dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank all the people and institutions that supported fieldwork and data collection:  A. Evangelidis ,  T. Fransson ,  T. Curk ,  P. Glazov ,  T. Nakahara ,  F. Nakayama ,  T. Suzumegano ,  D. Jennings ,  R. Julian ,  I. MacLeod ,  G. Hickey ,  J. J. Moreno ,  W. Bouten ,  M. de la Riva ,  J. L. Barroso ,  M. Majem , the " Progetto Falco pescatore " team, the protected areas of  Maremma Regional Park ,  Tuscan Archipelago National Park, Diaccia Botrona Nature Reserve, WWF Orbetello Lagoon and Orti-Bottagone Nature Reserve in Italy, Massachusetts Audubon, New Jersey Audubon-Cape May Bird Observatory Center for Research and Education, Delaware State Parks, Newfoundland Power, Eversource Energy , the Jane B. Cook 1983  Charitable Trust ,  3M ,  Squam Lakes Natural Science Center's Innovative Project Fund , and  Meredith Bay Colony Club in USA , and the  Canada Research Chair Program.
For sulfate measurements, the same samples were diluted in water (1:50) and analyzed using ion chromatography (Metrohm 930 Compact IC flex with Metrosep A PCC HC/4. 0 preconcentration column, and Metrosep A Supp 5 Guard/4. 0 chromatography column). The porewater concentrations of ammonium, phosphate, nitrate/nitrite and silicate were determined using a continuous flow nutrient analyzer (QuAAtro39; Seal Analytical) as published previously  [33]. These data are compiled in the (S1 Table in S1 File) and are also documented online  [34]. As a complement to Rhizon sampling, additional geochemical analyses were performed on centrifuged porewaters and sediments from three layers per core (surface, middle, bottom) of 6-10 cm each (Table  1 ). Sediment cores were sampled in 3 cm intervals. Sediment samples of ca.
Bacterial and archaeal 16S rRNA gene Amplicon Sequence Variants (ASVs) were assigned taxonomy using the SILVA 132 database  [39]. For fungal ITS rRNA and 18S rRNA gene analyses, DADA2 was implemented in QIIME2  [40]. Fungal ASVs were assigned taxonomy by BLAST against the UNITE (v. 8. 0 2018-11-18) classifier  [41]. Taxonomy assignments and count tables were merged with metadata as S4 objects for ordination analyses (PCoA) and data visualization in R using the phyloseq  [42] ), vegan  [43] , and MASS  [44]  packages. Further information on methods and ASV numbers is documented in S2 Fig and  S6.
Taxonomy notes The classification scheme as implemented in the SILVA 132 database  [39]  is currently changing. For example, genomics-based taxonomy revisions have raised the Epsilonproteobacteria to phylum status [Epsilonbacteraeota,  [45] ] and reorganized the Deltaproteobacteria into several phylum-level lineages  [46]. Since the taxonomic landscape continues to evolve, we use designations that are currently widely shared, in particular phylum-level archaeal designations that have gained widespread usage during the ongoing rapid expansion of the archaeal domain [for example Bathyarchaeota,  [47, 48] ]. Roadmaps for achieving consistent taxonomy of the cultured and uncultured microbial world have been proposed recently  [49].
Environmental parameters shaping the fungal community composition The phylogenetic bubble plot profile (Fig 5 ) indicates that the fungi do not show taxon-specific preferences for any of the hydrothermal sampling areas or our control site, in stark contrast to the pattern observed for bacteria and archaea (S10 and S12 Figs in S1 File). To examine this observation more rigorously, PCoA analysis was performed on the complete fungal dataset of intergenic spacer sequences. This analysis confirmed the lack of clustering by sampling site (Fig 6, S14 Fig in  S1 File ), but revealed a contrasting pattern of tightly clustered surficial samples from all sampling sites (background, Aceto Balsamico, Marker 14 and Cathedral Hill) with positive axis 1 values (Fig  6 ). Negative axis 1 values and the full range of axis 2 contained a broad spread of deeper (and a few shallow) sediment layers from hot hydrothermal Cathedral Hill sites where fungal communities appear to be distinct.
Data Management Office  at the  Woods Hole Oceanographic Institution  under Project Number  474317  and also available in PANGAEA (Wegner et al. 2021). Funding: This project was supported by collaborative  NSF Biological Oceanography  grants  1829903  and  1829680  " Hydrothermal fungi in the Guaymas Basin Hydrocarbon Ecosystem " to  V. Edgcomb  and  A. Teske , respectively. Postdoc G. Ramirez  and Graduate student C. R. Chambers were supported by  NSF Molecular and Cellular Biology  grant  1817381  " Next Generation Physiology " and by  ARPA- E grant  " Mining the Deep Sea for Microbial Ethano-and Propanogenesis". Sampling in Guaymas Basin  was supported by collaborative  NSF Biological Oceanography  grants  1357238  and  1357360  " Collaborative Research: Microbial carbon cycling and its interaction with sulfur and nitrogen transformations in Guaymas Basin hydrothermal sediments " to  A.
Data logger deployments and data collection Fieldwork was conducted in Charleslaguna in St Jonsfjorden, Svalbard, Norway. This coastal lagoon has a 90 m wide opening and a surface area of about 1 km 2  (Haug & Myhre 2016). The lagoon was created by the retreat of a glacier, the front of which is no longer in contact with the water. The shoreline of the lagoon is relatively flat, with large tidal mudflats in the inner reaches of the lagoon; the lagoon is surrounded by adjacent mountains with river valleys running between them  (Haug & Myhre 2016). Several landward ponds are also present that drain into the lagoon  (Haug & Myhre 2016). A total of 20 ringed seals were captured in this lagoon and equipped with GPS CTD satellite-relay data loggers (GPS-CTD-SRDL hereafter) (Sea Mammal Research Unit).
Data processing All data processing and analyses were done using the R statistical framework (R v. 3. 6. 0; R Core Team 2019). Satellite-derived locations were first filtered to remove all repeated locations and then processed further using a speed filter to remove all unrealistic locations  (McConnell et al. 1992). This was done using the R package 'argosfilter' (version 0. 62; Freitas 2012); the swimming speed threshold was set at 2 m s -1  (Freitas et al. 2008). The presence of the seals inside the lagoon was investigated throughout the year in order to study potential seasonality of visits to the lagoon. Because location data were not reported frequently along the tracks (mean ± SE duration between consecutive locations = 2. 59 ± 0. 04 h with 96% of the locations occurring within 12 h of another point), filtered locations were interpolated, creating locations that were regularly spaced at 1 h intervals (corresponding to the median time interval between 2 locations) along all track-lines.
Analysis and modelling approach The seasonality of use of the lagoon, as well as the seasonality of activities inside and outside the lagoon, were investigated using generalized additive mixed models (GAMMs; 'gamm' function in the R package 'mgcv'; see  Wood 2017) , in which the linear predictor is given by a user-specified number of smooth functions for the covariates, plus a conventional parametric component. The proportions of 1 h interpolated locations identified as being inside the lagoon for each day (knot value, k, set at 5), as well as the percentage of time hauled out, at the surface and diving for each 6 h period (k set at 6), were examined in relation to the day of the year (the number of days since 1 July, i. e. the earliest tagging date irrespective of year), in separate models.
One of these 6 seals visited an other lagoon (Richardlaguna on Prins Karls Forlandet, see Fig. S2 ) twice, for short periods, but these periods were omitted from the analyses. After the 1 h interpolation process, a total of 88 345 locations were extracted, involving 7840 (9. 7%) interpolated locations within Charleslaguna and 80 505 (90. 3%) locations outside the lagoon. The total proportion of tracking time ringed seals spent in the lagoon was on average 8. 9 ± 0. 4% d -1 , but lagoon use varied markedly through the year (Fig. 2 , Table  2 ), with visitation to the lagoon occurring from the time of tagging in July through until February; the seals did not use the lagoon from March to May and no data were available for June because the seals moult the tags along with their hair toward the end of the breeding period (late May/early June; see Fig.
Acknowledgements. The authors thank Oddmund Isakksen, Charmain Hamilton and Karen Lone for help with the seal captures. The study was financed by the Norwegian Research Council (GLAERE project), VISTA, the Svalbard Environmental Fund and the Norwegian Polar Institute. LITERATURE CITED Bengtsson O, Lydersen C, Lindström U, Kovacs KM (2020) Ringed seal (Pusa hispida) diet on the west coast of Spitsbergen, Svalbard, Norway: during a time of ecosystem change. Polar Biol 43:773-788 Berge J, Heggland K, Lønne OJ, Cottier F and others (2015) First records of Atlantic mackerel (Scomber scombrus) from the Svalbard archipelago, Norway, with possible explanations for the extension of its distribution. Arctic 68:54-61 Carlens H, Lydersen C, Krafft BA, Kovacs KM (2006) Spring haul-out behavior of ringed seals (Pusa hispida) in Kongsfjorden, Svalbard.
Our approach is to construct a budget of PV in a 13 km 3 13 km 3 500 m upper-ocean volume using measurements from a mooring array deployed in the northeast Atlantic as part of the OSMOSIS (Ocean Surface Mixing, Ocean Submesoscale Interaction Study) experiment, complemented with glider observations and an atmospheric reanalysis. The mooring dataset is exceptional in that it samples a complete annual cycle of the mixed layer evolution concurrently to many of its controlling 3D processes (such as submesoscale frontal instabilities). This work represents the culmination of a long succession of studies of submesoscale turbulence based on the OSMOSIS mooring and glider observations, and integrates insights and diagnostics generated by those investigations. These include the work of  Buckingham et al.
(2016) , who demonstrated the seasonality of submesoscale motions, evidenced by the wintertime occurrence of positively skewed relative vorticity in mooring data, and that of  Thompson et al. (2016) , who characterized the PV conditions for submesoscale frontal instabilities, and the instabilities' impact on upper-ocean stratification, using the year-long glider measurements. Yu et al. (2019a)  followed by diagnosing the annual cycle of upper-ocean vertical motion and restratification associated with submesoscale processes from mooring observations, and showed that submesoscale restratification events are generally triggered by mesoscale frontogenesis. Evans et al. (2018)  and  Buckingham et al. (2019)  used the glider and mooring datasets to examine the contribution of submesoscale frontal instabilities to sustaining turbulent kinetic energy dissipation in the mixed layer, and concluded that such contribution is generally modest.
a. Mooring data The data analyzed in this study were primarily collected from a mooring array deployed at an approximate water depth of 4800 m over the Porcupine Abyssal Plain (PAP; 48. 638-48. 758N, 16. 098-16. 278W) in the northeast Atlantic (Fig. 1 ). The array's primary purpose was to measure the detailed evolution of the mixed layer, its controlling submesoscale processes and its mesoscale context, over a complete annual cycle. The mooring area was intentionally chosen to be representative of the midlatitude open ocean far away from western boundaries and complex topography, a regime that spans a substantial fraction of the global ocean (Fig. 1a ). Nine subsurface moorings were deployed for the period September 2012-September 2013, arranged in two concentric quadrilaterals with side lengths of ;13 km (outer cluster) and 1-2 km (inner cluster) around a centrally located single mooring (Fig.
The CTDs and current meters sampled at 5-and 10-min intervals, respectively. For each mooring, we linearly interpolate the data onto a uniform depth grid with 10-m spacing between depths of 50 and 520 m, and average onto hourly bins. Potential density (referenced to the ocean surface) and depth are calculated from interpolated temperature, salinity, and pressure using the Gibbs Seawater Oceanographic Toolbox  (McDougall and Barker 2011). Compressibility effects are considered to be negligible over the top 520 m  (Yu et al. 2019a). A fourth-order low-pass Butterworth filter with a cutoff of one inertial period (16 h) is applied to the hourly data to remove unbalanced motions, such as internal tides, near-inertial flows and other highfrequency motions. A quality control of the mooring data is carried out prior to analysis.
b. Additional datasets In addition to the mooring observations, the OSMOSIS domain was also continuously sampled by at least two (five in total) autonomous underwater gliders for the entire year  (Damerell et al. 2016; Thompson et al. 2016; Erickson and Thompson 2018; Evans et al. 2018 ). The gliders navigated in a bow-tie pattern across the mooring array, measuring temperature and salinity profiles within the top 1000 m of the ocean at approximately 1-m depth intervals. The mixed layer depth H is calculated from the glider data using a threshold value of potential density increase (Dr 5 0. 03 kg m 23 ) from a near-surface value at 10 m  (Damerell et al. 2016). Air-sea heat and freshwater fluxes and wind stress data are taken from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim reanalysis product, with a time interval of 3 h  (Dee et al.
Acknowledgments. The OSMOSIS experiment was funded by the  U. K. Natural Environment Research Council (NERC)  through Grants  NE/I019999/1 ,  NE/I019905/1 ,  NE/I019921/1 , and  NE/I01993X/1. We are grateful to the engineers, scientists, captain, and crew of the  RRS Discovery, RRS James Cook , and  R/V Celtic Explorer , who participated in the deployment and recovery of the moorings and gliders. All data are archived at the  British Oceanographic Data Centre  (glider data are accessible at https://doi. org/10/cqc6). We thank two anonymous reviewers for their constructive comments on this manuscript. We acknowledge helpful discussions with  Christian E. Buckingham ,  Liam Brannigan , and  Florian Sévellec. XY is supported by the  French National Agency for Research  under Grant  17-CE01-0006-01.
Fish were introduced to respirometry chambers a few minutes before the first automated oxygen uptake rate recordings were started in the afternoon and remained there for ~17 hr until the following morning. The fish were then removed from the respirometry chambers and their body mass recorded. Fish oxygen uptake rates were calculated by multiplying the slopes (over 3-4 min) for the decline in oxygen inside the respirometry chambers during the closed phases of the respirometry cycles by the volume of the respirometry chamber after subtracting fish volume and background bacterial respiration (calculated from a respirometry chamber without fish). The SMR of each individual fish was then estimated by first calculating the mean of the lowest 10% of its oxygen uptake rate measurements from the ~17 hr respirometry trial, then excluding any outliers (data points outside the mean ± 2 SDs, or data points with r 2 values for the linear regressions of the decline in oxygen over time lower than the mean r 2 -2 SDs), and finally re-calculating the mean of the remaining data points.
| Statistical analysis Statistical analyses were performed in r version 3. 6. 1 (R Core Team, 2019). Linear mixed-effects models (LMEs) were used to explore the effect of the explanatory variables -anemone treatment (categorical), exposure time (continuous), fish body mass (continuous) and site (categorical) -on fish SMR, growth rate and behaviour. Fish ID (categorical) was included as a random effect to account for nonindependence of data (i. e. when fish were measured twice over time). Time was included as a continuous variable since not all measurements were taken at exactly four or eight weeks but spread around these target time points for logistical reasons. All LMEs were fitted using the lmerTesT package  (Kuznetsova et al. , 2017) , while marginal (mR 2 ) and conditional (cR 2 ) R 2 were obtained with the package piecewisesem  (Lefcheck, 2016).
The selection of the best-fit model was determined using likelihood ratio tests (LRTs), starting from the most complex model and subsequently removing non-significant interactions and explanatory variables (Tables  S6A, S7A , S8A, S9A and S10A) via the lmTesT package  (Zeileis & Hothorn, 2002). In addition, SMR and fish body mass were log 10 -transformed to account for their nonlinear (power) relationship. The 'ns' function in the splines package was used in the growth rate model to account for nonlinear body mass effects on fish growth. Space use was log 10 -transformed to alleviate non-normality. In each model, residuals were visually inspected to ensure that all assumptions were met. For graphical representation, regressions were fitted on predicted values obtained using the 'ggemmens' function in the ggeffecT package  (Lüdecke, 2018).
The consistency of each individual fish's behaviour across the three haphazardly chosen sections of 30 s video was evaluated by calculating the adjusted repeatability (R adj ) of activity and space use using the rpTr package  (Stoffel et al. , 2017). We calculated overall R adj using the same model structure as for the LMEs (Table  S5A ), with chronological order of the three 30 s sections added as an additional explanatory variable. We also calculated R adj for each anemone treatment separately by sub-setting the data for each treatment and removing treatment as a fixed effect in these models. Behaviour was significantly repeatable in all cases (R adj = 0. 318 to 0. 464, p ≤ 0. 0072; Table  S5B ). We tested for differences in survival between treatments using a Cox proportional hazard model via the 'coxph' function in the survival package  (Therneau, 2020; Therneau & Grambsch, 2000).
Flux calibration was performed using an appropriately placed bright infrared flux standard star using a wide (4 0) slit. We employed data reduction procedures that have been rigorously tested and are described extensively in the refereed literature  (Bonev 2005; DiSanti et al. 2006 DiSanti et al. , 2014;; Villanueva et al. 2009; Radeva et al. 2010) , including their application to unique aspects of iSHELL spectra  (DiSanti et al. 2017; Roth et al. 2020). Each echelle order within an iSHELL setting was processed individually as previously described, such that each row corresponded to a unique position along the slit, and each column to a unique wavelength. Spectra were extracted from the processed frames by summing the signal over 15 rows (approximately 2 5), seven rows to each side of the nucleus, defined as the peak of dust emission in a given spectral order.
Acknowledgements. J. M. N. T. G. is a  Royal Society Wolfson Research Merit Award holder  ( WM150058 ) and an  EPSRC   Established Career Fellow  ( EP/M022447/1 ). All research data supporting this publication are directly available within this publication. Funding. This research was supported by  NERC  grants  NE/E003206/1  and  NE/K003011/1  as well as  EPSRC  grants  EP/I019189/1 ,  EP/K00428X/1  and  EP/M022447/1. Declaration of interests. The authors report no conflict of interest. Author ORCIDs. A. N. Edwards https://orcid. org/ 0000-0002-2374-0162 ; S. Viroulet https://orcid. org/ 0000-0002-8204-1425 ;C. G. Johnson https://orcid. org/ 0000-0003-2192-3616 ;J. M. N. T. Gray https://orcid. org/ 0000-0003-3554-0499.
Simulated dataset and processing The present investigation employs the same simulated dataset presented in  (Menna et al. , 2020). The choice of testing our methodology on simulated rather than real-case scenarios is driven by a double necessity: we only intend to address the geometric component of the residuals introduced by refraction, avoiding other sources of uncertainty that are not easily discernible. In addition, in simulations reference data is available for accuracy checking of the proposed method. The employed simulations are derived from a real case dataset (Table  1 ), collected on a coral reef within the Moorea Island Digital Ecosystem Avatar (IDEA) project (https://mooreaidea. ethz. ch/). The images were acquired by SCUBA divers with a Nikon D750 24Mpx full frame DSLR camera (pixel size ≈ 6 µm) mounting a 24 mm prime lens in a NiMAR pressure housing.
The surveyed reef plot measures about 20x10m 2 , featuring a rough and hilly surface, at an average depth of about 12 m and with a depth variation of about 1. 6 m. Results of the photogrammetric processing aiming at monitoring across time the reef evolution are discussed in  Nocerino et al. (2019 Nocerino et al. ( , 2020)). Relative camera to seabed distance  ≈ 1. 5 m average GSD 0. 4 mm Number of images 600 (495 nadir) Number of strips 8 nadir + 1 oblique Table  1. Summary of the real case dataset used for generating the simulated data. From the full camera network, we extract a subset comprising a single round-trip nadir strip (130 images). This weak camera geometry allows to better emphasize the residual errors which we aim to compensate with the proposed method. From the photogrammetric processed data, a subsampled 3D point cloud (1point/10cm) is extracted and used as input to run the simulations.
Image observations are generated using a refraction index of 1. 34 and neglecting the water/air interface (port) according to the formula derived in  Menna et al. (2020). Lens distortions are fixed to zero and white noise is not added for the purpose of analyzing the sole effect of the refraction induced systematic errors. Four simulated datasets are created: A. FP0, entrance pupil on the planar water/air interface (flat port); B. FP30, entrance pupil at 30 mm from the flat port; C. FP100, entrance pupil at 100 mm from the flat port; D. DP+30, misaligned dome port with dome center  30 mm ahead the entrance pupil. The chosen offset values of 30 mm and 100 mm both for flat and dome ports are quite large. But, although still possible, for example in the case of using wrong extension tubes, they are here used to stress the related systematic errors and proposed corrections.
Minimal constraints bundle adjustments with self-calibration are performed in DBAT on the simulated image observations. As the systematic errors on the simulated dataset are expected to have only a radial component in image space, only radial distortion additional parameters are solved within the self-calibration solutions. Accuracy assessment is performed computing, through a similarity transformation, the difference (error) between the coordinates of triangulated image observations (sparse point cloud of tie points) and the original 3D point cloud used to generate the simulated image observations.
RESULTS AND DISCUSSION Table  2  reports the results of the iterative processing for the simulated datasets before and after the distance-dependent correction. It is worth remembering that the image residuals are only due to the incomplete functional model, unable to properly take into full account the refraction effects of water. After correction, in all cases there is an improvement in the bundle adjustment results, both in terms of parameters standard deviations and root mean square error (RMSE) of image residuals. This result indicates that the proposed correction method is able to correct the systematic residuals not compensated by the classical Brown/Beyer model. Although in both cases the starting residuals are rather small, for both FP0 and DP+30 the final values after correction are improved by an order of magnitude.