dataesr/scientific-paragraphs-categorization huggingface dataset:
This reflects Niger's placement in the FP S-curve. Having only recently shifted from stage 1 to stage 2, they have historically emphasized increasing demand and are now beginning to address supply-side issues as well. 19  Country leadership and coordination of partners was the second largest thematic category within HIPs. Each of the countries included at least 1 question that was focused on how to improve overall coordination. These tended to explore data sharing, documentation, and collaboration. Côte d'Ivoire, Niger, Mozambique, and Uganda each included at least 1 question related to cost and/or financing. This exercise demonstrated that although the term "HIPs" and the associated global initiative were not always known to stakeholders across the 6 countries, they spoke of specific FP practices and were implementing different HIPs.
MPM coordinated the process in Malawi, giving technical guidance to the process as a key MOH-Reproductive Health Directorate personnel. She reviewed the RLA outputs, coordinated the RLA dissemination with stakeholders and reviewed the article. CO coordinated and chaired the in-country stakeholder engagements and reviewed the Ugandan FP RLA. NP contributed to the conceptualization of the activity, conducted KII in Niger, supervised desk review and data analysis for Niger and Cote d'Ivoire, presented results in Niger and Cote d'Ivoire during discussion meetings, and reviewed the FP RLAs. SP reviewed and supported analysis of FP RLA questions in Nepal and provided inputs focused on adolescents/youth. BS provided technical inputs and insights and edited to finalize Nepal's FP RLA and 2-page FP RLA.
Acknowledgments: The authors thank and acknowledge the more than 250 stakeholders in  Côte d'Ivoire, Malawi, Mozambique, Nepal, Niger , and  Uganda  for their technical contributions to the research and learning agendas and for their commitment to advancing the use of evidence in the field of family planning. The authors thank  Lola Flomen  for her support of the consultative processes in both Malawi and Mozambique and contributions to early drafts of those research and learning agendas. We are grateful to the leadership team of the  U. S. Agency for International Development-funded Research for   Scalable Solutions  project for their strategic guidance and technical support, including their substantive feedback on this article. Funding: Support for this article was made possible through the  U.
We stress that knowledge of the riverine tDOC signal and how it will change in time and space is pivotal, as this determines remineralization pathways and key timescales within the carbon cycle. Concerning tDIC, concentrations are indirectly estimated from ions present in river water  (Tank et al. , 2012) , whose monitoring primarily relies on the ArcticGRO pan-Arctic program  (Holmes et al. , 2012). Such a database and continuity in monitoring programs are essential for tracking seasonal-to-interannual variations of tDIC alongside those of tDOC. This is important as the riverine tDIC:tDOC ratio exhibits large variations at the pan-Arctic scale (Figure  S7  in Supporting Information S1). In the Eurasian AO, the tDIC:tDOC ratio falls between 1. 0 and 1. 5, while it is as high as 4. 5 on the North American side  (Holmes et al.
Acknowledgments CB was supported by a Ph. D. fellowship from the  French Ministry of Higher Education, Research and Innovation. This work is part of the  Nunataryuk  project; the project has received funding under the European Union's  Horizon 2020 Research and Innovation Programme  under grant agreement no. 773421. This work was also funded by the  Centre National de la Recherche Scientifique  ( CNRS, LEFE program ). Part of this research was supported by  Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-Climate (GCOM-C)  to AM (contract # 19RT000542 ). This work was also supported by the  NASA Earth Science Division's Interdisciplinary Science (IDS) program  through an award to the  Jet Propulsion Laboratory, California Institute of Technology , under contract with  National Aeronautics and Space Administration  ( 80NM0018D0004 ).
Data Availability Statement Model code and instructions for running ED-SBS simulations are available at https://doi. org/10. 5281/ zenodo. 7417828. ED-SBS forcing files are available at https://ecco. jpl. nasa. gov/drive/files/ECCO2/LLC270/ Mac_Delta. Each user must first register for an Earthdata account at https://urs. earthdata. nasa. gov/users/new in order to access these files in NASA Earthdata.
Accepted Article This article is protected by copyright. All rights reserved. it has been shown that future carbon uptake in ESM projections can be constrained by diagnosing the timing and magnitude of modeled seasonal variations in pCO2  (Goris et al. , 2018). Similarly,  Nevison et al. (2016)  demonstrated, for the Southern Ocean, that the way an ESM represents the seasonal carbon cycle is related to the model's projected carbon uptake. A much weaker seasonal cycle than in higher latitudes is observed in the tropics, where seasonal pCO2 amplitudes are obscured by interannual variability. In polar latitudes where there is seasonal ice cover, the challenge of data sparsity associated with the difficulty of sampling pCO2 through the seasonal cycle presents challenges and uncertainty that are beyond the scope of this study.
Surface observation-based products of pCO2, DIC, and TA Observation-based pCO2 products that are compared for their representation of pCO2 seasonality are listed in Table  2. All of these products are built on surface ocean pCO2 measurements within the Surface Ocean CO2 Atlas database (SOCAT,  Bakker et al. , 2016)  yet they differ significantly in the way they spatiotemporally interpolate or map the sparse observations. Methodologically they include multiple linear regression  (Iida et al. , 2021) , gradient boosted decision trees  (Gloege et al. , 2022) , neural networks  (Chau et al. , 2022; Landschützer et al. , 2016; Zeng et al. , 2022) , ensembles of various machine learning approaches  (Gregor et al. , 2019; Gregor and Gruber, 2021) , and a Bayesian approach that is additionally.
Accepted Article This article is protected by copyright. All rights reserved. constrained by mixed-layer dynamics  (Rödenbeck et al. , 2013 (Rödenbeck et al. , , 2022)). Additionally, one neural network product applies adjustments to the SOCAT data to account for temperature gradients between the ocean surface and the depth of the seawater intake on the observing ships and for the cold skin layer effect  (Watson et al. , 2020). Of central importance to our study is that three of these products (JMAMLR, OceanSODA-ETHZ, and CMEMS-LSCE-FFNN) additionally provide time-varying surface DIC and TA products spanning 1985-2018. While we use all available pCO2 products listed in Table  2  for our analysis of seasonal cycles of CO2-fluxes and pCO2, our analysis greatly benefits from using these three products to attribute changes of pCO2 seasonality to its drivers.
ACKNOWLEDGEMENTS The authors have no conflicts of interest. KR and JS thank the  Bjerknes Centre for Climate Research  and the  IBS Centre for Climate Physics  for travel funding. KBR, KS, JEK, and SSh were supported by the  Institute for Basic Science  (grant no. IBS-R028-D1 ). The simulations with the CESM2-LE were conducted on the  IBS/ICCP supercomputer "Aleph ", a 1. 43 petaflop high-performance Cray XC50-LC Skylake computing system. AJF was supported by  NOAA's Pacific Marine Environmental Laboratory (PMEL). This is PMEL contribution 5502. JS acknowledges support by the  Research Council of Norway  (grant no. 270061 ) and JS and Ngo are thankful for provision of computational/storage resources.
by  UNINET/sigma2/(nn/ns2980k ). JH was supported by the  Initiative and Networking Fund of the Helmholtz Association  ( Helmholtz Young Investigator Group Marine Carbon  and  Ecosystem Feedbacks  in the Earth System [MarESys], grant number  VH-NG-1301 ). PL received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 821003  (project  4C ). LK was supported by  NSF's Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM)  Project under the  NSF  Awards  PLR-1425989  and  OPP-1936222 , with additional support from  NOAA  and  NASA. PV, JT, and Ngo acknowledge funding from the  Research Council of Norway  ( COLUMBIA-275268 , and  CE2COAST-318477 ). MI, KT, and YI were supported by the  Environment Research and Technology Development Fund  ( JPMEERF21S20810 ) of the  Environmental Restoration and Conservation Agency provided by Ministry of the Environment of Japan.
MI and KT were also supported by  MEXT KAKENHI  Grant Number  JP19H05700. JDM, NGr, and LGr acknowledge support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 821003  (project  4C ) and no. 820989  (project  COMFORT ). MG and TTTC acknowledge funding from the  European Copernicus Marine Environment Monitoring Service  (no. 83-CMEMSTAC-MOB ). This publication is partially funded by the  Cooperative Institute for Climate, Ocean, and Ecosystem Studies (COCOES)  under  NOAA Cooperative Agreement   NA20OAR4320271 , Contribution No. 2023-1266. MG and TTTC were supported by the  COMFORT Research and Innovation Action  project funded under the  Horizon 2020 Societal Challenges programme  of the  European Union. 19449224, ja, Downloaded from https://agupubs.
OPEN RESEARCH All of the RECCAP2 data has been published by Müller (2023) and is available through https://zenodo. org/record/7990823. 19449224, ja, Downloaded from https://agupubs. onlinelibrary. wiley. com/doi/10. 1029/2023GB007798by Cochrane France,  Wiley Online Library on  [ 30/08/2023 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on  Wiley Online Library  for rules of use; OA articles are governed by the applicable Creative Commons License.
(2023), CMEMS-LSCE: A global 0. 25-degree, monthly reconstruction of the surface ocean carbon system, Earth System Science Data Discussions, https://doi. org/10. 5194/esdd-2023-146. 19449224, ja, Downloaded from https://agupubs. onlinelibrary. wiley. com/doi/10. 1029/2023GB007798by Cochrane France,  Wiley Online Library on  [ 30/08/2023 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on  Wiley Online Library  for rules of use; OA articles are governed by the applicable Creative Commons License.
Earth System Science Data Discussions, 1-27. 19449224, ja, Downloaded from https://agupubs. onlinelibrary. wiley. com/doi/10. 1029/2023GB007798by Cochrane France,  Wiley Online Library on  [ 30/08/2023 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on  Wiley Online Library  for rules of use; OA articles are governed by the applicable Creative Commons License.
(a) Patient population The present prospective study was conducted in June 2020 on patients initially treated in ICU. We considered a convenient sample of 12 patients (aged ≥ 18 years) who recovered from sequelae in a rehabilitation facility after a COVID-19-related acute respiratory distress syndrome. Blood samples were drawn two months after hospital discharge. Demographic data were collected for all patients from the hospital electronic patient record, including medical history, ICU and hospital length of stay (LOS), critical illness severity score according to the Simplified Acute Physiology Score (SAPS) II  [31] , and organ supports and their durations (mechanical ventilation and renal replacement therapy). All patients were fed a standard hospital diet. No patient was under antioxidant supplementation.
Korea. The size of the stars is proportional to the magnitude mb of the events. b) Observed un ltered velocity records on the vertical channel of station MDJ. The traces' colors match those in a). The amplitude scaling for DPRK 1 to 5 is the same, and is different to the one for DPRK 6. c) Filtered (0. 033-0. 066 Hz) vertical velocity seismograms from b) for the six nuclear tests and for the collapse (purple) that immediately followed DPRK6 (orange). The traces are plotted with the same scale, except for DPRK6 whose amplitudes are divided by a factor of 10. Figure  1  shows the seismic traces of the six DPRK nuclear tests recorded at the Chinese station MDJ, located at about 370 km to the north of the test site. The un ltered seismograms (Fig. 1b ) and the long period ltered data (Fig. 1c ) of the six events show that their waveforms are highly correlated despite their size difference.
Data, grid and velocity models North Korea has a relatively low seismic rate (Fig. 5 ). The natures of the seismic activity, reported by the European Mediterranean Seismological Centre (EMSC), between 2009 and 2022, with magnitude (M) of 4. 0 and larger, are contrasted. We count super cial nuclear explosions, deep natural events offshore the northeastern region of the country, shallow mining events in known regions near the Chinese borders, and sequences of induced small magnitude earthquakes following the DPRK events in the vicinity of Punggye-ri  (Tian et al. , 2018; Yao et al. , 2018). The rst nuclear explosion in October 2006 with a magnitude 4. 0 is also included (Fig. 5 ). Seismic records of the events from regional broadband 3-component stations from the International Data Centre (IDC) and Incorporated Research Institutions for Seismology (IRIS) networks are collected and processed.
The azimuthal distribution of the seismic stations is chosen such as to recover most of the radiation pattern of the potential sources as possible around the region. From a set of eight well distributed stations we de ne the optimal distributed station combination composed of only four stations: MDJ, INCN, MAJO and BJT (Fig. 5 ). A complementary station combination could be composed of the following four stations: USRK, KSRS, MAJO and BJT, stations USRK and KSRS being primary IDC stations. For comparison, others previously mentioned operating systems in Japan, California, Taiwan, and Alaska use from three to ve stations per grid. Data processing involves instrumental response removal and integration of the velocity records to displacement traces. In a second time, data segments are ltered at long periods between 0.
This shows that the tested con guration for monitoring North Korea events is well calibrated for the detection of M4 + super cial events in North Korea. Moreover, sources with different mechanisms can be also spotted such as the collapse that followed the latest nuclear explosion by 8 minutes and 30 seconds (Fig. 11 ). For this given event observed in the DPRK 6 explosion's coda, the goodness of t is lower but the mechanism is well characterized as an implosion. Its moment magnitude Mw compares with the one of the January 2016 explosion when considering long period seismic regional data (Table  2  and Fig. 1c ). Furthermore, the GRiD MT approach for the seismic monitoring in North Korea limits the false detection rate of events whose source depths are greater than the rst few kilometers of the crust (Fig.
Discussion and conclusion These results show that the GRiD MT algorithm can become an interesting tool for detecting moderate magnitude events of interest for NDCs and other operational seismological institutes. However, such method depends on a very in-depth con guration analysis to identify the stations to be used. The rate of data availability over a relatively long time period may be important to take into account. Also, in the absence of data transmission from one or more stations, the procedure is strongly affected. For a long period in 2013, station INCN was not operating. The missing required data segments from station INCN triggered the obsolescence of the presented con guration, and no detection of the DPRK nuclear test in February 2013 could be obtained (Table  2 ). In such case, two options are available.
4 ). Also, adding more stations implies larger data storage for the Green's functions catalogs, and more importantly implies to rely even more on their availability. Furthermore, this requires quasi-perfect knowledge in velocity models for a large number of stations. While it is possible for regions with relatively simple geological properties, this may not be true for more complex tectonically active regions. Here, large VR peaks are observed for the Mw4. 5 + nuclear tests, and these events can then be automatically detected in near-realtime above a given VR threshold. For lower magnitude events, the long period signal to noise ratio appears to be too low in the 15-30 s band period (i. e. , DPRK1). In order to better invert and detect lower magnitude events, the option is to consider higher frequency data.
Acknowledgements: Great thanks to the  IDC  and IRIS networks for sharing seismic data. Softwares such as the Generic Mapping Tool (GMT) (https:// www. generic-mapping-tools. org/) and SAC (https:// ds. iris. edu/ds/nodes/dmc/software/downloads/sac/) were used to produce the gures and analyze data. The Computer Program in Seismology (CPS 330) written by  Robert B. Herrmann  ( Department of Earth and Atmospheric Sciences, Saint Louis University ) was used for the calculation of Green's functions. The Matlab and GMT scripts from  Carl Tape  (https://github. com/carltape/mtbeach) were used for the representation of the source mechanisms on the lune diagram.
This combined likelihood reads: L(ε; σ ) = B N N tot (Λ s (ε) + σ • ΔΛ s (ε)) ∨ Λ b × B M M tot Λ b × Ĝ σ. ( 6 ) The signal expectation is given flexibility according to its uncertainty ΔΛ s using a Gaussian pull term Ĝ(σ ) = e -σ 2 /2 , which accounts for limited simulation statistics and additional systematics. Equation 6 has zero degrees of freedom and hence model discrimination can only be obtained through a combination of multiple datasets, i. e. calibration source positions, or by exploiting channel event correlations. While the former sounds trivial, e. g. an absorption length can be estimated from measurements at different distance, the latter requires explanation. Let's imagine two photosensors i ∈ {A, B}, each probing the LAr with a certain photon detection probability ξ i ( x). Considering an energy deposition (E, x), the probability to see light in both channels depends on ξ A ( x) • ξ B ( x), while events triggering only channel A test ξ A ( x) • (1 -ξ B ( x) ).
Each pattern occurrence expectation L( ε; σ ) = P B N N tot G (Λ s ( ε) + σ • ΔΛ s ( ε)) • Λ b × G B M M tot Λ b × Ĝ σ. ( 7 ) The number of degrees of freedom is (2 nn -1), where n is the number of channels. Several datasets may be combined as the product of the individual likelihoods. Equation  7 implicitly includes correlations between photosensors, which avoids a potential overestimation of the overall veto efficiency that could arise from unaccounted systematics. Given a large set of channels the number of possible patterns may be immense, but can be truncated by e. g. neglecting events with detection pattern higher than a certain multiplicity.
Application In order to constrain the aforementioned effective channel efficiencies ε i , dedicated data taken under conditions that could be clearly reproduced with a Monte Carlo simulation was needed. As the Gerda background data is a composition of various contributions and itself under study, only the peculiar conditions of a calibration run, where the energy depositions originate from a well-characterized source, allow for such studies. However, usual calibrations were taken with the purpose to guarantee a properly defined energy scale of all HPGe detectors and were performed with three 228 Th calibration sources of O(  10 ) kBq activity each. The resulting rate in the LAr was far too high to study the veto response and was hence not even recorded during those calibrations. For this reason, special calibration data using one of the former Phase I 228 Th sources with an activity of < 2 kBq was taken in July 2016.
The source was moved to 3 different vertical positions (see  [40]  for details about the calibration system). The characteristics of the datasets are compiled in Tab. 1. To avoid β particles contributing to the coincident light production and enable a clean γ -only signature, an additional 3 mm copper housing was placed around the source container. 5 ach configuration was simulated with e8 primary decays in the source volume. The maximum likelihood analysis was performed on 208 Tl full energy peak (FEP) events with an energy deposit of 2615  (10)  keV in a single HPGe detector. As no direct β transitions to the ground nor first excited state of the 208 Pb daughter nucleus are allowed, a minimum of 3. 2 MeV is released in γ 's, which almost always includes a transition to the intermediate 2615 keV state.
Probability maps To finally evaluate the three-dimensional photon detection probability ξ( x) , a dedicated simulation of VUV photons sampled uniformly over the LAr volume around the HPGe array was performed. Positive light detections with any photosensor i were determined taking into account the efficien- The pdf is normalized to reproduce the total count rate in data. Despite small shape discrepancies, the predicted LAr veto survival probability (bottom panel) matches the data over a wide range of energies, even far from the model optimization energy region (gray band). A variable binning is adopted for visualization purposes cies ε i as obtained in Sect. 5. 1. For convenience, ξ( x) is stored as a discrete map, partitioned into cubic "voxels" of size 3 × 3 × 3 mm 3. This size matches the characteristic scale of the probability map gradients expected in Gerda.
Hence, the detection probability ξ k associated with voxel k corresponds to the ratio between positive light detections and total scintillation photons generated in the voxel volume. Figure  5b  shows a projection of this object. As outlined in Sect. 3 it allows to determine the expected number of signal photons and the corresponding event rejection probability, solely based on energy depositions in the LAr. Outside the densely packed array O(0. 1)%-level values are reached. Figure  5c  compares the energy distribution, before and after the LAr veto cut, of the 228 Th source data with the model prediction. Small discrepancies are expected from geometry inaccuracies and the modeling of charge collection at the HPGe surface. However, the event suppression (shown in the bottom panel) is reproduced over a wide range of energies far off the 208 Tl FEP-events used for model optimization.
Distortion studies As anticipated in Sect. 4, many uncertainties affect the simulation of the LAr scintillation in the Gerda setup. The channel efficiencies ε extracted from calibration data, as described in Sect. 5, absorb systematic biases that scale the detection probability by global factors (e. g. the LAr scintillation yield and the TPB quantum efficiency), but cannot cure local uncertainties (e. g. due to incorrect germanium reflectivity or fiber shroud coverage). A heuristic approach has been formulated to estimate the impact of such simulation uncertainties on the LAr veto model. The distortion of ξ( x) induced by varying input parameters can be conservatively parametrized by means of an analytical transformation T: ξ( x) → ξ ( x). As an additional constraint, the transformed ξ ( x) must still reproduce the calibration data presented in Sect.