dataesr/scientific-paragraphs-categorization huggingface dataset:
Geophysical techniques are necessary to resolve fracture geometry and subsurface structure (e. g. , multi-frequency seismics) and physical parameters of sediments (e. g. , controlled source electromagnetics) across a wide range of length scales (m to km). At smaller (mm to cm) scales, sediment cores were sampled directly and their physical and chemical properties assessed using laboratory-based methods. Numerical modelling approaches bridge the resolution gap, though their validity is dependent on calibration and constraint from field and laboratory experimental data. Further, time-lapse seismic and acoustic methods capable of resolving temporal changes are key for determining fluid flux. Future optimisation of experiment resource use may be facilitated by the installation of permanent seabed infrastructure, and replacement of manual data processing with automated workflows.
A comprehensive analysis of 3D seismic reflection volumes in the South Viking Graben, North Sea (an area of 2850 km 2 ;  Karstens and Berndt, 2015)  identified 46 large-scale (~100-1000 m-wide) chimneys within the shallowest 1000 m of the overburden. Chimney-like features can also be the result of seismic imaging artefacts. Seismic imaging artefacts may arise both as a result of data acquisition and/or processing (e. g. ,  Tucker and Yorston, 1973). In particular, for locations where gas is present in the subsurface, the effect this has on seismic velocity determination can have a significant impact on both time and depth migration. An example locality where a seismic artefact was interpreted as a chimney is the Goldeneye field, a prospective CO 2 storage site in the Central North Sea. Following high resolution 3D seismic processing a feature previously interpreted as a fluid escape conduit was later reinterpreted as a seismic imaging artefact caused by a glacial tunnel valley  (Dean et al.
Overview & data acquisition The methods used to investigate chimneys and their associated fluid flow can be divided into several types (Table  1 ). These include: seismic reflection imaging, ocean-bottom seismic methods, including seismic tomography and anisotropy analysis, controlled source electromagnetic surveying (CSEM), active acoustics, passive seismic monitoring, sediment sampling of both the target site and onshore analogues, laboratory rock physics experiments, and process-based numerical modelling and fluid dynamic modelling approaches. In support of the STEMM-CCS and CHIMNEY projects, four research expeditions were undertaken for data collection at the Scanner pockmark complex. 2D seismic reflection and refraction data, for use in travel time tomography, were acquired using a GI gun source and 18 ocean bottom seismographs (OBS) by RV Maria S Merian cruise  MSM63 (Fig.
Time-lapse/4D seismic reflection Time-lapse seismic imaging refers to the acquisition of seismic data at the same location at different points in time, in order to assess temporal changes in the subsurface. Where both seismic datasets are 3D, this may be referred to as 4D seismic imaging. This technique has been applied extensively to subsurface reservoir monitoring, including conventional and unconventional hydrocarbon production (e. g. ,  Watts et al. , 1996; Landrø et al. , 1999; Barkved et al. , 2003). Changes in the subsurface due to fluid flow may change properties such as fluid saturation, temperature, porosity, and pressure, and, hence, the elastic and stress-strain properties and behaviour of the rock, which cause a change in seismic response  (Johnston, 2013). Such temporal changes can be used to better understand the formation and development of fluid flow features, as well as providing constraints for multiphase thermo-hydro-mechanical simulations.
, 2019). Time-lapse and/or 4D seismic data were not acquired at the Scanner pockmark complex. This technique has, however, been applied to the studies of actively-seeping chimneys, such as at Lomvi pockmark on the Vestnesa Ridge, offshore W Svalbard  (Bünz et al. , 2012; Smith et al. , 2014; Panieri et al. , 2017) , and for experimental fluid injections, including the Sleipner storage site (e. g. ,  Arts et al. , 2004; Chadwick et al. , 2004 Chadwick et al. , , 2019;; Boait et al. , 2012; Eiken, 2019) , the QICS experiment  (Cevatoglu et al. , 2015) , and the STEMM-CCS Goldeneye release experiment  (Flohr et al. , 2021; Roche et al. , in review). Time-lapse seismic data therefore play a key role in the understanding of both naturally occurring fluid flow, and CCS monitoring in pre-, active and post-CO 2 injection phases  (Lumley, 2010).
Seismic tomography and full waveform inversion The P-and S-wave velocity structure of a chimney and its surroundings can be determined by applying travel time tomography and full waveform seismic inversion. These techniques are applied with the principal aims of: locating gas-bearing zones; delineating the shape of the chimney; determining the presence and features of fractures (open or cemented, size and connectivity); and characterising the sediment properties within and outside the chimney. Seismic travel time tomography is an inversion technique in which observed travel times (e. g. , Fig. 4b ) are compared to those computed through a discretised and parameterised model representation of the subsurface, with defined parameters that control the balance between minimising the data misfit and generating a model with the minimum required structure to fit the data.
The towed receiver data, sensitive to the top ~50-100 m, agree well with predicted data for a 1. 2 Ωm subsurface resistivity, while the OBEM data at larger offset to the source agree better with higher resistivities, indicating an increase in resistivity with depth. In c), the location of the grey box corresponds to observed E z values when the transmitter is above the pockmark. In d), a slight increase of P max at ~-200-400 m (highlighted with grey box), however, may indicate a localised increase of resistivities in the subsurface. e) OBEM instrument P max data example from SW to NE trending profile (red line). At about 100-400 m from the Scanner pockmark position, a localised increase of P max is observed, potentially related to an increase in resistivity in the subsurface in the vicinity of the seismic chimney.
The impact of partial saturation on anisotropy and attenuation in materials of known fracture density and orientation has been studied using models that can link laboratory and field datasets  (Amalokwu, 2016) , which are required to make robust determinations of permeability. The Scanner pockmark anisotropy experiment used several seismic sources with different frequencies, in the range ~10 Hz to 2 kHz, as described above (Fig. 3b ). This broadband dataset was specifically designed for the measurement of frequency-dependent anisotropy, to permit enhanced fracture characterisation. In order to achieve maximal azimuthal coverage, which is necessary for determining the directionality of anisotropy, profiles were acquired at multiple orientations through the OBS array (Fig. 3c ). Several approaches are available for investigation of frequency-dependent anisotropy associated with chimneys.
Furthermore, the spatially discrete nature of offshore site investigations means that subsurface information must be gathered from all potential future sites of interest to CCS, to understand the geometry of the structures that may be present. Depending on the availability of prior information about the specific imaging target, such as its spatial and depth scale, and the practicalities of implementing different imaging techniques, it may be possible to conduct experiments where multiple data sets are acquired concurrently, reducing the time-cost of the operation. For example, during the cruise JC152 seismic experiments at Scanner pockmark both the travel time tomography and seismic anisotropy datasets were collected using the same sets of shots, while the GI, Duraspark and Squid sparker sources were also recorded using a towed seismic streamer.
Acknowledgements This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 654462  ( STEMM-CCS ) and the  Natural Environment Research Council  (CHIMNEY;  NERC Highlight Topic ;  NE/N016130/1 ). We would like to thank all those involved in the planning and acquisition of data during research cruises MSM63,  JC152 ,  POS518  and  MSM78 , including the officers, engineers and crews, the scientific parties, and all seagoing technicians and engineers. The  NERC Ocean-Bottom Instrumentation Facility  (Minshull et al. , 2005) provided the OBSs and OBEs and their technical support at sea during JC152. OBSs and technical support at sea during MSM63 were provided by  GEOMAR. RD2 is operated by the  British Geological Survey.
Global Seamount Census Our analysis is founded on the seamount dataset of  Kim & Wessel (27, hereafter KW11) , which includes ∼25,000 seamounts with heights in excess of 100 m in areas away from continental margins. Approximately 8,500 of these seamounts are taller than 1 km. Those authors predict that their database likely underestimates the global inventory of seamounts by nearly a factor of two, such that the total number of seamounts may lie in the 40,000-55,000 range. Thus, the KW11 database is conservative, as it includes a significantly lower number of seamounts than earlier predictions. For example, Wessel et al. (25)  reported more than 100,000 seamounts with heights exceeding 1 km, and speculated that there are probably 25 million seamounts taller than 100 m. Similarly, Yesson et al.
(26)  reported ∼140,000 seamounts with peak heights between 500 m and 1 km, and ∼ 33,500 seamounts taller than 1 km. The KW11 data incorporates corrections for the ambiguity in gravity signals due to small seamounts and for the overlap with abyssal hills. As a result, seamounts captured by the KW11 data are distinct from abyssal hills, and correspond instead to active or extinct undersea volcanoes with heights in excess of 100 m. It is worth noting that in the three realistic simulations used in this study, each using high resolution bathymetry derived from multi-beam surveys, there are multiple examples of seamounts that are not found in the KW11 seamount census, emphasizing the conservative seamount count of KW11. Figure  1b  shows the global distribution of the KW11 seamounts, coloured by their height.
Global Deep-Ocean Mixing and Upwelling Next, we explore the global implications of seamount induced mixing by applying Eq. (  1 ) to the KW11 dataset and climatological hydrographic data (WOCE). Specifically, we only account for the turbulence induced by the shear in between the layered wake vortices (Bu > 1), which is absent from representation of ocean turbulence in climate models (and hence is the novelty of this work), and not the mixing associated with seamountradiated internal lee waves or wave-vortex interactions which can have overlaps with existing mixing estimates. The outcome is shown in Fig. 5a  -the details of the map's construction are discussed in the Supplementary Materials. For comparison, panels c and d respectively display the diffusivities associated with internal tides and lee waves on an illustrative deep-ocean density level.
5e ), where red and blue respectively denote local lightening (i. e. , diapycnal upwelling) and densification (i. e. , diapycnal downwelling)see Supplementary Materials for a description of the calculation. As found by other recent works (42; 2; 43; 44), downwelling is prevalent in the interior of basins, but intense upwelling occurs near topographic boundaries, where the selected isopycnal approaches the seafloor. When integrated globally (Fig. 5f ) or across the Pacific (Fig. 5h ), as much as tens of Sverdrups (1 Sv ≡ 10 -6 m 3 s -1 ) of deep waters down-or upwell across isopycnals, leading to a smaller residual net (i. e. , upwelling minus downwelling) diapycnal transfer on the order of 1-10 Sv for all deep-ocean density classes (Figs. 5g, i ). Examination of the individual contributions of seamount-, internal tide-and lee wave-generated turbulence to the water mass transformation rate reveals that seamount-induced mixing is a leading-order player in both the seamounts also contribute to mixing, (III) we only accounted for shear-induced turbulence in between layered vortical wakes; seamounts generate mixing due to wave generation, wave-vortex interactions, near boundary instabilities, and tide-seamount interactions, none of which were accounted for here, and (IV) the formula used to infer mixing from the seamount dataset seems to underpredict mixing compared to our regional high-resolution model outputs.
Discussion We have demonstrated that theoretical descriptions of the turbulent mixing generated by flow impingement on seamounts, which were developed for highly idealized scenarios, hold broadly for realistic flow and topography configurations. By conservatively applying these theoretical ideas to global seamount and oceanographic datasets, we have shown that turbulence associated with seamount-generated layered vortices makes a leadingorder contribution to deep-ocean mixing and upwelling -comparable to contributions from other, much more extensively studied sources of turbulence. We conclude that seamount-induced turbulence may be a significant, hitherto-overlooked new player in the ongoing debate around the closure of the overturning circulation's deep upwelling limb  (45; 46; 47; 48; 49; 50; 51; 52; 53).
This appears particularly plausible near the equator and in the subtropics, where the layered vortex regime highlighted in our work occurs widely. Despite this regime's likely important role in shaping the deep ocean circulation, there have been, to our knowledge, no observations of seamount-generated layered vortices and their associated mixing to date. As such, the mixing effects of seamounts are absent from state-of-the-art climate-scale ocean models. Furthermore, one of the largest uncertainties in the estimates presented here is the potentially significant underestimate in the number of seamounts in the KW11 dataset, thus future improvements to global bathymetric maps will also play an important role in quantifying seamount driven mixing. We suggest that addressing these major knowledge gaps will require not only first-of-their-kind observations and model parameterizations of seamountgenerated turbulence, but also advances in dynamical understanding of the evolution of the separated vortices.
Climate Data Collection A weather station was installed at the site in a forest gap in order to measure the microclimatic conditions. Air temperature ( • C) and rainfall (mm) were collected at 15-min intervals and recorded as hourly averages by means of CR1000 data-loggers (CAMPBELL SCIENTIFIC Corporation, Logan, UT, United States). For technical problems during winter months, absolute maximum and minimum air temperature ( • C) were referred to different duration's observation (Table  1 ).
Statistical Analyses We estimated the probability of co-occurrence of time-windows of needles flushing (phases 3-6) and feeding larvae (fourth to sixth instars) for each tree species during each growing season from 2016 until 2019 using SAS (SAS 9. 4, SAS Institute, Inc. , Cary, NC). The classification method adopted a parametric approach based on multivariate normal distributions within each group to derive discriminant quadratic functions. We assumed unequal variances between groups using the observed withingroup covariance matrices  (Ren et al. , 2020). For referring to the host phenology in the statistical analyses, we considered the budburst in three periods, based on the days before, during and after the occurrence of the time-window of needles flushing, when buds are susceptible to defoliation (phases 3-6).
To describe the stem radius variation among different years and between species, we fitted a Gompertz function defined as: y = A exp -exp (β -κτ ) where y is the daily stem radius variation, τ is the time computed in day of the year (DOY), A is the upper asymptote of maximum stem radius variation, β is the x-axis placement parameter, as time at inflection point of the curve, κ is the growth rate of the curve  (Camarero et al. , 1998; Rossi et al. , 2003). The selection of the Gompertz function was based on the lower Akaike information criterion (AIC) and the goodness of fit of the curves was evaluated graphically using the residuals plots of each curve. We then performed a Linear Mixed Models (LMM) with PROC MIXED procedures of SAS 9. 4 (SAS Institute, Cary, NC) to assess the effect of year and species, and their interaction the estimates of the coefficients of the Gompertz functions and the stem radius values at the beginning of budburst (SRF).
Year and species were considered fixed factors. Then, in the linear mixed models, trees (classified as numeric codes) were included as a random factor and defoliation was used as a covariate. To assess stem variation radius phases, the stem cycle was extracted using a three-step procedure composed of two SAS routines (SAS 9. 4, SAS Institute, Inc. , Cary, NC, United States)  (Deslauriers et al. , 2011). The procedure divides the series into two distinct phases: (1) contraction, the period between the first maximum radius and the following minimum radius; and (2) expansion, considered as recovery period, the period from minimum radius until the return to the previous maximum value or when the stem begins another contraction phase  (Turcotte et al. , 2009). SAS routines calculated the amount of stem radius variation and the duration of stem cycle phases  (Deslauriers et al.
, 2011). Linear Mixed Model (LMM) was used to analyze differences in the amplitude and duration of the contraction and expansion phases between species and among needles flushing period (before, during, and after), and the species × needles flushing period interaction. Species and periods were defined as fixed factors, whereas trees (classified as numeric codes) were used as random factor. Defoliation (%) was included as covariate. For the analysis, we used a linear MIXED model procedure in SAS (SAS 9. 4, SAS Institute, Inc. , Cary, NC, United States). The firstorder autoregressive [AR(1)] provided the suitable correlation structure  (Wolfinger, 1993). The selection of the first-order autoregressive [AR(1)] structure was based on the lower AIC. We evaluated normality and homoscedasticity graphically using the residuals plots of the linear models  (Quinn and Keough, 2002).
As numerous external specifiers are listed, the name is defined in such a way to closely retain the currently understood content of the clade, while allowing Sandowniidae to be nested within other clades, such as Chelonioidea or Thalassochelydia, but not Protostegidae. A similar clade was previously defined as Angolachelonia by  Mateus et al. (2009)  but also including the Late Jurassic Solnhofia parsonsi as an internal specifier. While S. parsonsi may very well belong to Sandownidae based on numerous shared apomorphies (Evers and Joyce 2020), the phylogeny of  Mateus et al. (2009)  is not reproducible (due to the absence of the dataset) and we, therefore, follow  Tong and Meylan (2013)   Ye, 1986 (Fig. 3a ). Reference Phylogeny-  Evers and Benson (2019, Fig. 12 ). Composition-Protostegidae, as here defined, is currently believed to consist of a speciose clade of Early to Late Cretaceous marine turtles with a worldwide distribution  (Kear and Lee 2006; Cadena and Parham 2015; Evers and Benson 2019).
X-ray crystallography Single-crystal X-ray diffraction intensity data were collected on tennantite-(Hg) using a Bruker Smart Breeze diffractometer (50 kV, 30 mA) equipped with a Photon II CCD detector and graphite-monochromatised MoKα radiation (Dipartimento di Scienze della Terra, Università di Pisa). The detector-to-crystal distance was set at 50 mm. Data were collected using ω scan mode in 0. 5°slices, with an exposure time of 90 s per frame, and corrected for Lorentz and polarisation factors as well as for  absorption using the software package Apex3 (Bruker AXS Inc. , 2016). The refined unit-cell parameter is a = 10. 455(7) Å and V = 1143(2) Å 3 in space group I43m. The crystal structure of tennantite-(Hg) was refined using Shelxl-2018  (Sheldrick, 2015)  starting from the structural model of tetrahedrite-(Hg) given by  Biagioni et al.
0897 for 214 reflections with F o > 4σ(F o ) and 22 refined parameters. Details of data collection and refinement are given in Table  3. Fractional atomic coordinates and equivalent isotropic displacement parameters are reported in Table  4. Table  5  reports selected bond distances. Finally, Table  6  gives the results of the bond-valence calculations obtained using the bond-valence parameters of  Brese and O'Keeffe (1991). The crystallographic information file has been deposited with the Principal Editor of Mineralogical Magazine and is available as Supplementary material (see below). Owing to the small size of the available crystal of tennantite-(Hg), powder X-ray diffraction data were not collected. The calculated pattern, based on the structural model given in Table  4 , is reported in Table  7.
Acknowledgements. N. Meisser  thanked for additional details about the finding of tennantite-(Hg) from the  Chandolin Alp, Switzerland. CB and MP acknowledge financial support from the  Ministero dell'Istruzione, dell'Università e della Ricerca  through the project  PRIN 2017 "TEOREMdeciphering geological processes using Terrestrial and Extraterrestrial ORE Minerals ", prot. 2017AK8C32. The study was also financially supported by the  Ministry of Culture of the Czech Republic  (long-term project  DKRVO 2019-2023/1. II. c; National Museum, 00023272 ) for JS and ZD. The comments of  Peter Leverett ,  Xiangping Gu , and an anonymous reviewer improved the original manuscript.
Marine surface sediments The sample dataset includes 100 core-top sediment samples from the ocean floor and two samples collected at the mouths of the Promira and Sado rivers. The 100 samples were collected from several marine sedimentary cores from 13 oceanographic cruises carried out between 1981 and 2013 on the Iberian margin (8 W-13 W/43 N-36 N) (Fig. 3 ), recovered by various coring methods (mostly box, piston and multicore) (Table  S1 ). Samples were recovered in the most superficial sections of the sedimentary cores, depending upon the availability of sediment. Our dataset contains 23 samples taken at core depths 0e0. 5 cm, 50 samples at 0e1 cm, 22 samples at 0e2 cm, 4 samples at 2e2. 5 cm and 1 sample at 4e5 cm core depths. 33 samples were collected from cores retrieved in the Galician continental margin, including 21 samples located between 9 W and 13 W and 43 N and 42 N and distributed between the continental slope and the abyssal plain (1129 m and 5378 m bsl).
Explanatory variables, data sources, treatments and analyses Climate data, temperature and precipitation were extracted from the CRU (Climatic Research Unit) database (Version: CRU CL v. 2. 0, grid with spatial resolution: 10 min, period: 1961e1990) of  New et al. (2002). The Net Primary Productivity (NPP) data come from the GFED4s (Global Fire Emissions Database that includes Small fires) (Version: GFED4s, grid with spatial resolution: 0. 25 , period: 1997e2015  (Giglio et al. , 2013) ). Fire regime data were derived from the global database of fire patches (FRY_MCD64) from the MODIS sensors on board the Aqua and Terra satellites  (Laurent et al. , 2018) , i. e. the burnt area in hectares (ha) and the Fire Radiative Power FRP (the energy emitted by fire measured in Watts) for each fire over the period 2001e2016  (Laurent et al.