dataesr/scientific-paragraphs-categorization huggingface dataset:
Such an evaluation is missing from the present paper; it will be part of a subsequent paper in future work. Another limitation stems from the lack of an automated identification of those clusters that are associated with the tumor region. Right now, we choose those clusters that maximize overlap with the manually segmented tumor region. Ideally, however, the abnormal tumor clusters should be identified automatically, by selecting those clusters that have the highest association with clinical outcomes. In this manner, the automated cluster identification can be naturally made part of a single machine learning pipeline for predicting clinical outcomes. Yet another limitation comes from the very small size of the subset of tumors (n = 10) over which we estimated the algorithm parameters (λ and number of clusters), before applying the algorithm to the remaining 81 tumors in our dataset.
A larger dataset, together with additional patient-specific tuning will help tune the algorithm's performance. The need for the improvements described above is clear, and they will be made part of a subsequent publication. In the present article, we chose to focus on the theoretical and algorithmic developments. As mentioned in the Introduction, this is the first time to our knowledge that statistical tools from the functional data analysis field are put into practice with DECT data. As such, the present paper remains an inherently exploratory one in its experimental framework. Nevertheless, we believe that we provide several important technical and methodological contributions. We constructed a functional regression mixture model that integrates spatial content into the mixture weights, and we developed a dedicated EM algorithm to estimate the optimal model parameters.
Thirdly, advanced tools were not available, and important background information did not exist in the past. Fourthly, some modern research strategies and their implementation that address the following gaps of knowledge and knowledge transfer have not yet gained sufficient acceptance. 1. Synoptic surveys generating long-term and year-round data series and allowing an assessment of complex climate-induced changes (vs. natural variability) are lacking  (IPCC, 2022). 2. Although concepts for standardized protocols, operating procedures and data integration do exist (see e. g. Miller et al. , 2015; Piazza et al. , 2019; Van de Putte et al. , 2021) , they have not been frequently and consequently implemented. They have to be urgently ap-plied to acquire large-scale and long-term comparable biogeographic data.
Such surveys are the core of the long-term observatory concept, integrating data from various complementary approaches and sources (autonomous long-term in situ monitoring, regular ship-based sampling, satellite-based remote sensing). Shipboard and autonomous data collections are suggested to take place at water depths ranging from the coastal shelf, including unusually shallow sites like the Norsel Bank off Kapp Norvegia (approx. 60 m), the slope between approx. 450 and 3000 m depth, influenced by the ACoC, to the deep sea, influenced by the Weddell Gyre. The concept envisages up to three sub-areas, since these should cover the different physical-chemical prerequisites and the biological heterogeneity within the wider study area, being partly representative for the East Antarctic SO (for an overview see Sect.
Methods Information on key taxa shall be collected across various spatial scales at the IEAMaR observatory and adjacent pelagic transects (Fig. 1 ) by means of various methodological approaches and in parallel to the flux studies (see Sect. 3. 1). Phytoplankton and pelagic primary consumers, such as krill, copepods, and young fish larvae, as well as secondary consumers, such as Antarctic silverfish Pleuragramma antarctica, shall be studied by CTD and rosette casts and pelagic net catches to assess species composition, abundance, population parameters, and feeding condition, and they shall be compared with data provided by acoustic systems. Benthic surveys shall primarily be conducted by means of minimally invasive methods (e. g. traps, corers, autonomous seabed and under-shelf ice sampling and acoustic as well as optical imaging, and scientific longline fishery) to minimize the anthropogenic impact of invasive sampling methods (e.
g. bottom trawls). Higher-order predator studies shall be continued by the instrumentation of animals with CTD-satellite trackers and biologging devices (e. g. Nachtsheim et al. , 2019; Houstin et al. , 2022) , as well as physiological and nutritional studies. Population estimates and habitat distribution (e. g. LaRue et al. , 2021; Wege et al. , 2021b)  of seal and penguin populations shall be monitored using a combination of air-borne and Very High Resolution (VHR) satellite imaging, including autonomous year-round observations  (Richter et al. , 2018; Fretwell and Trathan, 2020). Images will focus on locations representative for the core stations established at regular intervals. Both biologging data and VHR imagery data shall be used to determine critical habitats. As more data become available, we can project distribution changes of these core habitats into the future using climate modelling (e.
In combination with onsite laboratory experiments (e. g. at Neumayer Station III), they will help to unravel life-history strategies, life-stagespecific spatial and temporal distributions, and their adaptive scope over generations. Furthermore, internal and external data loggers suitable for smaller marine organisms, such as fish and invertebrates, audio-visual loggers to study foraging behaviour of predators, and long-term tracking of their preferred water temperature and depth, but also of physiological parameters, such as heart rate, blood flow and tissue oxygenation, have come into reach. Semi-permanent moorings and lander systems (see Sect. 3. 1) shall serve as a (power) base for those applications and allow for controlled deployment of traps and other gear. Otoliths of fishes shall be used as an archive of temperature preference and utilized resources in fishes.
Autonomous bio-environmental observatories shall constitute an important pillar of the long-term IEAMaR observatory. By combining multiple sensors on bottom-moored, seaice-moored and free-drifting platforms, the spatio-temporal gaps between field campaigns will be closed with highresolution data. These systems should merge existing stateof-the-art environmental sensors, such as CTDs, nutrient sensors, fluorometers, spectral radiometers and optical sediment traps, with the newest technology to monitor organisms beyond microbes. Examples for such sensors are camera systems; autonomous multi-frequency echosounders (e. g. acoustic zooplankton and fish profilers, wideband autonomous transceiver), which are able to record and transmit data and to receive real-time manipulation of the sampling programme; and automatic eDNA samplers and imaging profilers (e.
g. underwater vision profiler). The pelagic community will be sampled with an ultraclean CTD and under-way filtration systems for phytoplankton, ship-mounted echosounders, rectangular midwater trawls for plankton and nekton, and multinets and imaging zooplankton profilers (e. g. Lightframe On-sight Key species Investigations, LOKI) for the mesofauna (e. g. Schnack-Schiel et al. , 2008; Flores et al. , 2014). Net catches will be used to ground-truth biomass and community data derived from continuous sampling with multi-frequency broadband echosounders as well as from autonomous observatories equipped with echosounders. The community under the sea ice will be sampled using under-ice trawls, alongside with physical parameters of the sea ice and underlying water  (Castellani et al. , 2022) , and the sympagic in-ice community composition will be investigated on ice stations by ice core sampling (for sampling strategy see Sect.
3. 1). With reference to the proposed WSMPA, the IEAMaR observatory shall provide the focal point for the research and monitoring activities required in a WSMPA management plan and is important for the regular review of the effectiveness of the WSMPA. To ensure synergistic effects for science and marine conservation policy, the location of the observatory is partly congruent with the proposed WSMPA (Fig. 5 ). After the adoption of the WSMPA by CCAMLR, the longterm IEAMaR work shall provide research-based long-term data on the natural development of the protected environments and biotas. The sympagic, pelagic and benthic habitats to be monitored because they are at least partly repre-sentative for the East Antarctic SO rare and especially vulnerable habitats include the shelf-ice associated cryo-benthos  (Watanabe et al.
, 2006; Gutt and Dieckmann, 2021)  and benthic communities at unusually shallow sites that are thus exposed to unusual environmental conditions and disturbances  (Raguá-Gil et al. , 2004). LTER work at the IEAMaR observatory shall also provide insight into the biology, life cycle and trophic role of Antarctic toothfish. Since 2014, every year around 200 t of Antarctic toothfish is caught by fishing vessels operating in the CCAMLR research block 48. 6_5 in front of the DML coast (Fig. 5 ). Combining the information obtained by these longline operations with data obtained by the IEAMaR will allow comparisons of the benthic habitats in this research block with similar habitats in unfished areas to study the physical disturbance and effects of longline fishing. The close proximity and partial overlap of the IEA-MaR region with the CCAMLR research block also provide the opportunity to carry out further studies of local and regional food web and ecosystem effects caused by the annual removal of large quantities of D.
International integration The ecological complexity to be tackled by the IEAMaR concept both demands and provides the potential for extensive international collaboration. The planned IEAMaR long-term observatory will also complement the work of similar observatories in the maritime Antarctic (e. g. Palmer LTER off the western Antarctic Peninsula) and in other regions in the high-latitude SO. Inter-comparability of methods and data among various LTER efforts shall be a priority in the implementation of the planned observation and activities in the IEAMaR region. In addition, this observatory can serve as a showcase project within the Southern Ocean Observing System (SOOS;  Rintoul et al. , 2012; Newman et al. , 2019). In the first Antarctic and Southern Ocean Horizon Scan of SCAR  (Kennicutt et al.
All data acquired during the IEAMaR work shall be stored in international data repositories with general scope or maintaining specific information for standard analyses. For example, surface pCO 2 data shall be submitted to the annual updates of the Surface Ocean CO 2 Atlas (SOCAT)  (Bakker et al. , 2016) , which is widely used for studies on a regional and global scale. SOCAT, in turn, informs the Global Carbon Project for its annual update of the Global Carbon Budget. Biogeographic and biological trait data shall be made available through the SCAR Antarctic Biodiversity Portal (https://www. biodiversity. aq,lastaccess: 23August2022), whilst a broad variety of other ecological data shall be published in the PANGAEA Data Publisher for Earth and Environmental Science (https://www. pangaea. de, last access: 23 August 2022).
Genetic and biodiversity data can further be stored in the Barcode of Life Data System (https://www. boldsystems. org, last access: 23 August 2022). The Southern Ocean and Antarctic continent are managed within the framework of the Antarctic Treaty System, which is based upon scientific understanding and environmental protection. Some of the societal needs and challenges may overlap with a global context, while others are and will remain unique (Van de  Putte et al. , 2021). In general, all information, including data and their interpretation shall contribute to international scientific assessment programmes, such as IPCC and IPBES, and other advisory bodies (Fig. 3 ).
Such studies can also provide valuable information on the effectiveness of the proposed WSMPA. The pressure from stakeholders to address such unanswered applied ecological questions should foster coordinated cross-disciplinary and international research, in which major advances are to be expected in more than single disciplines. The envisaged frame-work for long-term studies in the IEAMaR region will also increase our knowledge about first-principle issues, e. g. on energy flow in food webs and on biodiversity patterns including their dynamics. The collected data are to be made publicly available for policy makers to facilitate appropriate actions and recommendations. Furthermore, the expected findings of IEAMaR studies shall be suitable for publication in textbooks and in public media. The stand-alone feature of the IEAMaR concept lies in the particularly extensive integration of long-term physical, geochemical and biological research, which allows for gaining unique ecological insights.
Data availability. The data (sets) used for the illustrations in some figures are referenced in the figure captions: bathymetry (water depth) in Fig. 1   (Dorschel et al. , 2022) ; bathymetry in Figs. 2a and 3  (Arndt et al. , 2013) ; and sea ice, chlorophyll, and temperature in Fig. 4   (Lavergne et al. , 2019, and http://oceandata. sci. gsfc. nasa. gov/, last access: 23 August 2022). No addition data sets were used in this publication. Author contributions. JG, DP and FCM contributed the most the concept development, wrote the main part of the paper including conclusions, and contributed to Sects. 1-3. HG and AVdP contributed to the general concept and text; HOP to the international implementation; SA, OE, CHa, MH, TH, EI, MJ, SM, and STr mainly to Sect. 3. 1; DKAB, CHe, HB, HF, CLB, HL, FM, FS, IvO, MW, and DZ to Sect.
Acknowledgements. Thanks are due to Rebecca Konijnenberg, Hendrik Pehkle and Yves Nowak (all AWI) for the design of Figs. 2 and 4. Financial support. Stefanie Arndt, Heike Link and Christoph Held have received financial support from the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority programme 1158 "Antarctic Research with Comparative Investigations in Arctic Sea Ice Areas". The article processing charges for this open-access publication were covered by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI).
Fault network To construct the geometry of the fault system, we use the Basel seismic database provided by Deichmann et al. 37  , in which different seismic events are grouped on the basis of their focal mechanism, location and timing of occurrence. We assume that each cluster corresponds to a fault plane obtaining a network of 11 faults with different orientations and locations. To simplify the geometry, we combine a few clusters on the basis of similarities of focal mechanism, location and timing. This operation is applied to two sets of three clusters each, which allows us to reduce the original network of 11 faults to a simpli ed network of 7 faults. To the representativeness of this simpli cation, we compare the failureinduced static stress redistribution in the simpli ed fault system and in the original fault system, i.
Acknowledgements A. B. and V. V. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Program  through the  Starting Grant GEoREST  (www. georest. eu)underGrantagreementNo. 801809. IDAEA-CSIC is a Centre of  Excellence Severo Ochoa (Spanish MinistryofScienceandInnovation , GrantCEX2018-000794-S funded by  MCIN/AEI/ 10. 13039/501100011033 ).
(2021a Rivilla et al. ( , 2021b)). We have performed a new deeper spectral survey with the Yebes 40 m telescope as part of the 21A014 project (PI: Rivilla). We observed during 29 different sessions between 2021 March and 2021 June, with a total observing time of ∼125 hr. The Nanocosmos Q-band (7 mm) HEMT receiver was used, which provides ultra-broadband observations (18. 5 GHz) in two linear polarizations  (Tercero et al. 2021). The receiver was connected to 16 FFTS providing a channel width of 38 kHz. We observed two different spectral setups centered at 41. 40 and 42. 30 GHz, with a total frequency range of 31. 07-50. 42 GHz. We have performed the initial data inspection and reduction using a Python-based 14  script that uses the CLASS module of the GILDAS package. 15 or each observing day, spectra averaging was performed, and we fitted baselines using an iterative method that first masks the more visible lines using sigma clips and then applies rolling medians and rolling averages, interpolating the masked regions with splines.
A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0. 1°x 0. 1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP1-2. 6) and one without mitigation (SSP5-8. 5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.
• The dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change service, a data product extensively used around the world for historical climate analysis. A great advantage of this dataset is thus to provide an extension of the ERA5-Land reanalysis into the future. • The dataset is global, has a spatial resolution of 0. 1°x 0. 1°, comprises 5 climate models allowing to address model uncertainty and includes 2 surface daily variables at monthly resolution: mean air temperature and precipitation. • To sample future climate uncertainty from anthropogenic forcing, two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP1-2. 6) and one without mitigation (SSP5-8. 5).
Data Description The high-resolution climate projections dataset covers the globe at a 0. 1°x0. 1° spatial resolution and at monthly temporal resolution for five surface variables. It comprises 5 models (see table  1 ) from the CMIP6 experiment  [1]  with simulations for the historical period  and the 21st century (2015 to 2100) under two emissions scenarios: one with mitigation policy (Shared Socioeconomic Pathway 1-2. 6 or SSP1-2. 6 ) and one with no mitigation (Shared Socioeconomic Pathway 5-8. 5 or SSP5-8. 5). The following two land surface are downscaled: mean daily temperature and total precipitation. The combination of models and scenarios represents 10 climate projections including both variables (see table  1 ). Other variables, models and emissions scenarios could be added in the near future.
The data was produced with a statistical downscaling method using the ERA5-Land reanalysis  [2]  for calibration and training (see next section for details). The interest of the downscaled data is the removal of model biases at a resolution more compatible with the requirements of assessments and further modelling of the impacts of climate change. In other terms it corrects the climatology (distribution) of model values to make them comparable with a reference observational dataset  [3] , which in this case is the ERA5-Land reanalysis. In the following subsections, we present the file naming conventions then proceed with an illustration of the bias removal over the historical period and the climate change signal differences at the end of century.
File name conventions There is no official Data Reference Syntax (DRS) defined by the climate modeling community for statistically bias-adjusted or downscaled CMIP projections as there is for CMIP6 and CMIP5 projections. However, there is a DRS for CORDEX bias adjusted simulations defined in the context of the EURO-CORDEX experiment. We adapted the EURO-CORDEX DRS for adjusted projections to CMIP6/CMIP5 in order to produce a DRS for CMIP adjusted projections  [4]. We kept the term "bias-adjustment" and "adjustment" even if strictly speaking we are producing downscaled projections. The terms "bias-adjusted" and "downscaled" are used interchangeably in the scientific literature because they often involve the same statistical techniques (even though, more recently, bias-adjustment is reserved for cases where the original model resolution is unchanged).
We also wanted to use only what was defined by the climate modeling community. On top of this, it appears to us the DRS is sufficient to include all the information needed in the file naming. The DRS we came up for CMIP6 adjusted projections is presented below through a short illustrative example of file naming: -data file containing original (uncorrected) model results: tas_day_IPSL-CM6A-LR_ssp126_r1i1p1f1_gr_20160101-20251231. nc where: -"tas" is the conventional name for surface temperature, -"day" is the label for frequency (here daily) -"IPSL-CM6A-LR" is the official name of the climate model, -"ssp126" is the short name of the used Shared Socioeconomic Pathway, -"r1lip1f1" is the member number of the simulation, -''gr'' is the grid label of the original data, -"20160101-20251231" are the start and end date of the simulation.
-bias-adjusted data file (new/modified information in bold) tasAdjust_Amon_IPSL-CM6A-LR_ssp126_r1i1p1f1_gr010_TCDF-CDFT23-ERA5Land-1981-2010_20160101-202512. nc where: -"Adjust" is added to the variable conventional name, -"mon" is the temporal resolution of the data (here monthly), -"gr010" is the grid label of the downscaled data, -"TCDF" is the short name of the organization that performed the post-processing, -"CDFT23" is the label referencing the method, -"ERA5Land" the short name of the data set used as observations, -"1981-2010" is the period used for statistical calibrating the method. The complete list of the data files is given in Annex 1.
In Figure  2a , the cumulative distributions functions (CDFs) are empirically estimated from monthly values averaged on the globe for the interpolated (left) and for downscaled data (right) over the calibration period. There is a spread between the ERA5-Land reanalysis and the interpolated data with overestimations and underestimations. This spread is more important for precipitation than for the temperature. For downscaled data, the difference between the simulations CDFs and observation CDFs is very diminished. However, for precipitation, we observe small differences for low monthly precipitation amounts. This is due to the fact that downscaling is performed at a daily scale and grid point by grid point, while CDFs are estimated on monthly and spatially-averaged data. The day-to-day (temporal) and spatial variability of the model data are preserved by the downscaling method, however residual biases can appear on monthly and spatial averages.
Experimental Design, Materials and Methods Four datasets are used in this work: • The reanalysis data that is used as reference for calibrating the statistical algorithm over a training period. The reanalysis grid sets the final resolution of the downscaled projections. • The original model climate projections come in a variety of spatial resolutions (typically between 2. 5°x2. 5° and 0. 9°x0. 9°) and are referred to as "raw". • The raw data interpolated on the reanalysis grid and referred to as "interpolated". • The downscaled data obtained from the interpolated data and the reanalysis data used for statistical calibration (both on the same grid) and referred as "downscaled". The raw and reanalysis data are input data that need to be sourced. The interpolated data is just an intermediary dataset needed by the methodology while the downscaled data is the final dataset.