 23-Feb-1956 (a list of directly measured events is available at the International GLE database (https://gle. oulu. fi)). However, little is known about extreme SEP events whose very existence was unknown until a few years ago  8. Even though observational advances such as a worldwide network of neutron monitors established in 1951/1957/1964 made it possible to characterize the Suns' eruptive behavior  9  , the temporal coverage of the observational record is not long enough to assess the frequency of extremely rare but highly energetic SEP events. Statistics of sunlike stars suggest that the superflares that produce highly energetic SEPs are extremely rare  10, 11.
With hindsight, this event could not have been detected by the statistical analysis of cosmogenic nuclides from the lower resolution ice core data  37. Here we present two previously unknown  14  C production events recorded in two (7176 BCE) and four (5259 BCE) independent, absolutely dated tree-ring chronologies. The magnitudes of the events are assessed by calculating the additional 14 C production using a global carbon cycle box model. They are compared to other known  14  C events by normalizing their magnitudes to modern geomagnetic shielding. Furthermore, the 7176 BCE event is used as a unique time marker to place a floating (i. e. not absolutely dated) portion of the Bristlecone Pine tree-ring chronology (USA) within 1-2 years on its absolutely dated part. Table  1  The modeled atmospheric  14  C increases, produced  14  C and normalized  14  C produced at the modern earth magnetic field is given for new and known events.
So far, only a few sequences covering a total of 2030 yrs of the past 12,400 years (16. 5%) have been analyzed with  14  C at the annual or biennial resolution, which is required to unambiguously detect SEP events, revealing five strong SEP events (Fig. 4 ). These findings might lead to the conclusion that strong SEP events hit Earth once every about 400 years on average (i. e. five events in 2030 years). This simple statistic, however, is likely biased by the fact that some studied time periods were not randomly selected for annual analysis, but rather were targeted based on indications from lower-resolution cosmogenic datasets or archaeological evidence that the present multi-annual  14  C record might contain more structures than previously visible. In an unlikely case that all major events have already been found over the last 12,400 years, the lower limit of occurrence for strong SEP events can be estimated as five events in 12,400 years or one every ~2400 years.
accelerator mass spectrometer  51. Two measurement sets were typically prepared from one set of samples within a week and subsequently measured. A second graphite sample was subsequently prepared and measured from one third of the prepared cellulose samples for quality control purposes. Two internal wood reference materials from 1515 CE (Pine and Oak) and two different radiocarbon free wood blanks (Kauri Stage 7, KB, and Brown Coal, BC, from Reichwalde) were repetitively analyzed together with the annual samples. While the wood-blanks were used for blank subtraction in the data evaluation process, the 1515 CE references were used for quality control only. For the analyses performed in Bristol, tree ring samples (20-30 mg) were processed alongside two KB wood blanks and two oak 1524 CE reference samples in 15 ml glass culture tubes.