dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical simulations with the WRF model We also used numerical simulations carried out with version 4. 1. 1 of the WRF model. The simulation configuration has been developed for precipitation studies in Antarctica  (Vignon et al. , 2019a)  and can be summarized as follows. WRF is run with a downscaling approach with a parent domain of 27 km resolution containing three (one-way) nested domains with 9, 3, and 1 km resolution centered over PEA (see Figure  2 ). The topography is from the Bedmap2 1-km resolution dataset from  Fretwell et al. (2013). The lateral forcings, the sea ice cover, the sea surface temperature, and the initial conditions are from the ERA5 reanalysis  (Hersbach et al. , 2020). To ensure realistic large-scale dynamics throughout the simulation period, the 27-km resolution domain has been nudged above the boundary layer towards ERA5 reanalysis for zonal and meridional wind with a relaxation time scale of 6 h.
Otherwise mentioned, the model variables shown in this paper are from the 1-km resolution domain. The first of the variables taken into consideration is the horizontal wind speed. Three sets of profiles have been extracted from the model grid points closest to the three MRR-PRO locations in the transect. The median and interquartile range (IQR) of the variable have been computed from the time series of profiles, covering the same temporal extent as the three MRR-PRO datasets. The second variable extracted from the WRF simulations is the relative humidity with respect to ice (RH i ), computed in post-processing from the specific water vapor content and the temperature of the model using the formula for the saturation vapor pressure with respect to ice from  Huang (2018). This variable allows us to link eventual layers of relatively dry air to the virga observed in the radar measurements.
As for the wind speed, profiles have been extracted above the three transect sites and the median and IQR have been computed for the period in which the MRR-PRO data are available. In addition to these profiles, the total accumulation of solid precipitation at the ground has been used in the analysis. The mixing ratio of graupel and snow, together with the vertical wind speed simulated by the model, has also been used to estimate the precipitation flux at a fixed altitude.
Occurrence of virga and surface precipitation The labeling of virga and surface precipitation time steps presented in section 2. 1. 5 permits the computation of simple statistics related to the occurrence of the two weather states above the three instrumented sites along the transect. Figure  3  shows the results for all four temporal windows used for computation, denoted by vertical bars of different colors. Panel 3. a shows the ratio between virga and surface precipitation at the three locations in the dataset. MRR-PRO 23 stands out for the relatively high ratio of virgae to surface precipitation (from 65 to 85%, compared to typically 40 and 25% for the MRR-PRO 06 and MRR-PRO 22), with virgae constituting between 40% and 50% of all the valid precipitation signals, depending on the window size, as illustrated by panel 3.
We suspect that turbulence may play a more significant role in this lower section of the profiles. A possible explanation for this behavior could be the presence of a larger speed and directional wind shear during virga cases, even though we could not verify this interpretation through measurements or numerical simulations. Note that for both Z ea and SW profiles in the MRR-PRO 23 dataset, two sudden increases can be observed between 1. 0 km and 1. 5 km. Two interference lines were present in the radar measurements at those heights before the post-processing. These increases are likely the leftover effects of those lines, caused by an imperfect reconstruction of the spectra by ERUO, resulting in a slight overestimation of both variables.
In crystalline bedrock, the open fraction of the fracture network constitutes the main pathways for fluids. Many observations point out that the state of stress influences the open fraction, likely indicating recent reactivation. But how this occurs is still unresolved. We analyse the conditions for fracture reactivation from fracture data collected in the uppermost 1 km of bedrock in Forsmark, Sweden. The open fraction is mainly correlated to the stress acting normally on the fracture but even away from critical failure, leading us to analyse the potential fluid pressure required for reactivation, P c. We observe that 100% of the fractures are open when P c is hydrostatic, and the ratio decreases exponentially to a plateau of ~ 17% when P c is lithostatic and above. Exceptions are the oldest fractures, having a low open fraction independent of P c.
The state of fracture networks in crystalline bedrock generally results from a long deformation history, where the rock permeability and mechanical properties evolved due to successions of fracture failure and sealing processes  [1] [2] [3] [4]. Because fractures are the main conduits for flow and transport, understanding how these past events have shaped the current open fraction of the fracture network (called "openness" hereafter) is key to understanding geological reservoirs in the context of industrial applications, such as geothermal energy, carbon sequestration, or energy and waste storage projects. The present study focuses on the Forsmark site in Sweden. Forsmark was chosen to host a future deep repository for nuclear waste disposal and has a data-rich core-log database on fracture properties that is made available by SKB, the Swedish Nuclear Fuel and Waste Management company.
The permeability of fractured rocks has often been related to the presence of critically stressed fractures  15, 16. In Forsmark, the data show that very few fractures are currently in a critical state and that many open fractures are far from failure conditions. Tensile failure is expected close to the surface due to an increase of the ratio between horizontal and vertical stresses in the first few hundred meters (the 'Brown-Hoek' effect)  17, 18  , but it does not address the problem of deep fractures for which the confining stress is high. If it is known how normal stress affects fracture aperture and transmissivity  19, 20  , it is less clear how this mechanism causes the failure of sealed fractures. Our goal is to characterize which mechanism has reactivated the fractures and led to the present fracture openness.
The sampled fracture set Fractures observed in cores are any type of brittle discontinuity in rock, regardless of the failure mechanism, including also fractures that can have been sealed afterward by mineral precipitation. Ductile discontinuities such as dikes, sills, or pegmatite veins, are not considered. In Forsmark, fractures form a complex multiscale network of joints and faults that can be observed on surface outcrops (Supplementary materials: Fig. 13 ). The fraction of fractures recorded in the database depends on the recording criteria in the cores (only traces covering the centerline of the borehole are retained), the fracture size distribution (fsd), and the probability to intersect the boreholes, which is proportional to the square of the fracture size  28. The fsd has been inferred from outcrop mapping  29, 30  ; it varies from one fracture domain to another, as the fracture density.
Using fsd models constrained by outcrop trace distribution and enriched by fracture growth models  29  , we estimate that the fractures in the core database are mostly joints ranging in size from 10 cm to 1 km, with an average size between 1. 5 and 5 m depending on the fracture density. They are thus likely to be similar to the fractures mapped on outcrops, which present a high percentage of T-intersections (one fracture abutting another)  31. Most of the fractures are either vertical or horizontal. If the density of vertical fractures is almost independent of depth, the density of horizontal fractures increases significantly in the first 100 m (see Darcel et al. 30  and Supplementary materials: Figs. 7a, 8a ), certainly in response to topographic, exhumation-related or thermal stresses  18, 32.
Results Dependency of the openness on fracture stress at Forsmark. The data in the database containing the fracture characteristics was collected by SKB at Forsmark. It contains nearly 90,604 fractures collected along 40 boreholes with their characteristics measured in the wells and on the cores: depth, mineral fillings  10, 37  , orientation, structural domain (either fracture domain or deformation zone defined primarily by the intensity of fracturing) and openness state (open, sealed and partly-open) (see "Dataset description"). We added stress characteristics by calculating the normal and shear projections ( σ n and τ ) of the stress tensor model established in Forsmark  38  (see "Normal stress σ n and shear stress τ in the fracture" and Supplementary Materials: Fig. 3 ). The failure envelope of existing fractures has been measured from tilt and shear tests on rock samples  39.
13 (vertical error bars; Fig. 5e ). The correlation coefficient r w presents less correlation as for σ n ( r w = -0. 53 ) when considering the entire dataset. However, a similar correlation coefficient is measured when considering the data between P * c = 0 and P * c = 1 ( r w = -0. 62 ; Supplementary Materials: Table  1 ). For the fractures with P * c > 1 , there is no correlation with the openness ( r w ∼ 0).
To summarise, the interpretation of the fracture openness data presented above could be: 1. Fractures had an initial openness inherited from the old mineralisation stages (blue dashed line and red solid lines). 2. The initial openness can be measured on the current network under conditions where reactivation is impossible ( P * c > 1). 3. The mechanical reactivation under the current stress leads to an increase in openness with an exponentially decreasing efficiency as the overpressure required for failure increases or if the initial openness is small (i. e. , smaller than ~ 0. 15).
Methods Dataset description. The cored boreholes consist mainly of a series of shallow and deep boreholes (KFM01-KFM12) drilled between 2002 and 2006 and distributed across the area of the future repository site (Supplementary Materials). We also include two series of shallow boreholes, drilled in 2005 (KFM90A-F) and between 2011 and 2016 (KFM13-KFM23). The latter series are in the north-west of the area, near the nuclear power station. Along with core mapping, borehole imaging using BIPS (Borehole Image Processing System) was also performed and interpreted in these boreholes. The results of the interpretations are available in the SKB database SICADA. Our dataset is composed of the following fracture information: fracture position, orientation, open/partly-open/sealed interpretation, and mineral and wall alteration types.
Methods An ad-hoc prepared database protocol was distributed to 9 Pituitary Centers chosen by the project scienti c committee and comprising Centers of worldwide repute, which agreed to provide activity information derived from registries related to the years 2018-2020 and completing the database within 60 days. The database, composed of Excel® spreadsheets with requested speci c information on leading and supporting teams provided by each Center, was reviewed by two blinded referees and all 9 of 9 candidate centers satis ed the overall PTCOE de nition, according to referees' evaluations. To obtain objective numerical criteria, median values for each activity/parameter were considered as the ideal PTCOE de nition target, whereas the low limit of the range was selected as the acceptable target for each respective parameter.
They proposed candidate members of the SEB to the Board of the Pituitary Society based on previous recommendations  [9]  to re ect both leading PTCOE professionals (endocrinologists and neurosurgeons) and to obtain geographical (and Health System organizations) and gender balance. Twelve experts were selected, and they all agreed to participate in the project. The subsequent step was to nalize a database template to be distributed to the candidate Centers. The database design was driven by the criteria for PTCOE de nition, as established by The Pituitary Society Expert Group and published in late 2017  (9) , and was also consistent with the proposed PTCOE mission that aims at: 1. providing the best standard of care to patients with pituitary diseases; 2. being organized as a multidisciplinary clinical team, led by the liaison of both neurosurgeons and endocrinologist in close collaboration with supporting professionals; 3.
advancing science and knowledge by generating novel data; 4. providing rigorous up to date education and training to specialty fellows; 5. advising and working jointly with health care authorities to address speci c problems which may arise. As the criteria were speci c for both the leading and supporting teams (neurosurgeons and endocrinologists basic requirements, experience and organization, neuroradiology, pathology and radiotherapy facilities), the database was composed of Excel® spreadsheets, with a general introduction to the center concept and then speci c issues to be addressed by the Neurosurgery, Endocrinology, Neuroradiology, (Neuro-) Pathology and Radiotherapy Units, as well as other supporting teams. The nal draft was reviewed by all SEB members, modi ed according to their comments and, nally, approved for dissemination (Supplemental Material 1).
The pre-testing phase proceeded with a survey, in which all SEB members were asked to identify candidate centers to be invited, as being worldwide recognized for their expertise and, therefore, likely being putative PTCOE. Nine out of 10 contacted centers agreed to provide activities information derived from registries related to the years 2018-2020 and to complete the database within 60 days. S. F. and M. M. U. oversaw an internal review of all the databases submitted by the candidate centers before data analysis. Eight out of 9 contained errors, missing or con icting data, so these 8 candidate centers were asked to amend their data, provide missing data and/or provide explanations for con icting results. In the same phase, we sought feedback from candidate centers about the size of the database, the feasibility to collect the requested data, and the challenges they encountered in compiling the demanded information.
Revised data were collected during phase 3 of the pilot study, with 7 of 8 revisions obtained that were internally re-analyzed for consistency, before proceeding the next step. Phase 4 was devoted to the review process by referees. SEB members served as referees and every candidate center had two randomly assigned referees blinded to the study center. During this phase, a detailed, ad-hoc evaluating form (Supplemental material 2) was generated to help referees review the large amount of information from the databases of all candidate centers. Based on the criteria for the de nition of PTCOE  [9] , referees were asked to provide their comments and complete the evaluation form within 15 days. A ranking system from 1 (lowest) to 5 (highest) was adopted by the referees, where the ranks 3 and above were considered positive to ful ll the criteria.
Scores were provided for the center facilities and for clinical and educational multidisciplinary activities, as documented in the database. Referees also evaluated each unit, giving scores for clinical, research, dissemination activities and trials of Endocrinology and Neurosurgery Units, the latter being evaluated also on post-surgical complications. Moreover, facilities of the neuroradiology and neuropathology units, as well as clinical activities from the neuroradiology and radiotherapy units, availability of a variety of clinical experts and clinical examinations for the management of speci c complications of pituitary disorders were evaluated by the referees. Upon the completion of their review, the referees evaluated if the overall de nition of PTCOE was satis ed, producing general (open) comments on the candidate centers.
The last phase (phase 5) of this pilot study was dedicated to assembling all referee responses (3 were not received and were completed instead by M. M. U. ) and collecting all available data. Importantly, all 9 of 9 candidate centers satis ed the overall de nition of PTCOE, according to referees' evaluations. Data collection was completed at the end of July 2022, cumulative results were subsequently presented and shared with all SEB members, and participating centers during a web call. During this call, data were discussed in-depth and SEB members highlighted some discrepancies/inconsistencies in the results. Accordingly, speci c requests for revision were sent to participating centers, who all replied within 15 days. The results reported below includes all nal data from the 9 candidate centers and are presented as total and percentage or as median and range.
Timing of center response and referee evaluation: Median time for data submission from candidate centers was 58  days. After the internal review process, a revised database was submitted within a median time of 11 (4-81) days. The referee evaluation phase lasted 12 (3-65) days. A maximum score (equal to 5) for general and speci c activity data from each of the units were obtained for each center. Among the feedback received from Centers about the process, the most common comments were related to the very large dimension of the database and the di culty in collecting all required information.
Endocrinology Personnel and activity volume Analysis of the data related to the endocrinology units from PTCOEs showed that the median number of dedicated pituitary-focused endocrinologists was 6 (4-17) and dedicated pituitary nurses were 3 (1-6) (Fig. 6 ). The median number of patients with pituitary disorders managed annually in the endocrinology units was 1403 (855-1874). Overall, 1335 (342-4230) dynamic tests were performed by endocrinology units. Details of the most frequently used dynamic test are depicted in Fig. 7 , showing that the corticotropin (ACTH) stimulation test was the most frequently executed test, followed by the dexamethasone suppression test and the oral glucose tolerance test (OGTT).
The basic criteria for de ning a PTCOE were published by a panel appointed by the Pituitary Society in 2017  [9]. However, these theoretical criteria required validation in real-life practice before being accepted globally and used for PTCOE accreditation. We designed a voluntary study involving several high-volume tertiary pituitary care centers selected by a panel of experts appointed by the Pituitary Society to test qualitatively the ful llment of previously formulated criteria for PTCOEs. When the review process provided positive qualitative evaluations, we analyzed large amount of data provided by each center to benchmark the activities and infer numerical thresholds against which a given pituitary center could be compared, before being accredited as "excellent". Our results suggest that to perform an adequate assessment of each center, the volume of information to be collected is quite substantial and this, together with the challenges of the pandemic, could lengthen the process of data collection within a center.
This process requires dedicated personnel at the center to extract and synthesize the requested data from existing databases in different units, which may be administratively challenging in many cases. On the other hand, accrediting bodies will also rely on dedicated personnel to review the data, which will require a signi cant organizational effort to evaluate requests for accreditation as PTCOEs from around the world. A further organizational burden may be re ected by a need for on-site visits to verify reliability of submitted data, as performed for assessment of excellence in care for other diseases. Our study did not include a site visit, which would have been a challenge to perform in the setting of the COVID-19 pandemic  [17]. In fact, an "on-site" visit could become a future component of the accreditation process.
On the other hand, progressively more frequent use of chemotherapy for invasive refractory pituitary adenomas  [59]  would require the presence of at least one neuro-oncologist in a PTCOE. This study has several limitations as it relied on self-reported data from surveyed centers and did not include an on-site visit. Nonetheless, it is unlikely that highly reputable centers, which volunteered for the study, provided inaccurate data. However, among the di culties we encountered was a misunderstanding of the purpose of some questions in the survey, which needed clari cations from the Scienti c Committee. Therefore, revised responses were obtained from a few centers after data were assessed for inconsistencies and clari cation of speci c questions. Moreover, questions that required further clari cation were identi ed and reformulated for next steps.
The questionnaire did not address unexplored areas, including control rate in medically treated prolactinoma, since this aspect was not considered crucial for a PTCOE de nition, being well managed by general endocrinologist [60], according to guidelines  [61]. Moreover, side effects of medical treatment [62] or more detail on services important for PTCOE (such as cardiology, neuropsychology, or bone units) [63] were also not recorded. We believe that a compromise should be reached to obtain core information from the centers in a reasonable time frame. Our study demonstrates that with a formulated questionnaire, the accreditation process may require up to eight months for well-organized centers. This period appears reasonable to report current activity data, instead of historical data that may not re ect contemporary situations of evaluated centers.
The application of a derived objective model can be used for accreditation of pituitary centers as PTCOEs. As these proposed criteria are transparently discussed and accepted by the scienti c community, the structure for managing the accreditation process by the Pituitary Society as an accrediting body would need to be established through a wellbalanced, cost-effectiveness analysis. Further steps would optimize the process, such as testing it on a limited number of non-pre-selected centers to assure high standards, objectivity, and transparency. Moreover, these steps will lead to the development of identical databases in all centers and allow a rapid, easy, yearly update while forming a robust basis for research and for possible periodic re-evaluation over time of each PTCOE.
Acknowledgments: Scienti c Evaluation Board:  Andrea Giustina , Italy (PI),  Felipe F Casanueva , Spain (co-PI),  Shlomo Melmed ,  USA ,  Ariel Barkan ,  USA ,  John Wass Uk ,  Steven WJ Lamberts , The Netherlands,  Edward R Laws , US,  Monica Gadelha ,  Brazil ,  Vera Popovic-Brkic , Serbia,  Pamela Freda ,  USA ,  Ursula Kaiser ,  USA ,  Philippe Chanson ,  France ,  Stefano Frara, Italy  (secretary),  Melin M Uygur ,  Turkey Participating Centers: Massachusetts General Hospital, Boston, USA  ( A. T. Tritos )  Cedars-Sinai Medical Center, Los Angeles, USA , ( V. Bonert )  Erasmus Medical Center, Rotterdam, the Netherlands  ( S. J. Neggers  and  R. K. Balvers ),  Charité Universitaetsmedizin, Berlin, Germany  ( L. Haberbosch  and  V. Hubertus )  Leiden University Medical Center, Leiden, the Netherlands  ( A.