dataesr/scientific-paragraphs-categorization huggingface dataset:
For this study, POC (only available for the dry season samples) was found to be as high as 2. 23 mg. L -1 , with a mean concentration of 1. 42 ± 0. 26 mg. L -1 (N = 19) in the VDG and 1. 33 ± 0. 06 mg. L -1 (N = 10) in DH (Table  2 ). No significant difference in POC between the two locations was found (Table  3 ). In open ocean waters,  Morel (1988)  proposed the empirical relationship POC = 90 Chla 0. 57 (R 2 = 0. 68; N = 409), but in coastal waters, such robust relationships between POC and Chl-a may not be observed because of the higher non-phytoplankton contribution (e. g. ,  Sathyendranath et al. , 2009). For our dataset, after converting POC from mg. L -1 to mg. m -3 , we obtained POC = 1,444 Chla 0. 19 (R 2 = 0. 40; N = 29; p < 0. 005; Figure  3C ; Tables  2, 5 ). TSS samples were also examined in relation to their POC content to assess their relative fraction of organic material but this was not possible due to a poor covariation of POC with TSS (R 2 = 0.
More recently,  Vantrepotte et al. (2015)  retrieved DOC from a CDOM (412) using linear regressions and a dataset of contrasting coastal waters, comprising samples from the English Channel (R 2 = 0. 72), Vietnam (R 2 = 0. 81), and French Guiana (R 2 = 0. 78). For our dataset, there was no correlation between DOC and a CDOM (400) (R 2 ∼ 0. 1; p > 0. 1; N = 55; Figure  4C ). DOC was found to be significantly different between both seasons and locations (Tables  2, 3 ). It was higher during the wet season (1. 39 ± 0. 29 mg. L -1 ; N = 13; Table  2 ) and varied spatially from lower concentrations in the embayment of the VDG (∼0. 8-1. 0 mg. L -1 ) increasing toward Clarence Strait (>1. 0 mg. L -1 ) and reaching up to 2. 35 mg. L -1 in DH (Figure  1 ). Overall DOC (1. 34 ± 0. 37 mg. L -1 ; N = 10) in DH was found to be much lower than that observed by  Burford et al.
20, N = 37; Figure  4A ). Also a proxy for salinity, a CDOM can be used as a marker of freshwater influence during flooding conditions (e. g. ,  Schroeder et al. , 2012) , but for our dataset, there was a very weak relationship between salinity and a CDOM (Figure  4B ): albeit with a poor relationship, CDOM was found to increase with salinity during the dry season (R 2 = 0. 14; N = 26; Figure  4B ), while it decreased with salinity during the wet season (R 2 = 0. 34; N = 11) possibly due to its terrestrial source (thus in higher concentration near the rivers). Its spectral slope, S CDOM , was not seasonally or spatially uniform (0. 008-0. 017; N = 38) but statistically, no seasonal or spatial difference was found (Tables  2, 3 ; Figure  5A ). S CDOM was within the set range described by  Blough and Del Vecchio (2002).
, 2004). For our dataset, b bp (555) surface values varied three-fold (0. 02-0. 07; N = 36; Figure  7D ; Table  2 ). We found a significant difference in b bp (555) between locations and seasons (Tables  2, 3 ). Although, its mean value of 0. 03 was above that found by  Whitmire et al. (2007)    440 nm (Figure  8D ). The combined absorption by NAP and CDOM generally exceeded 70% of the total absorption from 400 to 620 nm. The second highest combination was CDOM and phytoplankton (>60%), while NAP and phytoplankton contributed 50-60% for the same wavelengths. The median contribution of phytoplankton at 440 nm was 18% overall (Figure  8D ), increasing to >55% at 665 nm. Darwin Harbour showed a mixed CDOM and NAP assemblage. The particulate absorption coefficient, a P (440), was found to be equally correlated with both TSS (R 2 = 0.
For the VDG dry season samples, fucoxanthin/Chl-a ratios were typically one order of magnitude higher than those of zeaxanthin, confirming that diatoms were the most abundant phytoplankton group (>70%; N = 36). In the VDG, Chl-c, an accessory pigment in diatoms, was found to be more abundant than Chl-b (53%), a pigment generally associated with e. g. , picophytoplankton Prochlorococcus which was mostly present in DH (72%). The pigment ratio Chl-b/Chl-a was low in the waters along Melville Island and higher in Darwin Harbour, while Chl-c/Chl-a increased from the VDG coast to Melville Island and was higher in Darwin Harbour but lower in the VDG wet samples. The VDG dry season dataset was characterized by higher PSC (Figures  11A-D ), while the VDG wet and DH samples were higher in PPC. The PPC/PSC ratio provides a photo-physiologic index for phytoplankton cells.
There was a significant correlation between a phy (440) and Chla (R 2 = 0. 55; p < 0. 001; N = 50; Figure  12B; Table 5 ), and no significant difference was found between the relationship derived from our dataset and that of  Bricaud et al. (2010)  (p > 0. 1) or  Bricaud et al. (2004)  (p > 0. 1). There was no significant seasonal variation in a phy (440/676) (Figure  12A ; Table  3 ), and no significant difference was found between the model derived for this dataset and that of  Bricaud et al. (1995)  (N = 50; p > 0. 05), therefore inferring that any of these models can be used in the VDG and/or DH (Figure  12 ). A significant difference (p < 0. 005) however, was found for a phy * (440) between locations (Figure  12C ; Table  3 ), thus reflecting the difference in phytoplankton composition between the VDG and DH.
Effect of Local and Seasonal Forcing The major processes controlling the optical properties in DH and the VDG are mainly the wind speed and direction during the dry season, and the tides year round. In addition, the river discharges in this region are controlled by seasonal rainfall and thus add to the complexity of this coastal system by increasing land-sourced CDOM delivery to the coastal waters. The coastal waters of Northern Australia have a tidal range that is amongst the largest in the world for a coastline facing an open ocean. For our dataset, the tidal range varied from 1 to 6 m. Stations located in DH (dry season only; N = 24) were mostly sampled during neap tides, with a mean tidal range of 2. 05 ± 1. 04 m. In the VDG however, the tidal ranges were 20% higher at the time of our dry season sampling (4.
For our study, the DH stations were sampled during the clearest conditions (neap tides) and thus our DH dataset may not be the most representative of this environment. However, it provides an interesting comparison with the VDG that is further discussed in the Section Challenges for Remote Sensing of Water Quality in the NMR. In addition to tidal movement, another physical forcing explaining the higher TSS concentrations in the VDG during the dry season are the prevailing south-easterly winds occurring at this time of the year. These trade winds, when acting in phase with the tidal currents, significantly enhance resuspension. While the recorded wind speeds were very low during the wet season field campaign (<2 m. s -1 ; W-SW), winds of up to 10 m. s -1 occurred during the dry season sampling.
g. ,  Pusey et al. , 2015). The seasonal variability in river runoff also directly results in significant difference in salinity between seasons. The large amount of runoff reaching the coastal waters at the end of the wet season (∼April), which coincided with the timing of our VDG 2012 wet season field campaign (Table  1A ), explains the lower salinity observed during this seasonal sampling. Terrestrial runoff plays a role in CDOM characterization, by changing its slopes and concentrations. Higher a CDOM (440) values were observed during the wet season but surprisingly, these concentrations were not found to be significantly different from the dry season. Neither was a relationship found between a CDOM and salinity, but it has to be stressed that the CDOM dataset for the wet season was limited (N = 10), with only four stations having salinities lower than 28 PSU.
, 2003)  with a relatively constant spectral slope (0. 029 ± -0. 003), therefore inferring that only a small fraction of the DOC was derived from terrestrial sources  (Fichot and Benner, 2012). The underlying assumption that CDOM properties can be inferred from DOC concentrations  (Shanmugam, 2011; Hestir et al. , 2015)  was, however, not verified in this dataset. Other recent studies also reported the complete lack of relationship between DOC and CDOM (e. g. ,  Nelson and Siegel, 2013)  because the controlling factors for DOC and CDOM were different  (Yamashita et al. , 2013).
Conversely, in situ Chl-a concentrations weakly increased from The number of samples, N, varies due to quality control. 0. 6 to ∼0. 75 mg. L -1. We can only interpret this comparison with caution for at least two reasons: first, because of the limited in situ dataset used for this exercise, and second because of the seasonal dominance of CDOM and NAP in this system which will inevitably hamper accurate satellite retrievals, of Chla in particular. This discrepancy also highlights the need to parameterize a region-specific remote sensing algorithm for the NMR. A seasonal parameterization, based on the wet and dry season in situ optical observations presented in this study, has been applied to MODIS-Aqua imagery of the VDG  (Schroeder et al. , 2015). In recent years, ocean color remote sensing has provided a powerful means for studying ocean biogeochemistry and ecosystems over large spatial scales  (Gardner et al.
CONCLUDING REMARKS The continental shelf of the NMR is the widest on the Australian continent, it is also one of the most pristine coastal environments worldwide  (Morrison and Delaney, 1996; Halpern et al. , 2008). Currently, the Northern Territory has no specific coastal management legislation or coastal climate change policy in place. Catchments of this region and the coastal marine environment are highly connected, yet the VDG lacks a coastal environmental monitoring program extending beyond the boundary of the well-managed Kakadu National Park. Therefore, long-term monitoring of key bio-physical coastal water quality parameters of the VDG, from in situ and satellite observations, would be of benefit to the regional environmental management. But, to date, the lack of in situ optical datasets in the NMR has limited the derivation of regional ocean color satellite algorithms for this region.
The dataset presented in this study fills this gap and is the first collected in the tropical, optically complex coastal waters of VDG and DH. Results from this study generally show that these two regions are separate coastal environments with different optical characteristics. The VDG was found to be mostly dominated by CDOM during the wet season and NAP during the dry season, while DH has a mixed absorption budget. The phytoplankton populations are also different, with the VDG being characterized by bigger phytoplankton cells (diatoms) in comparison to DH, due primarily to differences in nutrient stocks (Table  4 ) and light availability. The strong, south-easterly winds and the tidal energy are a combination that increases water turbidity in the Gulf during the dry season, thus limiting light penetration and hence phytoplankton growth.
ACKNOWLEDGMENTS The authors would like to thank  Dr. Arnold G. Dekker  ( CSIRO ), for valuable comments on the manuscript prior to its submission,  Dr. A. Bricaud  ( LOV, Villefranche-sur-mer ) for assistance with the selection of phytoplankton spectra suitable for this analysis,  Dr. Ian Leiper  ( Charles Darwin University ) for help with ArcGIS and some of the field sampling in  Darwin Harbour , and  Ms. Julie Haines  ( CSIRO Adelaide, Waite Campus ) for the analysis of the DOC and POC samples. This research was funded by the  North Australia Marine Research Alliance (NAMRA)  and the  Australian Government's National Environmental Research Program (NERP). VB was supported by the  European Union  ( FP7-People Co-funding of Regional ,.
FUNDING This research was funded by the  North Australia Marine Research Alliance (NAMRA)  and the  Australian Government's National Environmental Research Program (NERP). VB was supported by the  European Union  ( FP7 -People Co-funding of Regional, National , and International Programmes, GA no. 600407 ) and the  CNR   RITMARE Flagship Project. In-kind support was provided by the  CSIRO  and the  Australian Institute of Marine Science (AIMS).
83 0. 002*** 1;14. 43 0. 001*** b bp (555) 1; 25. 28 0. 001*** 1;37. 58 0. 001*** ϒ 0. 475 (NS) ∼1 (NS) b bp (555) 1; 8. 30 0. 008*** 1; 4. 55 0. 045** b bp *(555) 0. 006*** 0. 08 (NS) a NAP *(440) 0. 001*** 0. 15 (NS) S NAP 1. 00 (NS) 0. 001*** S cdom 0. 18 (NS) 0. 22 (NS) c(555) 0. 003*** 0. 008*** Fucoxanthin 1;1. 89 0. 18 (NS) 1;7. 71 0. 011** Salinity 0. 001*** 1;33. 81 0. 001*** Temperature 0. 001*** 0. 001*** Secchi 0. 004** 0. 001** *If only the p-value is indicated, a non-parametric test was applied on the dataset. p*** < 0. 01; ** < 0. 05; NS (i. e. , not significant; p > 0. 05).
Acknowledgements Open access funding provided by  FH Salzburg -University of Applied Sciences. The authors gratefully acknowledge the support of the  ERA-Net wood-wisdom program  for the project " Biocopol-enhancing wood durability and physical properties through innovative bio-based sustainable treatments" as well  as the  ANR-10-EQPX-16-Xyloforest  for funding. Dr. Jinbo HU  acknowledges to the supports, which are provided by  Scientific Research Fund of Hunan Provincial Education Department  ( 16B281 ) and  Scientific Research Foundation for High-level Personnel of Central South University of Forestry and Technology  ( 2015YJ023 ) in China.
Compliance with ethical standards Funding The study was financed for one part by the  European Union  with the  ERA-Net wood-wisdom program  with the project " BioCoPolenhancing wood durability and physical properties through innovative bio-based sustainable treatments " and one part was supported by the  French funding agency   ANR-10-EQPX-16-Xyloforest. Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The setup consisted of a monochromatic beam at 96. 5 keV (single Laue crystal) from a W150 wiggler (gap 34 mm), 5 m of propagation (sample-detector distance), a tapered fiber optic scintillator, an x0. 3 magnification set of lenses and a FReLoN-2k14 CCD camera producing data of 45. 5 µm voxel size. The tomography was reconstructed based on 5,000 projection of 0. 5 s exposure each over 360 • with the center or rotation shifted near the edge of recorded radiographs to almost double the horizontal reconstructed field of view. Additionally the fossil was set in a tube filled in with 5 mm Al balls to homogenize attenuation and increase exposure while preventing saturation of the detector (i. e. , attenuation protocol). Tomographic correction was done with the program PyHST which generated a stack of 16 bits TIF files.
Ring corrections was performed on this stack of TIF files. Finally data was binned to produced an isotropic voxel size of 91 µm to reduce the overall load on computer for segmentation. The braincase of this skull was scanned at the ID17 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France; proposal ES339). The setup consisted of a FReLoN-2eV camera, a 0. 5× magnification set of lenses, a 2 mm LuAG scintillator, a monochromatic X-ray beam of 150 keV (bent double-Laue crystals) and a sample-detector distance of 10. 9 m to perform Propagation Phase Contrast Synchrotron micro Computed Tomography (PPC-SRµCT). The tomography was computed based on 2,510 projections (58 × 1,024 pixels, binning factor of 2) of 0. 5 s each over 360 • resulting in data with a 117. 23 µm isotropic voxel size.
An attenuation protocol  (Carlson et al. , 2001)  allowed an increase in the exposure time, to compensate for X-ray attenuation by the sample, without saturating the detector. Additionally, the centre of rotation was shifted by ∼35 mm to increase the horizontal field of view in the reconstructed data (i. e. , half acquisition protocol). Given the limited vertical field of view, 50 scans were necessary (30% of vertical overlap between two consecutive scans) to cover the full height of the sample. Tomographic reconstruction was performed using the single distance phase retrieval approach of the software PyHST2  (Paganin et al. , 2002; Mirone et al. , 2014). The resulting 32 bits data were converted to a stack of 16 bit tiffs using the min and max crop values from the 3D histogram generated by PyHST2.
, 2013; Laaß, 2015). Thus, even though the pineal tube is enormous and contributes a significant portion of the volume of the endocast in Moschops, the endocast volume in this study includes the pineal tube in order to allow comparisons with previously published datasets on therapsid endocast volume (a measure of the endocast volume excluding the pineal tube is nevertheless provided for comparison, Table  1 ). Published endocast volume data was used to compare the endocast volume of AM4950 to that of other therapsids (Table  1 ). As endocranial volume cannot be compared directly between species of differing body mass, we used the encephalization quotient (EQ) for comparative purposes  (Jerison, 1973; Manger, 2006; Hurlburt, Ridgely & Witmer, 2013). The EQ is the ratio between the endocast volume of a given animal and the expected volume of the endocast for an animal of similar body mass  (Jerison, 1973; Manger, 2006; Hurlburt, Ridgely & Witmer, 2013).
We used three different types of EQ calculations in our dataset. First,  Jerison's (1973)  EQ, which is calculated as follows: Jerison's EQ = (endocast volume in cm 3 )/(0. 12 * (body mass in g) 2/3 ). This is the most commonly used EQ in the literature, but since Jerison's EQ is often criticised as it is based on a visually, rather than a mathematically, fitted regression line, we also used the regression equation for calculating EQ from  Manger (2006). This is considered more accurate as it is defined mathematically. Though they give similar results, Manger's EQ is preferred over Eisenberg's EQ  (Eisenberg, 1981)  because it incorporates more species into the calculation of the regression equation, and excludes outliers such as primates and cetaceans. It is expressed as follows: Manger's EQ = (endocast volume in cm 3 )/(0.
0535 * (body mass in g) 0. 7294 ). Both these EQ calculations were determined to compare relative brain size across mammals. Since early therapsids are known to have endocranial volumes closer to that of extant reptiles than mammals  (Jerison, 1973; Quiroga, 1980; Quiroga, 1984; Kielan-Jaworowska, Cifelli & Luo, 2004; Rowe, Macrini & Luo, 2011; Rodrigues, Ruf & Schultz, 2013a; Rodrigues, Ruf & Schultz, 2013b) , we used a third expression of EQ calculated by  Hurlburt, Ridgely & Witmer (2013) , which is adapted to the comparison of smaller endocranial volumes. This EQ is calculated as follows: Hurlburt et al. 's EQ = (endocast volume in cm 3 )/(0,0155 * (body mass in g) 0,553 ). This EQ usually gives higher values due to the dataset on which the regression is based. In all cases, the larger the brain relative to the predicted brain size, the larger the EQ.
ACKNOWLEDGEMENTS We thank  B De Klerk  and the  Albany Museum  for the specimen loan,  Dr. R Redelstorff  for help with SAHRA regulation and  S Jasinoski  ( ESI ) for advice. We acknowledge the  European Synchrotron Radiation Facility  for provision of synchrotron radiation facilities and we would like to thank  P Tafforeau  for assistance in using beamline ID17. We also thank the three reviewers,  S Lautenschlager ,  C Kammerer  and  S Walsh. SAHRA PermitID :  2060.
Scaling Relations The observed hR| ai was computed by combining the cluster data in all focus regions. This was done by first calculating the conditional mean cluster rain rate for a set of cluster area intervals for the individual Z × Z domains (that are land-free and dominated by deep convection) disregarding which focus region the domain belongs to. Subsequently, the ensemble average of the conditional mean cluster rain rate at each cluster area interval was used as an estimate of hR| ai for each Z. We found that the conditional mean can be modeled as a power law with a crossover exponent as illustrated by Figure  6 : Rja h i ¼ ka b 1 ≤ a ≤ a 1 ka bÀc 1 a c a 1 < a < Z 2 ( (6) Note that the observed hR| a = 1i in Figure  6  deviated from equation (  6 ) due to the misidentification of random grid-point noises as rainy grid-points.
A3. Selecting Deep Convective Cluster Domains There are 40 different data series in the TRMM data set (from eight starting times a day over the first five consecutive days, with data points in each series spaced 5 days apart). To be admitted for further analysis, each Z × Z domain covering the focus regions (e. g. , one of the four domains with Z = 60 over IO) was tested for two conditions: 1. The land fraction coverage over the domain must be less than 10% of the domain area, Z 2. 2. Denote m j,k (Z) as the average cluster rain rate for the jth Z × Z domain satisfying condition (1) in the kth data series. Suppose there are J such domains over all ocean basins. For example, J = 16 if all 60 × 60 domains in the four ocean basins satisfy condition (1). We require that m j,k (Z) > M (Z) À S(Z) for all k, where M(Z) and S(Z) are respectively the average and standard deviation of the set {m j,k (Z) | j = 1, …, J; k = 1, …, 40}.
Acknowledgments We thank  Matthew R. Igel  and another anonymous reviewer for their suggestions and feedback for the manuscript. H. N. H. acknowledges the support of the  A*STAR   International Fellowship. The TRMM 3B42 is freely available from the website hosted by the  NASA   Goddard Earth Sciences Data and Information Services Center ,  GES-DISC , http://disc. sci. gsfc. nasa. gov. The MISR data were obtained from the  NASA Langley Research Center Atmospheric Science Data Center. This research is in collaboration with the  Center for Environmental Sensing and Modeling (CENSAM)  and is supported by the  National Research Foundation Singapore  under its  Campus for Research Excellence and Technological Enterprise program. CENSAM is an interdisciplinary research group of the  Singapore MIT Alliance for Research and Technology.
ORCA2-LIM-PISCES We use physical state fields from the global configuration (ORCA2) of Océan Parallélisé in the Nucleus for European Modeling of the Ocean (NEMO-OPA) coupled to the Louvain-la-Neuve Sea Ice Model, version 2 (LIM2;  Madec et al. 1998). The resolution of the model grid is 28 in longitude, with latitudinal resolution varying from 0. 58 at the equator to a maximum of 1. 98 at approximately 208N and 208S and 31 levels in the vertical. Climatological European Remote Sensing Satellite-1 and European Remote Sensing Satellite-2 scatterometer monthly mean wind stresses were used for the tropics, and the NCEP-NCAR climatological dataset was used poleward of 508N and 508S to drive the model. Surface salinities were restored to climatology. Mesoscale processes are parameterized using  Gent and McWilliams (1990) , with this parameterization producing bolus velocities that are then added to the prognostic circulation fields to provide tracer transports.
Acknowledgments. We thank  Matthew Thomas  for his help in configuring the subduction diagnostics using Ariane. We thank the three anonymous reviewers and the editor for their constructive comments and criticism. The work of K. T. is supported through  NSF  Award  OCE-1155983  and through the  Meteorological Research Institude's priority research fund for the study of ocean carbon cycle changes. The support of K. B. R is through  NOAA  Awards  NA17RJ2612  and  NA08OAR4320752 , which includes support through the  NOAA Office of Climate Observations (OCO)  as well as through  NASA  Award  NNX14AL85G. D. I. was partially funded by the Flagship Project RITMARE-The Italian Research for the Sea-funded by the  Italian Ministry of Education, University, and Research  within the  National Research Program   2001-13.