dataesr/scientific-paragraphs-categorization huggingface dataset:
However, this is rather fortuitous and can change with the use of a different reference dataset (e. g. Gardner et al. , 2013; Wouters et al. , 2019). Another question is whether a glaciological observation network reduced to the long-term observation series is good enough to estimate the temporal variability of global glacier mass changes. We thus performed another ad hoc estimate with a glaciological sample reduced to the current 41 WGMS reference glaciers (Fig. 2b ), coming with more than 30 years of ongoing mass-balance measurements (WGMS, 2017). The WGMS reference glaciers provide a more stable sample over time but come at the price of a much reduced sample size. As such, the sample is reduced from 154 to 38 glaciers with observations in 2016/17 and, hence, neighbouring glaciers are needed in 10 (instead of 4) out of 19 regions (Table  S1 ).
The ad hoc estimates for the WGMS reference glacier sample from 1975/76 (i. e. the first year with reference glacier data from the southern hemisphere) to 2016/17 show a much-increased variability, strong offsets over certain periods, and an increased uncertainty by about 40 % (Figs. 2, S4 ). This low performance suggests that -for the present approach -the WGMS reference glacier sample alone is too small and represents too few regions for an ad hoc estimation of global glacier contributions to sea-level rise. It is worthwhile to note that regions with large areas of glacierization (e. g. Arctic Canada South, Russian Arctic, South Asia East & West, peripheral Greenland, and peripheral Antarctica) lack long-term mass-balance series.
Conclusions and outlook Direct glaciological observations, as currently conducted for about 150 glaciers worldwide, are able to satisfactorily capture the temporal mass-balance variability but are often not representative of the total mass change of a region. We presented a new approach to provide ad hoc estimates of regional glacier mass changes for the most recent years based on the anomaly of glaciological mass-balance observations and a bias correction to a reference dataset over a common calibration period from 2006/07 to 2015/16. The ad hoc estimates for 2016/17 and 2017/18 indicate that global glacier mass loss has further increased (with respect to the previous decade) and resulted in annual global glacier contributions to sea-level rise exceeding 1 mm SLE, which corresponds to more than a quarter of the currently observed sea-level rise (cf.
IPCC, 2019). Our new approach allows for the timely reporting of global glacier mass changes and can be applied to a new consensus estimate as reference data, once available. To increase the accuracy of the global ad hoc estimates, we need to extend the glaciological sample into so far underrepresented and strongly glacierized regions such as High Mountain Asia, the Southern Andes, Russian Arctic, Greenland, or Antarctica. At the same time, we need to tap the full potential of spaceborne surveys to further improve the spatiotemporal coverage and resolution of the reference datasets.
Acknowledgements. We are indebted to the national correspondents and principal investigators of the WGMS for sharing their observations with the community. We thank  Regine Hock  for constructive input on mass-balance terminology and units, and  Betsy Armstrong  for polishing the language. Financial support. This research has been supported by the  Federal Office of Meteorology  and  Climatology MeteoSwiss  within the framework of the  Global Climate Observing System (GCOS) Switzerland , the  Copernicus Climate Change Service  ( C3S ) imple-mented by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission , the  European Space Agency (Glaciers_cci  (grant no. 4000109873/14/I-NB) ), and the  INRAE Grenoble as part of LabEx OSUG@2020. Review statement.
Code availability. The analytical scripts are available from the lead author on request. Data availability. The full sample of glaciological observations for individual glaciers is publicly available from the WGMS (https://doi. org/10. 5904/wgms-fog-2019-12; WGMS, 2019). The regional and global reference datasets from Zemp et al. (2019)  are available from the Zenodo repository (https://doi. org/10. 5281/zenodo. 3557199; Zemp, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-14-1043-2020-supplement.
Plant cuticle and organic matter occluded within pedogenic carbonates (OOM) were rinsed in 3 M HCl to remove carbonates and analyzed at the Stable Isotope Facility, University of California, Davis, using a PDZ Europa ANCA-GSL elemental analyzer interfaced to a PDZ Europa 20-20 IRMS (isotope-ratio mass spectrometer). External precision, based on repeated analysis of standards and replicates, is < ±0. 2 ‰. For Hamilton Quarry (HQ), all material was previously mounted on slides for taxonomic analysis  (Hernandez-Castillo et al. , 2009a, b, c). Because of this, biomarker δ 13 C values of bulk stratigraphic sediment samples were used  (Richey et al. , unpublished data ; see the Supplement). HQ n-C 27-31 n-alkane δ 13 C was analyzed using a Thermo Scientific GC Isolink gas chromatograph connected to a Thermo Scientific MAT 253 mass spectrometer.
Models The MATLAB model Paleosol Barometer Uncertainty Quantification (PBUQ;  Breecker, 2013) , which fully propagates uncertainty in all input parameters, was used to derive pedogenic carbonate-based CO 2 estimates  (Figs. 2a, S1a). For each locality, paleosols of inferred different soil orders were modeled separately. We applied improved soilspecific values for soil-respired CO 2 concentrations (S (z) ;  Montañez, 2013)  and the δ 13 C of organic matter occluded within carbonate nodules (δ 13 C OOM ; Fig. S5 ) as a proxy of soil-respired CO 2 δ 13 C. For samples where OOM was not recovered, estimates were revised using PBUQ and the plant fossil organic matter δ 13 C used in Montañez   2007 ), δ 13 C OOM was substituted for δ 13 C POM for localities that occur in the same geologic formation, and a large error (±2 ‰) was used to account for the uncertainty in this approach.
PBUQ model runs conducted in this study resulted in a small subpopulation of biologically untenable CO 2 estimates (i. e. , ≤170 ppm; Gerhart and Ward, 2010). To limit estimates below that threshold, two changes to the PBUQ MATLAB code were made (see the Supplement for details). All other input parameters remained unchanged from  Montañez et al. (2007). For cuticle fossil-based (Figs. S2-4 ) CO 2 estimates (Figs. 2a, S1a), we utilized a mechanistic (non-taxonspecific) gas-exchange model  (Franks et al. , 2014). For some fossil cuticles, pore length (PL) was measured directly; for others, PL was inferred from guard cell length (GCL; Table S1 in the Supplement). Guard cell width was estimated via GCL using the prescribed gymnosperms and ferns scaler (0. 6;  Franks et al. , 2014 ; Table  S1 ).
The calibration of the GEOCLIM model was performed at the Clim. Past, 16, 1759 Past, 16, -1775 Past, 16, , 2020  https://doi. org/10. 5194/cp-16-1759-2020 continental-scale by tuning the parameters of the model so that 30 % of the alkalinity generated by the weathering of silicates originates from the weathering of mafic rocks (GEO-CLIM REG;  Dessert et al. , 2001; Goddéris et al. , 2014). 2. 3 O 2 /CO 2 ratio O 2 /CO 2 ratios (Fig. 3a ) were calculated using the 10 000 CO 2 estimates produced by our modeling and combined with O 2 estimates obtained using geochemical mass balance and biogeochemical models  (Krause et al. , 2018; Lenton et al. , 2018). Unreasonably high O 2 /CO 2 ratios (generally those that correspond to CO 2 200 ppm) were removed from the resulting O 2 /CO 2 dataset.
Statistical analyses We utilize a bootstrap approach that assesses uncertainties of both CO 2 (or O 2 /CO 2 ) and age. Each age uncertainty was truncated to ensure no overlap in locality ages, which were constrained by their relative stratigraphic position to one another  (Richey et al. , 2020;  https://doi. org/10. 25338/B8S90Q). The 10 000 modeled CO 2 estimates were trimmed by 28 % to remove anomalously high or low values. The means of the resulting 7200 CO 2 estimates were compared to the trimmed means of the 10 000 CO 2 estimates to ensure that trimming did not alter the central tendency of the data. Locality ages were resampled and perturbed, assuming that the individual ages and truncated age uncertainties represent the mean and standard deviation of the ages. Similarly, the trimmed CO 2 -O 2 /CO 2 datasets were resampled with replacement, and the resampled ages and estimates were used to build 1000 resampled datasets.
Each resampled dataset was subjected to LOESS analysis (0. 25 smoothing), and the median and 95 % and 75 % confidence intervals of the LOESS analyses were calculated  (Figs. 2, 3a, b, S1). The Pennsylvanian and Permian portions of the record were analyzed separately due to differing data density, with significant overlap across the Pennsylvanian-Permian boundary interval  (Figs. 2b, 3b, S1b). To test the validity of short-term fluctuations in the LOESS CO 2 trend, we undertook further analysis of the raw Monte Carlo data produced by PBUQ and the mechanistic stomatal model in several short-term increments. Eleven short-term highs or lows (A-K in Fig. 4a ) were designated and used to form bins of ±0. 5 to ±1 Myr. Within an individual bin, each shown "bootstrapped" CO 2 data point is the trimmed mean of 10 000 Monte Carlo model runs.
, 2017) , they argue for a major reorganization of the predominant surface factors influencing weatherability in the tropics across the Carboniferous-Permian in order for pCO 2 values to have fallen to well below 300 ppm. The demise of the LPIA was greenhouse gas forced, reflecting the increasing importance of magmatic degassing and likely decreased weathering efficiency driven by intensifying aridification, denudation of the CPM, and the loss of the wetland forests throughout tropical Euramerica. Data availability. Underlying primary data are deposited in the Dryad Digital Repository  (Richey et al. , 2020)  and can be accessed at https://doi. org/10. 25338/B8S90Q. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-16-1759-2020-supplement.
Acknowledgements. We thank  Carol Hotton  ( National Museum of Natural History Smithsonian Institute ) and  Thomas Taylor (RIP) ,  Edith Taylor , and  Rudolph Serbert  ( University of Kansas ) for access to plant cuticle data used in this study. We also thank  Benjamin Mills  ( University of Leeds ) and  Duncan Temple-Lang  and co-workers at the  UC Davis Data Science Initiative  for guidance with statistical analyses. Finally, we thank  Joseph White  ( Baylor University ) for useful comments on the article. Financial support. This research has been supported by the  National Science Foundation ,  Division of Earth Sciences  (grant no. EAR-1338281 ), the  National Science Foundation ,  Directorate for Biological Sciences  (grant no. 1148897 ), and the  University of California ,  Davis (Graduate Research Mentorship Fellowship ).
(g) Statistical analysis All experimental data were expressed as mean ± s. e. To test for an effect of the plant treatment (SNF and NI) on the survival and weight of aphid lines, data were analysed using a two-way ANOVA. This allowed testing the effect of two independent variables ( plant condition (SNF or NI); different aphid lines (Amp, -Ri(n), -Ri(a), -Hd and -Ss)) on the weight and survival of aphids at 12 dpi. Differences between the aphid lines were tested using the Sidak's multiple comparison test. Data generated on the plant dry weight, the nitrogen fixation assay per plant or per mg of nodule, the number of root nodules per plant and the weight of the nodules per plant were analysed using a one-way ANOVA. Results from these experiments on SNF and/or NI plants were compared independently based on the treatment (one-way ANOVA).
Summary and conclusions This work presents a series of measurements of the capture cross section of 242 Pu from thermal to 500 keV using complementary neutron beams and different experimental techniques. The results presented in this manuscript were obtained from two different experiments carried out at the Budapest Research Reactor (BRR) and the n_TOF-EAR1 facility at CERN using a set of high-quality 242 Pu targets. The new 242 Pu(n,γ) data solve the existing discrepancies at thermal thank to the improved accuracy. In the resonance region, the high resolution of n_TOF-EAR1 has allowed to extract a large set of resonance parameters up to 4 keV. Last, this work provides the the first data set in the URR covering the full energy range from 1 to 500 keV, and the results support the trend indicated by the PROFIL experiments to reduce the capture cross section in JEFF-3.
Acknowledgments This measurement has received funding from the  EC   FP7 Programme  under the projects NEUTANDALUS (Grant No. 334315 ) and CHANDA (Grant No. 605203 ), the  Spanish Ministry of Economy and Competitiveness  projects  FPA2013-45083-P, FPA2014-53290-C2-2-P  and  FPA2016-77689-C2-1-R  and the  V Plan Propio de Investigación Programme  from the  University of Sevilla. Support from the  German Federal Ministry for Education and Research (BMBF) , contract number  03NUK13A , is gratefully acknowledged.
05), in the eno mutant relative to wild type (Dataset S1). To gain insight into the functions of these genes, we performed Gene Ontology (GO) term enrichment analysis using agriGO software  (18). Particularly, a significant enrichment was found for the molecular function of transcription regulator activity (P = 0. 0011, FDR = 0. 0429), DNA binding (P = 0. 00022, FDR = 0. 0087), and transcription factor activity (P = 0. 0007, FDR = 0. 0275) (Fig. 5A  and SI Appendix, Fig. S2 ), which suggests that ENO functions in a complex transcriptional network that fine-tunes the spatial and temporal regulation of genes controlling meristematic activity. In addition, functional GO enrichment analysis using ClueGO software  (19)  for the corresponding Arabidopsis homologs of upand down-regulated differentially expressed genes revealed 66 and 86 overrepresented GO terms, respectively (Dataset S2).
Taken together, these findings suggest that ENO loss-of-function results in prolonged FM maintenance leading to an enlargement of FM size. The role of ENO as a transcription regulator and its genetic interaction with lc and fasciated prompted us to examine expression changes in SlWUS (Solyc02g083950) and SlCLV3 (Solyc11g071380) genes in our RNA sequencing experiment. Notably, SlWUS expression was significantly up-regulated (fold change [FC] = 1. 4) in eno reproductive meristems. In contrast, no significant differences were found for SlCLV3 (Fig. 5B  and Dataset S1). To further investigate the contribution of ENO to the regulation of the CLV-WUS signaling pathway, expression patterns of SlWUS and SlCLV3 were examined by in situ hybridization. Thus, a similar expression pattern was observed for SlWUS mRNA in wild-type and eno SAMs (Fig.
For this purpose, 1. 6-kb region harboring the full-length ENO coding sequence was sequenced in a set of 103 accessions producing fruits of different sizes, comprising of 92 S. lycopersicum, 7 S. lycopersicum var. cerasiforme, and 4 S. pimpinellifolium accessions (Dataset S3). Sequence analysis identified 24 polymorphic sites and defined 9 haplotypes (SI Appendix, Fig. S4 ). Seven of these polymorphisms were detected in the ENO coding sequence, which resulted in one synonymous and six nonsynonymous substitutions (Fig. 6  A and B). Furthermore, we identified an 85-bp InDel annotated as a transposon-related element, which is located 107 bp upstream of the ENO start codon that was absent in haplotypes 1 to 5 and present in haplotypes 6 to 9 (Fig. 6A ). To thoroughly analyze the functional effect of the detected polymorphic sites on fruit locule number, the set of accessions was additionally genotyped for LC and FAS loci (Dataset S3).
(30)  (Dataset S4 and SI Appendix, Materials and Methods and Fig. S5 ). Results showed that the ENO promoter deletion allele first appeared at low frequencies in S. pimpinellifolium accessions, while it rose to near fixation already in the Andean S. lycopersicum var. cerasiforme group, the next step of domestication (Fig. 6G ). Interestingly, all S. lycopersicum accessions tested contained the ENO promoter deletion allele, except for a few Vintage accessions that contained introgressions from wild species in the region of ENO (SI Appendix, Fig. S6 ). In contrast to the ENO promoter mutation, the lc and fas mutations arose at low frequency in the Andean S. lycopersicum var. cerasiforme group and varied in frequency in the course of tomato breeding depending on the group tested (Fig. 6G ). Hence, taken together, the results show that the ENO promoter deletion allele arose prior to tomato domestication and increased in frequency to reach fixation in cultivated tomato, setting up the genetic environment that made significant changes in fruit size possible through selection and breeding of lc and fas mutant alleles.
Materials and Methods A detailed description of plant materials, plant growth conditions, microscopy, gene expression studies, vector construction and plant transformation, bioinformatic sequence analysis, the DNA-protein interaction assay, and any associated references are available in SI Appendix, Materials and Methods. Data Availability. The sequencing datasets for this study can be found in the National Center for Biotechnology Information Short Read Archive under the BioProject accession codes PRJNA503558  (45)  and PRJNA495568  (46).
ACKNOWLEDGMENTS. We thank  Dr. R. Fernández-Muñoz  ( Instituto de Hortofruticultura Subtropical y Mediterránea-Universidad de Málaga-Consejo Superior de Investigaciones Científicas ) for providing seeds of wild and cultivated tomato accessions, and  Dr. R. Micol-Ponce  ( Universidad de Almería ) for help with the EMSA experiments. This research was supported by the  Spanish Ministry of Economy and Competitiveness  (Grants  AGL2015-64991-C3-1-R  and  AGL2015-64991-C3-3-R ) and the  Research and Innovation Programme of the European Union Horizon 2020 (Breeding for Resilient, Efficient and Sustainable Organic Vegetable production [BRESOV], Project ID 774244 ). J. M. J. -G. was funded by  Agence Nationale de la Recherche (ANR)   Grant  tomaTE,  ANR-17-CE20-0024-02  and  ANR TREMPLIN-European Research Council  Grant  RITMO ,  ANR-17-ERC2-0013-01.
In order to convert the backscattered radiation profiles into water vapor mixing ratio profiles, the calibration coefficient is calculated from water vapor column ancillary data: GNSS (Global Navigation Satellite System) IWV (Integrated Water Vapor). The description of the calibration method and the total uncertainty budget can be found in  Vérèmes et al. (2019). At the Maïdo Observatory, the lidar provides four to eight water vapor profiles per month. The calibrated lidar water vapor database extends from November 2013 to December 2017. The time slot of routine operations is around 19:00 to 01:00 (+1) local time, but there are intensive periods of observation during field campaigns that allow a longer measuring span. The Raman lidar water vapor observations were validated during the Maïdo ObservatoRy Gaz and Aerosols NDACC Experiment (MORGANE) intercomparison exercise in May 2015  (Vérèmes et al.
Satellite data The brightness temperatures of the infrared (IR) channel at 10. 8 µm of the geostationary weather satellite Meteosat-7 have been used to provide the regional characteristics of deep convection over the Indian Ocean. The satellite centered at 57. 5 • E provided images for the Indian Ocean from December 2005 to March 2017. Aura's Microwave Limb Sounder (MLS) v4. 2 water vapor and ozone data were included in the study to compare with the in situ measurements and to evaluate the spatial extent of the convective air masses measured at the observatory. In particular we have used water vapor from the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset  (Davis et al. , 2016). The SWOOSH dataset contains monthly mean stratospheric water vapor and ozone profiles from several satellite instruments for the period 1984 to present.
The data are available on a 3D (latitude/longitude/pressure) grid. The SWOOSH input data for the period August 2004 to the present day correspond to measurements from the Aura MLS satellite. The MLS water vapor data are available on a pressure grid with 31 levels between 316 and 1 hPa (e. g. , the vertical resolution is ranging from 1. 3 to 3. 6 km between 316 and 1 hPa). The estimated accuracy for MLS water vapor decreases from 20 % at 216 hPa to 4 % at 1 hPa and is ∼ 10 % in the TTL region (150-70 hPa). Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) has been making backscatter measurements at 532 and 1064 nm since June 2006. We use the total attenuated backscatter coefficients β 532 available from the CALIPSO V4.
10 level 1 lidar data products. Following  Vaughan et al. (2004) , the attenuated scattering ratio SR 532  (Eq. 3 in Vaughan et al. , 2004)  profiles are computed as the ratio of β 532 (corrected for molecular attenuation and ozone absorption) to the molecular backscatter coefficient β m. β m is calculated using the number density of molecules from the GEOS-5 global model of the NASA Global Modeling and Assimilation Office (GMAO) and the Rayleigh scattering cross section. More details are given in the CALIOP Algorithm Theoretical Basis Documents (ATBDs; cf. Eqs. 4. 13a and 4. 14).
Convective cloud coverage was estimated using 3-hourly Meteosat-7 infrared brightness temperatures at 5 km resolution. A threshold of 230 K is used to detect deep convective clouds in the Meteosat-7 brightness temperature data (i. e. , pixels with brightness temperatures less than 230 K correspond to convective clouds). This threshold has been previously used to identify convection on geostationary satellite infrared images (e. g. ,  Tissier et al. , 2016). This temperature corresponds to a height of about https://doi. org/10. 5194/acp-20-10565-2020 Atmos. Chem. Phys. , 20, 10565-10586, 2020    The quasi-biennial oscillation (QBO) also affects TTL temperatures and humidity (e. g. ,  Zhou et al. , 2001; Yuan et al. , 2014; Davis et al. , 2013). Following  Davis et al. (2013) , we defined a QBO index as the zonal mean (10 • S-10 • N) of the difference in the ERA-Interim zonal wind at 70 and 100 hPa.
3 •. The latitude-height cross section of CALIOP SR 532 in Fig. 5  corresponds to measurements with a 60 m vertical resolution. The horizontal interval of the CALIOP data along its orbit is 330 m; for this study we use a 9-point running average to reduce noise. Figure  5a  and b show significant structure in the RH ice profile measured on 25 January 2016. Higher values of RH ice (> 40 %) between 13 and 15 km coincide with higher values of CALIOP SR 532 between 12 and 15 km. The RH ice reaches its maximum value at the cold point altitude (17. 3 km). The CALIOP SR 532 indicates a cirrus cloud between ∼ 12 and 15 km north of the island. The cirrus layer extends from ∼ 16. 2 to 20 • S corresponding to a horizontal scale of ∼ 400 km. Meteosat-7 infrared brightness temperature at 21:30 UTC, so ∼ 10 min before the CALIPSO overpass at 21:39 UTC in Fig.