dataesr/scientific-paragraphs-categorization huggingface dataset:
Above 1 Hz, the sensors have a flat response to ground velocity and they were connected to Nanometrics Centaur digitizers logging data at 500 samples per second. At the end of July (day 212), we added a sixth sensor of the same type to this array. The data of this station were recorded by an Omnirecs DATA-CUBE3 at 200 samples per second. The aperture of this array was 360 m, and power supply was achieved via batteries charged by solar energy. In mid-July on days 202 to 204, we installed three additional arrays, each consisting of five stations with an aperture of 100 m. For each of these stations, we used a three-component 4. 5 Hz geophone (PE-6/B manufactured by Sensor Nederland) connected to an Omnirecs DATA-CUBE3 logging ground velocity at 400 samples per second. The geophones were installed in the snowpack and later on ice (for details see  Lindner et al.
, 2019)  while the digitizers stayed at the surface to retain GPS capability. Power supply for these stations was achieved via alkaline batteries which needed to be replaced on a weekly basis. In the following, consistent with the station names, we refer to our four arrays as A0 (stations PM01-PM06), A1 (PM11-PM15), A2 (PM21-PM25), and A3 (PM31-PM35). While A0 recordings are continuous (apart from gaps due to station maintenance), recordings from the other arrays suffer from occasional power outages and frequently exhibit gaps over midnight of up to 26 min. A0, A1, and A2 stations recorded data through early September (days 250 to 252, respectively), and A3 stations were dismantled on August 23 (day 236) due to a slushy snow layer at the glacier's surface. In addition to the seismogenic ground motion, we surveyed the (low-frequency) glacier surface motion due to ice flow and glacier hydraulics at three locations using GPS units (Fig.
1a ) (2 h sampling interval after post-processing). Furthermore, we make use of the following time series: discharge in the Simme River to the north (measured ≈ 4 km from the terminus of Rätzligletscher), level of the outlet stream (≈ 1. 5 km from the terminus of Rätzligletscher), and level of Lac des Faverges. Simme discharge is provided as hourly averages by Switzerland's Federal Office for the Environment, and the stream and lake level are provided through a monitoring program conducted by the municipality of Lenk and the company Geopraevent. The lake level was monitored www. the-cryosphere. net/14/287/2020/ The Cryosphere, 14, 287-308, 2020 by a pressure sensor (Fig. 1 ) sampling data at 10 min intervals. 3 Data and observations.
2b ). As the lake level fell to the elevation of the moulin inlet, the direct connection between moulin and lake became disrupted. Subsequently, the lake connected to the moulin through a supraglacial channel which steadily incised deeper into the ice but slowed down the drainage (6-11 m 3 s -1 ). The exact transition time to this state is unknown but was within the first day of the drainage initiation. Discharge magnitudes similar to those of the lake drainage period were also measured in the Simme River prior to the lake drainage (three peaks on days 213-225) and after the lake drainage (days 248-252). Most of these discharge peaks can be linked to rainfall events having a shorter duration than the lake drainage (precipitation data are provided by the Switzerland's Federal Office of Meteorology and Climatology MeteoSwiss).
Acknowledgements. The geophones and DataCubes for 15 stations were provided by the  Geophysical Instrument Pool Potsdam (GIPP)  under the project AnICEotropy. We thank  Andreas Bauder , who installed the GPS stations, and  Philippe Limpach , who processed the GPS data, as well as  Lorenz Meier  from  Geopraevent  for sharing the data from the lake monitoring. We are also grateful to our technicians Pascal Graf and  Christian Scherrer , as well as to  Adrian Doran  and all other people who helped in the field. We appreciate the collaboration with the municipality of Lenk and would like to acknowledge logistical support from  Remontées Mécaniques de Crans-Montana (CMA) , the  Swiss Armed Forces , and the  Federal Office of Civil Aviation. We used ObsPy (Beyreuther et al. , 2010) for seismic data processing and created the figures with the Matplotlib plotting library for Python (Hunter, 2007).
We acknowledge the constructive comments from the editor  Jürg Schweizer ,  Alex Brisbourne , and the anonymous reviewer. Financial support. This research has been funded by the  Swiss National Science Foundation  ( GlaHMSeis  project (grant no. PP00P2_157551 )). Review statement. This paper was edited by  Jürg Schweizer  and reviewed by  Alex Brisbourne  and one anonymous referee.
Data availability. Seismic data used in this study are accessible via the repository of the Swiss Seismological Service under network code 4D (https://doi. org/10. 12686/sed/networks/4d, Swiss Seismological Service, 1985). GPS data are available upon request. Discharge data are available via Switzerland's Federal Office for the Environment and precipitation data via the Federal Office of Meteorology and Climatology MeteoSwiss.
1  and Fig. 2 ) were incorporated to build a multivariate model for prediction. These variables were included and/or removed based on the significance of the partial F-values. The final models were selected when the inclusion of more spectral bands was statistically not justifiable. This analysis was conducted with the PROC REG statement in the statistical analysis software SAS  (82)  in a random training set of the total data (60% of the observations). The prediction models were selected based on their low value for Mallows' Cp estimator and high coefficient of determination (R 2 ). The estimate of the RMSE of each prediction model was calculated in the validation data set, the remaining 40% of the observations and the final models were selected based on the lowest value of the RMSE.
325 to 1. 075 µm, and the spectral resolution is 3 nm. The hyperspectral images were georeferenced and ensembled using the image analysis software ERDAS®. Digital counts (DCs) were extracted individually for each tarp and for individual plots with the software ENVI®. A linear regression model for a single spectral band was developed using light reflectance captured with the spectroradiometer from the tarps as the response variable (dependent) and the DCs as the independent variable. The linear equations were utilized for the estimation of the total canopy reflectance of each of the two hundred spectral bands in each plot.
Efficiency of indirect selection of EW with spectral information The fourteen spectral indices (EWI) and the eleven regression models developed in this study (Table  3 ) were calculated with the ground based and aerial spectral information collected in the four experimental trials. Each of the indirect selection methods (spectral indices and models) was considered as an independent variable and subjected to an analysis of variance (ANOVA) for an alpha-lattice experimental design with the lmer function included in the package lme4 in the statistical software R. The variance components were extracted with the function varComp and estimates of the heritability in a broad sense (h 2 ) calculated according to the formula described by  (83) : h 2 = 𝜎 𝑔 2 𝜎 𝑔 2 +(𝜎 𝑒 2 𝑟 ⁄ ) where   2 corresponds to the genetic variance,   2 to the error variance and r is the number of replications in the experimental trial.
Results Onset of facial hair as a marker of male puberty timing. Data on relative age of voice breaking and relative age of first facial hair were available in up to 207,126 male participants in the UK Biobank (UKBB) study. For each of these measures, participants were asked if the event occurred relative to their peers: younger than average, about average or older than average. We previously reported that signals for AAM in women show concordant associations with dichotomised voice breaking traits in these UKBB men  5  , but to our knowledge no genetic study has previously evaluated timing of facial hair appearance as a marker of pubertal development. We defined two dichotomous facial hair variables in UKBB men: (1) relatively early onset (N = 13,226) vs. average onset (N = 161,175); and (2) relatively late onset (N = 26,066) vs.
average onset. These facial hair traits were in concordance with similarly dichotomised voice breaking traits defined in the same individuals, although more men reported early or late facial hair onset (39,292; 19. 6%) than early or late voice breaking (19,579; 10. 2%) (Supplementary Table  1 ). To test the ability of these facial hair traits to detect puberty timing loci, we assessed their associations with previously reported AAM loci. Of the 328 reported autosomal AAM signals for which genotype data were available in UKBB, 266 (81. 1%, binomial-P = 1. 2 × 10 -31 ) and 276 (84. 1%, binomial-P = 2. 1 × 10 -38 ) showed directionally concordant individual associations with relatively early and relatively late facial hair, respectively. Furthermore, substantially more AAM signals showed at least nominally significant associations (GWAS P < 0.
05) with relatively early (102, 31. 1%) or relatively late facial hair (152, 46. 3%) compared to ~16 expected by chance for each outcome (Supplementary Data 1). A multi-trait GWAS for puberty timing in men. We analysed four GWAS models in UKBB men (imputation v2, ~7. 4M SNPs): two models (early and late) for each of relative timing of voice breaking and facial hair. The shared genetic architecture between these four traits was high (genetic correlations (r g ) aligned to later timing of voice breaking ranged 0. 57-0. 91; Supplementary Table  2 ), and all four showed high genetic correlation with the continuously measured age at voice breaking in 23andMe (r g 0. 61-0. 81). These high correlations supported the rationale to combine the GWAS data across all five strata using MTAG  10. This approach enables genetic data from correlated traits and overlapping samples to be combined in a single analysis, provided three key assumptions are met: (1) homogeneity of variance-covariance matrix for effect sizes for all SNPs across all traits; (2) sampling variation can be ignored and (3) sample overlap is adequately captured.
Simulations involving large numbers of traits with extreme sample overlap have demonstrated that assumptions (2) and (3) may safely be made for most applications of MTAG. Violation of the assumption of homogeneity of effect sizes may be plausible in this setting if some SNPs have an effect on voice breaking and not facial hair (and vice-versa). We therefore calculated the upper bound for the false discovery rate (FDR) in our meta-analysis using the 'maxFDR' calculation developed by the MTAG authors. As the value for maxFDR was 7. 9 × 10 -4 , this indicates that test statistics are unlikely to be biased due to any violation of the homogeneity assumption. We therefore aimed to enhance power to identify GWAS signals for continuous age at voice breaking (as recorded in 23andMe) by using MTAG to combine data from the four dichotomous UKBB puberty timing traits all aligned to later timing of voice breaking; this approach yielded an effective GWAS meta-analysis sample size of 205,354 men for continuous pubertal timing.
In this combined MTAG dataset, we identified 7,897 variants associated with male puberty timing at the genome-wide statistical significance threshold (GWAS-P < 5 × 10 -8 ), comprising 76 independent signals (Fig. 1 ; Supplementary Data 2). The most significantly associated variant (rs11156429, GWAS-P = 3. 5 × 10 -52 ) was located in/near LIN28B, consistent with previously reported studies in men and women. Of these 76 signals, 29 were not in linkage disequilibrium (LD, conservatively defined as r 2 > 0. 05) with a previously reported signal for puberty timing (either AAM in women or voice breaking in men) (Table  1 ). We sought collective confirmation of the 76 independent signals for male puberty timing in up to 2394 boys with longitudinally assessed pubertal sexual characteristics in The Avon Longitudinal Study of Parents and Children (ALSPAC)  11.
Consistent with genetic analyses of AAM in females, tissues in the CNS were the most strongly enriched for genes co-located near to male puberty timing associated variants (Supplementary Figs. 1  and 2 ; Supplementary Data 6 and Supplementary Table  4 ). To identify mechanisms that regulate pubertal timing in males, we tested all SNPs genome-wide for enrichment of voice breaking associations with pre-defined biological pathway genes. Four pathways showed evidence of enrichment: histone methyltransferase complex (FDR = 0. 01); regulation of transcription (FDR = 0. 02)); ATP binding (FDR = 0. 03); and cAMP biosynthetic process (FDR = 0. 03) (Supplementary Data 7). Links between hair colour and puberty timing. Noting that three loci for puberty timing were located proximal to genes previously associated with pigmentation (HERC2, IRF4, C16orf55), we assessed the broader relationship between these traits.
72 × 10 -19 ) and lower risk for late voice breaking (P IVW = 5. 90 × 10 -6 ); but we found no association with AAM in women (P IVW = 0. 23) (Supplementary Table  5 ). Male puberty timing and other complex traits and diseases. To assess the extent of shared heritability between male puberty timing and other complex traits, we calculated genome-wide genetic correlations across 751 complex traits/datasets using LD score regression  21  (Supplementary Data 8). Apart from other puberty and growth-related traits, the strongest positive genetic correlation was observed with 'overall health rating', followed by several social traits, including educational attainment, fluid intelligence score and ages at first/last birth. Conversely, male puberty timing showed negative genetic correlations with cardiometabolic diseases, including Type 2 diabetes and hypertension, as well as health risk behaviours, including alcohol intake frequency and smoking (Supplementary Fig.
Methods 23andMe study. Genome-wide SNP data were available in up to 55,871 men aged 18 or older of European ancestry from the 23andMe study. Age at voice breaking was determined by response to the question 'How old were you when your voice began to crack/deepen?' in an online questionnaire. Participants chose from one of seven pre-defined age bins (under 9, 9-10 years old, 11-12 years old, 13-14 years old, 15-16 years old, 17-18 years old, 19 years old or older). These were then rescaled to one year age bins post-analysis by a previously validated method  27. SNPs were excluded prior to imputation based on the following criteria: Hardy-Weinberg equilibrium P < 10 -20 ; call rate < 95%; or a large frequency discrepancy when compared with European 1000 Genomes reference data  6. Imputation of genotypes was performed using the March 2012 'v3' release of 1000 Genomes reference haplotype panel.
Gene annotation and identification of loci. For each independent signal identified in the voice breaking meta-analysis, we identified all previously reported age at menarche (AAM) and voice breaking (VB) loci within 1MB of that SNP. We assessed if each locus had previously been associated with puberty timing (AAM or VB) within 1MB, and if any such loci within 1MB were in LD (r 2 < 0. 05). Heterogeneity between AAM and VB for each SNP was determined by the I 2 statistic and P-value generated by the METAL software. Gene annotation was performed using a combination of methods. Information on the nearest annotated gene was obtained from HaploReg v4. 1. In addition, other genes in the region were identified using plots produced from LocusZoom. The most likely causal variant was determined by combining this information with identification of any non-synonymous variants within the region as well as application of existing knowledge.
Replication in ALSPAC. The Avon Longitudinal Study of Parents and Children (ALSPAC) recruited pregnant women resident in the Avon area of the UK with an expected delivery date between 1 April 1991 and 31 December 1992. Since then, mothers, partners, and offspring have been followed up regularly through questionnaires and clinical assessments  11. The offspring cohort consists of 14,775 liveborn children (75. 7% of the eligible live births). Full details of recruitment, followup and data collection have been reported previously  11. Ethical approval for the study was obtained from the ALSPAC Ethics and Law committee and the Local Research Ethics Committees. A series of nine postal questionnaires regarding pubertal development was administered approximately annually from the time the participant was aged 8 until he was aged 17.
The genetic risk score (GRS) was calculated based on 73 SNPs (genotypes at 3 SNPs were unavailable) weighted by the effect size reported for that SNP in the ReproGen Consortium. The GRS was standardised, and results are presented as increase in the phenotype per standard-deviation increase in the GRS. Linear (continuous phenotype) and logistic (binary phenotype) regression analyses were performed unadjusted and adjusted for age (except for age at PHV, age at voice change and age at which armpit hair started to grow) and controlled for population stratification using the first 10 principal components. Gene expression and pathway analysis. We used MAGENTA to investigate whether genetic associations in the meta-analysed dataset showed enrichment in any known biological pathways. MAGENTA has previously been described in detail  29.
In brief, genes are mapped to an index SNP based on a 150 kb window, with a regression model applied to correct the P-value (gene score) for gene size, SNP density and LD-related properties. Gene scores are ranked, and the numbers of gene scores observed in a given pathway in the 75th and 95th percentiles are calculated. A P-value for gene-set enrichment analysis (GSEA) is calculated by comparing these values to one million randomly generated gene sets. Testing was completed on 3216 pathways from four databases (PANTHER, KEGG, Gene Ontology and Ingenuity). Significance was determined based on an FDR < 0. 05 for genes in the 75th or 95th percentile. To determine tissue-specific expression of genes, we used information from the GTEx project. GTEx characterises transcription levels of RNA in a variety of tissue and cell types, using sample from over 1000 deceased individuals of European, African-American and Asian descent.
In both cases, blond hair was used as the reference group and models were adjusted for the top 10 principle components to account for population structure. In men this produces an effect estimate as an odds ratio for early puberty (relative to blond-haired individuals), while in women the effect estimate is on a continuous scale for AAM (in years) relative to the mean AAM for those with blond hair. Genetic correlations. Genetic correlations (r g ) were calculated between age of puberty in males and 751 health-related traits which were publically available from the LD Hub database using LD Score Regression  21, 30. Mendelian randomisation analyses. GWAS summary statistics for longevity were obtained from Timmers et al. 31. Briefly, Timmers et al. performed a GWAS of parent survivorship under the Cox proportional hazard model in 1,012,050 parent lifespans of unrelated subjects using methods of Joshi et al.
32  , but extending UK Biobank data to that of second release. Data were meta-analysed using inversevariance meta-analysis results from UK Biobank genomically British, Lifegen, UK Biobank self-reported British (but not identified as genomically British), UK Biobank Irish, and UK Biobank other white European descent. The resultant hazard ratios and their standard errors were then taken forward for two-sample MR using the 74 male puberty loci which were available in both datasets. We then performed a sensitivity analysis after removing outlier SNPs on the basis of heterogeneity using MR-PRESSO  33. Summary statistics for the association between the genetic variants and risk of prostate cancer were obtained from the PRACTICAL/ELLIPSE consortium, based on GWAS analyses of 65,044 prostate cancer cases and 48,344 controls (all of European ancestry) genotyped using the iCOGS or OncoArray chips  34.
Unbalanced horizontal pleiotropy was tested based on the significance of the intercept term in MR-Egger regression. The total effect of puberty timing on prostate cancer risk was separated into a direct effect (independent of BMI, a potential mediator) and an indirect effect (operating via BMI), as described in Burgess et al. 19. Genetic associations between hair colour and puberty timing were assessed in two ways. First, we performed a two-sample MR analysis based on summary statistics (as described in Burgess et al. 19  ) using the most recently reported GWAS for hair colour  18  and 23andMe data for SNP effect estimates on age at voice breaking in men, and published ReproGen consortium data on AAM in women. However, there is partial overlap (between samples used for the discovery phase GWAS of hair colour variants (n = 290,891 from 23andMe plus UK Biobank) and the samples used for puberty timing (n = 55,871 men from 23andMe; maximum potential overlap = 55,871/290,891 = 19%).
Acknowledgements This work was funded by the  UK Medical Research Council  (MRC; Unit programme  MC_UU_12015/2 ) and used  UK Biobank data  under application 9905. The  MRC, Wellcome Trust  (Grant ref:  102215/2/13/2 ) and the  University of Bristol  provide core support for ALSPAC. This publication is the work of the authors and <John Perry and  Ken Ong>  will serve as guarantors for the contents of this paper. GWAS data for.
Data availability Puberty associations: The results for the top 10000 SNPs detailed in this manuscript can be found at this https://doi. org/10. 17863/CAM. 46703. UK Biobank: Data are available to all bona fide researchers for all types of health-related research that is in the public interest, without preferential or exclusive access for any person. All researchers, whether in universities, charities, government agencies or commercial companies, and whether based in the UK or abroad, will be subject to the same application process and approval criteria. 23andMe: Full summary statistics for 23andMe datasets will be made available to qualified researchers under an agreement that protects participant privacy. Researchers should visit https://research. 23andme. com/dataset-access/ for more details and instructions for applying for access to the data.
Dissolved 238 U and salinity across the entire Peruvian OMZ displayed poor linear correlation regardless of seawater oxygen concentrations (Fig. 7a-b ). The general consen-sus is that U behaves conservatively in oxic seawater in the open ocean, and early observations have shown that 238 U activities can be calculated from salinity based on a simple linear correlation between the two (e. g. ,  Chen et al. , 1986; Ku et al. , 1977). Compilations in  Van Der Loeff et al. (2006)  and  Owens et al. (2011)  further demonstrated that the majority of uranium data points in the global seawater dataset follow a linear correlation with seawater salinity. The 238 Usalinity formulations from either  Chen et al. (1986)  or  Owens et al. (2011)  are thus generally appropriate for open-ocean conditions and have been widely used in 234 Th flux studies.
, 2017; Stramma et al. , 2008). Future studies should take into consideration the large temporal variations of the residence times of total 234 Th in order to properly evaluate how carbon biogeochemical cycles and carbon export efficiency in these OMZs will respond to continuing ocean deoxygenation. Data availability. Data are available in supplementary tables and archived at https://doi. org/10. 1594/PANGAEA. 921917 (Xie et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-17-4919-2020-supplement. Author contributions. RCX, FACLM and EAP designed the study. RCX carried out sampling, onboard beta counting of 234 Th, and drafted the manuscript. IR conducted 234 Th and 238 U analyses at the home laboratory. JL computed current velocities and vertical diffusivities respectively from VmADCP and microstructure profiler data.