dataesr/scientific-paragraphs-categorization huggingface dataset:
Calcareous silty clay The discovered volcanic particles of OH-DP-0027 are highly vesicular micro-pumices with spherical or elongated vesicles and glass shards with thick septa or a platy shape. The composition of OH-DP-0027 is Na-phonolitic (Table  1 , Fig. 2a ) and thus similar to cryptotephra Co1262-709  (Wagner et al. , 2012)  and the associated cryptotephra horizons Table  1. Average major element glass composition (normalized to 100 %) of investigated tephra layers OH-DP-0027 to OH-DP-2060 of the DEEP site sequence, the correlations discussed, and the standards used for evaluation of the accuracy of the analyses. All data of the Ohrid tephra layers were obtained using an electron microprobe outfitted with WDS. The total is given as analytical total, and the tephra layers printed in italics are not correlated with the respective Ohrid tephra and are discussed only.
Acknowledgements. The SCOPSCO Lake Ohrid drilling campaign was funded by  ICDP , the  German Ministry of Higher Education and Research , the  German Research Foundation , the  University of Cologne , the  British Geological Survey , the  INGV  and  CNR  (both Italy), and the governments of the republics of Macedonia (FYROM) and Albania. Logistic support was provided by the  Hydrobiological Institute in Ohrid. Drilling was carried out by Drilling, Observation and Sampling of the Earth's Continental Crust (DOSECC) using the Deep Lake Drilling System (DLDS). Special thanks are due to  Beau Marshall  and the drilling team. Ali Skinner  and  Martin Melles  provided immense help and advice during logistic preparation and the drilling operation. Edited by:  F. Wagner-Cremer.
The gradient of the slope, called the b-value, gives a measure of the distribution of earthquake magnitudes. A higher b-value implies a higher abundance of smaller events. Several studies have sought to map b-values using various methods. Studies range from regional studies of a number of subduction zones  (Wiemer & Wyss 1997 , 2000; Katsumata 2006; Amorèse et al. 2010; Vorobieva et al. 2013; Nishikawa & Ide 2014; Tormann et al. 2015)  to more local studies of specific volcanic areas  (Wyss et al. 1997 ) and during the aftershocks of big events  (De Gori et al. 2014). Depending on the comprehensiveness of the seismicity catalogue, variation in b-value can be investigated laterally, vertically and/or temporally. Microseismic data from hydraulic fracturing  (Williams & Le Calvet 2013)  and from laboratory experiments  (Scholz 1968 ) has also been used to better understand factors that affect magnitude distributions.
DATA We use two seismicity catalogues to estimate b-values (Fig. 2 ). The first is provided by the Seismic Research Centre (SRC) of The University of the West Indies (The UWI) from the years 2008 to 2013 (hereafter referred to as SRC2008). It uses the Duration Magnitude (Md) for all events with values rounded into 0. 1 bins. The second earthquake catalogue is from the two Observatoires Volcanologiques et Sismologiques (de la Guadeloupe: OVSG; and de la Martinique: OVSM) of the Institut de Physique du Globe de Paris (IPGP) from the years 1996 to 2012 (hereafter referred to as OVS1996). In order to make a direct temporal comparison with SRC2008, we also analyse a subset of this data between 2008 and 2012 (OVS2008), results for which are given in the supplementary figures. This catalogue does not use magnitude bins for the events, but reports magnitudes for each event to the second decimal place.
Here, a lower significance level increases the uncertainty. We use a level of 5 per cent. Choosing both a high significance level for the main calculation and a low significance level for its uncertainties generates a stricter evaluation of significant b-value variation. The Magnitude of Completeness (M C ; also called 'minimum magnitude' or M min ) is chosen automatically to be the smallest magnitude at which the similarity between the data curve and the   4. Note that the SRC2008 data are collated in 0. 1 magnitude intervals. The arc has a region of low seismicity around the island of St Vincent (∼12. 5 • N-13. 5 • N), visible in the SRC2008 catalogue. Note that OVS1996 is incomplete south of 13. 5 • N. (b) Histograms of the magnitude distributions for the different catalogues. Note the large number of events without magnitude information in the SRC catalogue, which have been excluded from our analysis.
(c) Average b-values for the two catalogues. The green and blue circles and lines represent the cumulative catalogue. The blue circles show all the events used to calculate the b-value (solid red line) and its uncertainties (dashed red lines). Therefore, the minimum magnitude is shown by the colour change of the circles from green to blue. straight line fit is sufficient, based on a manually preset significance level, so that there is no need to restrict the seismicity catalogue to events above minimum magnitude. The maximum-likelihood method was developed with the assumption that there is no magnitude uncertainty and no magnitude upper bound. However, it has been tailored using different constraints. If data are grouped into magnitude bins, the results are biased, but this bias is negligible for an interval as small as 0.
25b; this value decreases to σ b ∼ 0. 1b for simulated data with N = 100. Case 2: the cells are circular but we fix the number of events per cell to 100. The distance of the furthest event therefore varies according to the density of events in that area (cf. Wiemer & Benoit 1996; Wyss et al. 1997). The maximum allowed search distance was set to 100 km for OVS1996 and OVS2008 and 300 km for SRC2008. Note that as the grid-search method is set up using x-y coordinates, it does not explicitly consider depth (z) when calculating distances. However in our case since most of the events follow the downgoing slab the method serves as a basic tool to map the variation in b-values with depth. Case 3: we use a 2-D analysis of the b-value with the x-axis being the arc contour ('along-arc'). The arc contour is shown in Fig.
Challenges working with b-values Incomplete earthquake data sets: Artefacts can be introduced by incomplete earthquake data sets, so that different minimum magnitudes have an effect on the results  (Frohlich & Davis 1993; Vorobieva et al. 2013). Following large main shocks the seismicity catalogues can be incomplete due to missing aftershocks  (Enescu et al. 2009). Rather than using an overall M min we use the full dataset, calculating individual M min for each b-value estimation with the significance level of the KS-test. Our tests show that a truncation at M min = 2. 5 does not change the result. Temporal variation: Previous studies have shown that the b-value can vary over time (see Smith 1981 for a detailed discussion). These temporal changes in the magnitude range could bias the results  (Frohlich & Davis 1993; Wiemer & Wyss 2000).
Column 4: Number of events per cell. The dashed black line shows the minimum required number for the b-value calculation (50). Note that there is no set maximum number of events per cell; we only cut the figure at 500 events per cell for clarity. plotted for the entire dataset. Even given the uncertainties and errors in both the b-value calculation and fracture zone positioning within the subduction zone, the relationship is clear. An explanation can be found in water delivered to the subduction zone via fracture zones. The spatial correlation between fracture zones and higher b-values is consistent with a recent proposal of  Manea et al. (2014) , using a combination of arc magma geochemistry and geodynamic modelling to show that fracture zones play a fundamental Figure  9. Results for the along-arc observation using fixed numbers of events per cell.
See Suppl. Fig. 4  for further explanations. Figure  S10. Results for the along-arc observation using fixed cell sizes. This figure extends Fig. 8  of the main paper by including OVS2008 (B). OVS2008 is a subset of OVS1996 (A) decimated to match the temporal range of SRC2008 (C). Tests on this dataset were made to ensure the b-value results were not biased by the earthquake catalogue used. Here, column 3 shows the required size of the cell to reach the defined number of events. The dashed black line shows the maximum allowed width for the b-value calculation (100 km). Both catalogues show a significantly higher b-value around the island of Dominica. For OVS1996 further significantly higher b-values can be identified around the island of Montserrat and the South of Martinique. Figure  S12. Results for the along-arc observation for OVS1996 using different methods.
The islands are (from north to south): S, Saba; StE, St Eustatius; StK, St Kitts; Mo, Montserrat; Gu, Guadeloupe; D, Dominica; Mq, Martinique; StL, St Lucia; StV, St Vincent; Gr, Grenada. The triangles mark the location of the three-component broadband stations of the SRC (yellow) and the IPGP (blue) networks used in this study. (b) Location of the study area in a broader tectonic setting. The position of map (a) is indicated by the red outline. (c) Vertical gravity gradient of the latest satellite altimetry-derived grid (Sandwell et al. 2014)  for the Central Atlantic from the Mid-Atlantic Ridge (black line) in the east to the Lesser Antilles in the west. To map the most significant fracture zones (black lines, named) we followed the methods of Matthews et al. (2011)  for the satellite data combined with an inspection of shipboard magnetic data.
All the synthetics and observed data were sampled to 20 Hz and filtered between 0. 005 and 0. 08 Hz (i. e. for the period range 12. 5-200 s). We measured relative traveltimes and amplitudes by comparing observed and synthetic waveforms. Traveltimes and amplitudes can either be handpicked or measured with cross-correlation (e. g. Dahlen & Tromp 1998; Maggi et al. 2009). Here, we automatically pick the negative and positive peaks of both S and ScS wavelets independently. Thence, we define the traveltime as the midpoint time of the negative and positive wavelet peaks, and the amplitude as a difference between the two peaks (see Fig. 2 ). Note that this method is rapid and appropriate for the data set we used, since waveforms filtered up to 0. 08 Hz have simple shapes, and that the results obtained by handpicking or cross-correlation methods lead to similar values.
A C K N O W L E D G E M E N T S We are grateful to Laura Cobden and an anonymous reviewer for their detailed and constructive reviews that helped improving a first version of this article. This study was supported by Academia Sinica Career Development Award 102-CDA-M02. We acknowledge NIED (National Research Institute for Earth Science and Disaster Prevention, Japan) for providing us the F-net data, and Martin Vallée for providing SCARDEC source time function database. partial derivatives. The synthetic waveforms used for the estimation are computed in the same way and are filtered with a bandpass filter (0. 005-0. 08 Hz). We pick S wave peaks (up-down) in all the observed and synthetic waveforms, as shown in Fig. 2  and stack them by the centre time of the S peaks after normalization by the amplitude of the peaks.
! 22! To create SMART cloud files from Atmos haze outputs, we have written a script that bins the haze particles generated by the photochemical model into specified radii (also called particle "modes") while preserving the total mass of each atmospheric layer. The particle mode sizes we use span from 0. 001 μm -2 μm; larger particles do not exist in our atmospheres due to rainout. Spherical modes are R= 0. 001 μm, 0. 005 μm, 0. 01 μm, and 0. 05 μm. Fractal modes are R = 0. 06 -2 μm with 4 modes between 0. 06 and 0. 1, 10 equally spaced modes between 0. 1 μm and 1 μm, and 2 μm. In total, this represents 19 particle modes. In each layer of the SMART cloud files, we include a mixture of two particle modes; the mass density contributed by the two modes is selected based on the distance in log space of the Atmos particle radius to each neighboring SMART size bin.
Acknowledgments This work was performed as part of the  NASA Astrobiology Institute's Virtual Planetary Laboratory , supported by the  National Aeronautics and Space Administration  through the  NASA Astrobiology Institute  under solicitation NNH12ZDA002C and Cooperative Agreement Number  NNA13AA93A. G. Arney  was supported in part by the  NASA Astrobiology Institute Early Career Collaboration Award. E. T. Wolf  acknowledges  NASA   Planetary Atmospheres Program  award  NNH13ZDA001N-PATM  and  NASA   Exobiology Program  award  NNX10AR17G  for financial support. B. Charnay  acknowledges support from an appointment to the  NASA   Postdoctoral Program , administered by  Universities Space Research Association. E. Hébrard was supported by an appointment to the  NASA   Postdoctoral Program  at  NASA Goddard Space Flight Center , administered by  Universities Space Research Association  through a contract with  NASA.
Simulations were facilitated through the use of the Hyak supercomputer system at the  University of Washington eScience Institute. We are grateful to  C. McKay  and three other anonymous reviewers whose comments substantially improved the quality of our manuscript. We thank  R. Buick ,  D. Crisp ,  N. Kiang , and  M. Parenteau  for conversations and advice. Spectra shown in this work will be archived at the  Virtual Planetary Laboratory  online spectral database. !81!.
Acknowledgments The authors are thankful to  F. Delbes  ( UPMC ) and  J. J. Pantel  for his help in sample crushing, to  C. Anquetil  and  F. Costa-Torro  ( CNRS ) for running 13 C NMR measurements, and to  E. Charon  for calibrating the Raman spectrometer. This work was supported by the  PNP  ( Programme National de Planetologie) program  of the  CNRS (Centre National de la Recherche Scientifique ), the  National Science Centre  grant  NN307468938 (BK ) and by the  ERC  project '' Paleo-NanoLife. ''We also acknowledge the anonymous reviewers for their constructive comments.
Acknowledgments. The authors extend their thanks to the  French Ministry of Agriculture , Forest (MAAF), and  Environment (MEEM) , the  Grant for State Key Program for Basic Research of China (973)  (No. 2013CB329405 ), the  National Natural Science Foundation  (No. 61573275 ), and the  Science and technology project of Shaanxi Province  (No. 2013KJXX-46 ) for their support.
The downward flux of DOC was 0. 3 ± 0. 1, 0. 3 ± 0. 0, and 0. 3 ± 0. 1 mmol m À2 d À1 in groups 1, 2, and 3, respectively (Table  S4  and Figures 3  and 4b ). The flux of DOC is identical in all three groups and is small relative to the magnitude of the POC export flux (1-10% of the POC flux, Tables  S1  and S2 ). At least for our Scotia Sea data set, DOC downward flux can therefore be ruled out as a factor driving the inverse relationship between PP and e ratio. On the seasonal scale, this may be different as DOC can also be exported as part of the restratification process in spring  [Xi et al. , 2014]. This could potentially result in larger downward DOC flux than the downward diffusive flux presented here. Also, the flux of DOC is, by definition, not included in the ThE i (or any export efficiency estimate apart from the f ratio  [Dugdale and Goering, 1967]  based ones) because ThE i is the ratio of POC exported over PP.
Acknowledgments A Naveira-Garabato ,  K. Sheen ,  M. Villa-Alfageme ,  A. J. Poulton ,  S. Richier ,  C. M. Moore ,  M. Hartmann ,  G. Taran ,  P. Ward , and  T. Tyrell , the scientific party, crew, and officer of  RRS James Clark Ross (British Antarctic Survey ) are acknowledged for their help, support, and advices. Geraint Tarling  ( British Antarctic Survey ) is warmly acknowledged for his great leadership during cruise JR 274 and scientific advices. The Ocean Productivity website (http://www. science. oregonstate. edu/ocean. productivity/) is acknowledged for providing primary production data. This study was funded by the  NERC   SeasFX  project (grant  NE/J004383/1 ). We thank  Natural Environment Research Council (NERC, UK) , the  Department of Environment, Food, and Rural Affairs (Defra, UK) , and the  Department of Energy and Climate Change (DECC, U.
Data Processing Around each high tide, a period of 2 h was selected when wave characteristics and water levels at the array were approximately stationary. The pressure data were, after correction for air pressure, converted to free sea-surface elevation using linear wave theory. Sensor heights with respect to the bed were measured manually during low tide and were linearly interpolated to determine the sensor heights during high tide. The sea-surface elevation and velocity signals were quadratically detrended and, when needed, resampled to 4 Hz. These were then band pass filtered into infragravity (0. 005-0. 05 Hz) and sea-swell frequencies (0. 05-1 Hz). Sea-surface elevations were processed per blocks of 30 min into water depth h, and into significant sea-swell wave height H SW , and significant infragravity-wave height H IG using standard spectral analysis.
When part of the sea-surface elevation and velocity time series showed alternatingly dry and wet conditions, the complete series was removed. High tides with alongshore-velocity variance in the infragravity band exceeding 75% of the cross-shore infragravity velocity variance were removed from both data sets. Shear wave energy contribution (calculated following  Lippmann et al. [1999] ) did not exceed 75% of the total infragravity energy. Imposing limits for the shear wave energy contribution, as performed by De  Bakker et al. [2014]  for the hydrodynamical analysis of the Ameland data, was not critical to our present results and was therefore foregone here. In total, 71 two hour periods were retained in the Ameland data set and 55 in the Sand Motor data set. OBSs are sensitive to both sediment properties [e.
The products of an SW and IG time series are neglected, as they are uncorrelated and therefore small compared with the other two components. Equations (  1 ) and (  2 ) can thus be written as ⟨uc⟩ = ūc + ⟨u SW ⋅ c SW ⟩ + ⟨u IG ⋅ c IG ⟩. (3) Values of q SW , q IG , and q mean were computed for 30 min blocks. The relative contribution of q IG to the sand flux rate was determined as |q IG |∕(|q IG |+|q SW |+|q mean |). Contributions of the other components were determined analogously. Although we measured u and c at different heights above the bed, we chose not to correct for this as effects for our data were estimated to be small (not shown) following the method of  Ruessink et al. [1998a]. In addition, the exact sensor heights during high tide were not known. To study the dependence of sand suspension events on the relative location of sea-swell waves, we determined the correlation r 0 between the sea-swell wave envelope A, and u IG.
1 and 0. 2 m, indicating that suspension peaks consistently spread higher up into the water column. However, correlations drop for the Ameland  data set when the sensor height of the upper OBS exceeds 0. 15 m, possibly because these measurements were then taken above the sand suspension layer. Phase lags were determined by calculating the phase of a cross-power-spectral density analysis of the two instantaneous concentration signals and a subsequent averaging over the infragravity wave band. Figure  10b  shows that the phase lag between the two concentration signals (where correlations are above 0. 6) is ≈0 ∘ , thus showing no phase lag higher up in the water column. Figure  10c  illustrates that, in general, as expected, q IG are smaller higher in the water column, but the dependence on H IG ∕H SW is similar.
Conclusions Measurements of near-bed velocity and sand concentrations were collected at up to four locations in the intertidal zone on a moderately and a gently sloping beach. On the whole, our data show that the local ratio of infragravity to sea-swell wave height is a good proxy for the infragravity-related sand suspension mechanism, the infragravity flux direction, and the relative importance of infragravity waves to the total cross-shore sand flux. On the moderately sloping beach, infragravity waves are typically small compared to sea-swell waves (H IG ∕H SW < 0. 4), despite offshore wave heights close to 5 m. Accordingly, the sand is suspended by the sea-swell waves, and the infragravity flux direction is linked to the correlation r 0 of the infragravity orbital motion with the sea-swell wave envelope.
Analysis of the LHCXs in the diatom genomes Phaeodactylum tricornutum and T. pseudonana LHCX gene model identifiers were retrieved from the diatom genomes, respectively, on P. tricornutum Phatr2 and T. pseudonana Thaps3 in the JGI database (http://genome. jgi. doe. gov/). Phaeodactylum tricornutum LHCX proteins were used as query to perform BlastP searches on the Pseudo-nitzschia multiseries (http://genome. jgi. doe. gov/Psemu1/ Psemu1. home. html) and Thalassiora oceanica (http://protists. ensembl. org/Thalassiosira_oceanica/Info/Index) genome portals. Best hit sequences were tested on Pfam (http://pfam. xfam. org/) to assess the presence of the Chloroa_b-bind domain (PF00504), characteristic of light-harvesting proteins. Protein alignments were performed with MUSCLE (http://www. ebi. ac.
rETR PSII was calculated as: Y2×light intensity, where Y2 is the efficiency of PSII. In silico analysis of the LHCX non-coding sequences Determination of motif occurrence and de novo search of over-represented motifs in the 5'-flanking regions (1000 bp or the entire intergenic sequence between the coding gene of interest and the upstream gene) and the introns of the PtLHCX genes were performed by the use of the FIMO (v4. 11. 1) and MEME Suite (v4. 9. 1) tools  (Bailey et al. , 2009) , with a p-value cut-off of 0. 0001. The Tomtom tool (v4. 11. 1) on the MEME Suite was used to compare motifs with known transcription binding sites. Microarray data from  Alipanah et al. (2015)  (accession GSE58946) were downloaded from the GEO database (http://www. ncbi. nlm. nih. gov/pubmed/23193258) using the GEO query package (http://www.
ncbi. nlm. nih. gov/pubmed/17496320). Data were loaded and analysed in the R environment using the Limma package (http://www. ncbi. nlm. nih. gov/ pubmed/25605792). Selection of transcripts categories: (i) up-regulated, log2 fold change (Fc) >3, adjusted p-value <0. 01 (122 genes); (ii) down-regulated, Fc <-3, adjusted p-value <0. 01 (200 genes). The P. tricornutum genome sequences and gff mapping of filtered gene models were downloaded from the JGI website and refer to Phatr2 (http://genome. jgi. doe. gov/Phatr2/Phatr2. home. html). The significance of motif enrichment was evaluated using the binomial test with a p-value cut-off of 0. 05. All analyses were performed using custom scripts in Perl and R.
For T. pseudonana (Thaps3), P. tricornutum (Phatr2), and P. multiseries (Psemu1), ID numbers refer to the genome annotation in the JGI database (http://genome. jgi. doe. gov/). For T. oceanica (ThaOc_1. 0), ID refers to the Ensembl Protist database (http://protists. ensembl. org/ Thalassiosira_oceanica/Info/Index?db=core). (+) and (-) indicate the forward and reverse chromosomal or scaffolds, respectively. The protein length and intron numbers are also indicated. Previous reports  (Lepetit et al. , 2013; Nymark et al. , 2013 ) indicate that the LHCX4 transcript is induced in dark-adapted cells. Therefore, we extended the analysis of the expression of the four LHCX genes to cells adapted to prolonged darkness (60 h). In these conditions, we observed a significant increase only of the LHCX4 mRNAs (Fig.