dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008) , driven by ECMWF analysis fields. Grid nudging of wind speed, temperature, and water vapor mixing ratio was employed within the PBL, with a nudging coefficient of 0. 0003 s -1. WRF-Chem was adopted to predict GOCART dust emissions  (Ginoux et al. , 2001)  along with the meteorology. The WRF-CAMx preprocessor (version 4. 2; Environ, 2014) was used to create CAMx ready input files, collapsing the 33 vertical layers used by WRF into 14 layers in CAMx but keeping the layers up to 230 m above ground level identical. Biogenic VOC emissions were computed by applying the MEGAN emission model v2. 04. Sea salt emissions were computed using published algorithms  (de Leeuw et al. , 2000; Gong, 2003). Aarhus University (Denmark) applied the WRF-DEHM modeling system over EU and NA. The DEHM model used anthropogenic emissions from the EDGAR-HTAP database and biogenic emissions were calculated using the MEGAN model.
The gas-phase chemistry module includes 58 chemical species, 9 primary particles, and 122 chemical reactions  (Brandt et al. , 2012). Secondary organic aerosols (SOA) were calculated following the two-product approach assuming that hydrocarbons undergo oxidation through O 3 , OH, and NO 3 and for only two semi-volatile gas products  (Zare et al. , 2014). However, the module is simple because it does not include aging processes and further reactions in the gas and particulate phases  (Zare et al. , 2014). WRF-CMAQ1 was applied by ITU (Istanbul Technical University) over EU. The Meteorology-Chemistry Interface Processor (MCIP) version 3. 6  (Otte and Pleim, 2010)  was used to process WRF output for CMAQ. The MEGAN v2. 1  (Guenther et al. , 2012)  model was used to calculate the biogenic VOC emissions from vegetation, using surface temperature and radiation from MCIP output.
CMAQ v4. 7. 1  (Foley et al. , 2010)  was configured with the CB05 chemical mechanism and the AERO5 module  (Foley et al. , 2010)  for the simulation of gas-phase chemistry and aerosol and aqueous chemistry, respectively. The WRF-CMAQ2 system was applied by Ricardo Energy & Environment (Ricardo E&E) over EU. It was configured using WRF v3. 5. 1 and CMAQ v5. 0. 2. The CMAQ model adopted the CB05-TUCL chemical mechanism  (Whitten et al. , 2010; Sarwar et al. , 2011)  and the AERO6 threemode aerosol module  (Appel et al. , 2013). MCIP version 4. 2 was used to process WRF output for CMAQ. The MEGAN v2. 0. 4 model was used to calculate the biogenic VOC emissions from vegetation, using surface temperature and radiation from MCIP output. The WRF-CMAQ3 modeling system was applied by the University of Hertfordshire and utilized the uncoupled version of the WRF v3.
4. 1 model and CMAQ v5. 0. 2. The results from WRF simulations were preprocessed for CMAQ using MCIP version 3. 6  (Otte et al. , 2005). In the CMAQ model, the gas-phase chemical mechanism was based on Carbon Bond chemical mechanism version 5  (Foley et al. , 2010)  with updated toluene and chlorine chemistry (CB05-TUCL), and the aerosol chemical reaction was treated with AERO6 module. The biogenic emissions were derived from MEGAN. The WRF-CMAQ4 simulation was performed by Kings College (UK) using CMAQ v5. 0. 2  (Byun and Schere, 2006) , with CB05 chemical mechanism that included aqueous and aerosol chemistry. The CMAQ model is driven by meteorological fields from WRF v3. 4. 1. The anthropogenic emissions for most of the model domain are from MACC and the missing information was filled with the emissions provided by EDGAR/HTAP.
The biogenic emissions were estimated using the BEIS3 model. The dust and sea salt  (Gantt et al. , 2015)  emissions were generated using CMAQ inline modules. HZG used the COSMO-CLM meteorological model to drive the CMAQ model. For AQMEII3, CMAQ version 5. 0. 1 was used, with the CB05-TUCL scheme and the multipollutant aerosol module AERO6. CMAQ was run using the optional in-line calculation of dry deposition velocities. Wet deposition processes include in-cloud and sub-cloud scavenging processes. All atmospheric parameters were taken from regional atmospheric simulations with the COSMO-CLM (CCLM) mesoscale meteorological model (version 4. 8) for the year 2010  (Geyer, 2014)  using National Centers for Environmental Prediction (NCEP) forcing data employing a spectral nudging method for large-scale effects  (Kalnay et al.
, 1996). CCLM is the climate version of the regionalscale meteorological community model  COSMO (Rockel et al. , 2008; Steppeler et al. , 2003; Schaettler et al. , 2008). CCLM uses the TERRA-ML land surface model  (Schrodin and Heise, 2001) , a turbulent kinetic energy (TKE) closure scheme for the PBL  (Doms et al. , 2011) , cloud microphysics after  Seifert and Beheng (2001) , the Tiedtke scheme  (Tiedtke, 1989)  for cumulus clouds, and a long-wave radiation scheme following  Ritter and Geleyn (1992). The meteorological fields were processed afterwards to match the 24 × 24 km 2 CMAQ grid using the LM-MCIP preprocessor. The emission input for CCLM-CMAQ is based on the EDGAR HTAPv2 database, interpolated to the CMAQ model grid and aggregated following the SNAP emission sector nomenclature. Sector-specific hourly temporal profiles and speciation factors of PM and VOC species were applied by the SMOKE for Europe emissions model  (Bieser et al.
Observational data used The observational data used in this study are the same as the dataset used in second phase of AQMEII  (Im et al. , 2015a, b)  and was derived from the surface air quality monitoring networks operating in EU and NA. In EU, surface data were provided by the European Monitoring and Evaluation Programme (EMEP, 2003; http://www. emep. int/) and the European Air Quality Database (AirBase; http://acm. eionet. europa. eu/databases/airbase/). In NA observational data were obtained from the NAtChem (Canadian National Atmospheric Chemistry) database and from the Analysis Facility operated by Environment Canada (http://www. ec. gc. ca/ natchem/). For the purposes of comparing the models against observations, only stations with data completeness greater than 75 % for the whole year and elevation above ground below 1000 m were included in the analysis.
Stations with continuous missing records for periods longer than 15 days were removed from the dataset. No imputation on missing values was performed. In addition, we also make use of vertical profiles of ozone, temperature, and wind speed data measured by ozonesondes and extracted from the World Meteorological Organization (WMO) World Ozone, and Ultraviolet Radiation Data Centre (Toronto, Canada) and made available to the AQMEII community. These measurements report vertical profiles of ozone at several vertical levels. Further details on these data are given in  Solazzo et al. (2013). Time-averaged statistics were calculated after the spatial aggregation of the modeled and observed time series over the subregions shown in Fig. 1  and prior to the spectral decomposition (the original time series were spatially averaged first and then this spatial average time series was spectrally decomposed).
As noted in the introduction, unsupervised hierarchical clustering was used to determine subregions where the LT and SY components showed similar characteristicsspatial averaging within these subregions was carried out due to the similarity of the observation data within these regions, implying they will experience common physical and chemical characteristics. Errors due to the heterogeneity induced by country-specific emission profiles (in EU) are therefore included in the DU component. As a consequence of the spatial averaging, the relative importance of the ID component is likely reduced, since the ID fluctuations are highly variable in space  (Hogrefe et al. , 2014). Furthermore, no land-use type filtering was applied to the stations used for evaluation. While this choice limited impact on the SY and LT components  (Solazzo and Galmarini, 2015; Galmarini et al.
The difference in altitudes (491 the former and 1000 m a. s. l. the latter) and the complex terrain of the alpine region might also be responsible for the large model differences at these two (relatively close) stations. Vertical profiles of temperature in NA (Fig. 6 ) show strong surface bias (negative) at station 021 and 457 (both close to the western border of the domain) for both models. At sta-tion 021 (data collected under daylight conditions) the bias becomes positive and small in magnitude above the PBL, whereas at station 457 (data collected under nighttime conditions) the bias keeps the same sign throughout the troposphere. At the other stations, the bias within the PBL is overall small and either positive (107, 456) or slightly negative  (stations 458, 338). Bias profiles for WS at eight ozonesonde stations in EU (Fig.
Wind direction The spatial and temporal distributions of WD are reported in Fig. 8. The boxes summarize the temporal and spatial variability of the WD values at the receptors of each subregion (no averages were applied). For EU1 (Fig. 8a ), the median of all models but WRF-CAMx is within ±5 • of the observation, similar to EU2. The modeled 22th and 75th percentiles are also in line with the observations in these two subregions (the CCLM model predicts slightly larger variability). The EU3 subregion is topographically more complex, and the analysis is based on four stations with only 55 % data va- lidity over the entire period. Southern winds are predominant (based on the observation), while the models show large variability and even several instances of WRF (but not all) and ECMWF data tend to underpredict the median value.
Indeed, quality and safety indicators targeting older inpatients are essential to support and drive quality improvement, as well as inform health care stakeholders. These indicators are also of great interest to compare the performance of various health systems  [16]. Some commonly used indicators based on large administrative health databases (eg, hospital discharge and insurance claims databases) could help assess and compare patient safety and health care quality across hospitals and health territories in both countries. They could also enable comparisons of Swiss and French health systems' performance in providing high-quality, safe care to vulnerable older inpatients  [12, 15] , which are a source of "cross-country learning" and improvement  [17, 18]. For example, the Patient Safety Indicators (PSIs)  [19] , which have been developed by the US Agency for Healthcare Research and Quality (AHRQ)  [19]  and adopted internationally  [20, 21] , could screen acute hospital discharge data for potentially avoidable adverse events occurring during hospitalization.
Data Visualization For both Switzerland and France, we will construct comparative graphic displays of the anonymized results for scientific, public, or institutional reporting. They will comprise (1) a Swiss and French atlas documenting territorial variations in risk-adjusted GPSIs; (2) funnel plots reflecting between-hospital and between-hospital category variations in risk-adjusted GPSIs; (3) individual control charts of risk-adjusted GPSIs displaying temporal variations for each hospital, hospital category, and territory; and (4) national, territorial, and individual temporal trends in GPSIs over the period 2012-2014. Regarding GQIs, the clinical panel will select candidate indicators among those suggested in Textbox 1. We will then apply a similar development and validation process, except for criterion and predictive validity assessment.
Candidate GPSIs and GQIs AHRQ PSIs are measures based on administrative health data that "screen for adverse events that patients experience as a result of exposure to the healthcare system, and that are likely amenable to prevention by changes at the system or provider level"  [19]. Likewise, candidate GPSIs will screen Swiss and French linked administrative data for thromboembolic or hemorrhagic adverse events that resulted from exposure to medical or surgical conditions requiring anticoagulant treatments, and occurred during hospital stay or after discharge (Textbox 1). Hospital-level GPSIs will measure, for each acute care hospital, the cumulative incidences of in-hospital and postdischarge anticoagulant-related adverse events and be defined with a denominator (ie, index stays in a given hospital within a 1-year period) and a numerator (ie, denominator stays resulting in the adverse event of interest during hospitalization and up to 30 days, 60 days, 90 days, 6 months, and 1 year after discharge).
We will identify index stays and hemorrhages using hospital discharge data and linked administrative health data, respectively. For each GPSI, 2 GQIs will measure for each hospital (1) the proportion of index hospital stays resulting in the recommended anticoagulation (drug, dose, frequency, and duration) at discharge, and up to 30 days, 60 days, 90 days, 6 months, and 1 year after discharge; and (2) the median duration of recommended anticoagulant treatment after discharge for index hospital stays. For example, the GPSI described above will be completed by the following GQIs: (1) the proportion of 2012 hospital stays for elective total hip arthroplasty in patients aged 65 years and over resulting in the recommended anticoagulation (drug, dose, frequency, and duration) at and up to 30 days after discharge; and (2) the median duration of recommended anticoagulant treatment after discharge for these index stays.
Administrative Health Data Scope and Time Frame We will extract Swiss administrative health data from the Helsana Group data warehouse and will include 2010-2015 insurance claims data individually linked to 2012-2015 acute care hospital discharge data and individual measures of dependency for nursing home residents. Since data collection of inpatient diagnosis and procedure codes and dependency measures started in 2012, only insurance claims data will be available for the period 2010-2012. We will extract French data from the national health insurance information system (Système National d'Information Inter-Régimes de l'Assurance Maladie [SNIIR-AM]) hosted by CNAMTS  [55]. Since 2007, SNIIR-AM has included individually linked data from various administrative databases for the entire French population: all hospital discharge data (ie, discharge data from acute, postacute, rehabilitation, psychiatric, and long-term care hospitals and from hospital-at-home facilities); insurance claims data; data on nursing homes residents; and data on health professionals' characteristics.
We will include only those patients insured under the general scheme, as vital status and death date are exhaustive only for these patients. Multimedia Appendix 1 comprehensively describes Swiss and French data, including linkage methods and success rates. In both countries, we will identify index hospital stays and in-hospital adverse events from 2012-2014 hospital data and adverse events up to 1 year after discharge from 2012-2015 linked hospital and insurance data (Textbox 2). Similarly, we will test anticoagulation management up to 1 year after discharge over the period 2012-2015 using linked data (Textbox 2). GPSI risk-adjustment factors will be estimated using data from 2010-2014 to account for patient conditions up to 2 years before their admission. Since hospital discharge data will not be available for the period 2010-2012 in Switzerland, we will derive risk-adjustment factors from insurance claims data only (Textbox 2).
Data Source and Criterion Validity Metrics We will assess GPSI accuracy by measuring the performance of the algorithm in identifying corresponding adverse events based on administrative health data compared with a reference standard (medical record screening). We will identify GPSI+ (ie, complicated) and GPSI-(ie, uncomplicated) hospital stays from administrative data based on the algorithm. Then, we will randomly select a sample of these GPSI+ and GPSI-stays and verify whether an adverse event is recorded in the corresponding medical record. GPSI criterion validity will then be assessed based on the sensitivity, specificity, PPV, and negative predictive value of the algorithm.
Every GPSI risk-adjustment model will undergo a 3-fold cross-validation, in which we will use a random sample comprising one-third of the whole data to develop the empirical model (development dataset), another one-third to estimate the parameters (estimation dataset), and the remaining one-third to test the predictive validity of the model (validation dataset)  [57]. The predictive validity of the model will be assessed by measures of its calibration (Hosmer-Lemeshow goodness-of-fit test) and discriminatory power (C statistic)  [58]. We will provide expected cumulative incidences with their 95% confidence intervals. We will calculate GQIs as (1) the proportion of at-risk hospital stays resulting in the recommended anticoagulation (drug, dose, frequency, and duration) at discharge and up to 30 days, 60 days, 90 days, 6 months, and 1 year after discharge; and (2) the median duration of recommended anticoagulant treatment after discharge for index hospital stays.
We will also consider a single proxy measure of older inpatient complexity-similar to the Charlson and Elixhauser indexes-that could encompass all these conditions. GPSIs will also be adjusted on individual thromboembolic or bleeding risk scores, including venous thromboembolism risk scores for surgical patients (Caprini and Rogers scores)  [44] , ischemic stroke risk scores for patients with atrial fibrillation (CHA 2 DS 2 -VASc or ATRIA score)  [43] , and a major bleeding risk score for patients on anticoagulation (HAS-BLED)  [43]. CHA 2 DS 2 -VASc and HAS-BLED have already been estimated based on French data  [45]  and will be adapted to Swiss data. We will develop the remaining scores in both countries.
Indeed, many countries, including France, try to implement frailty or vulnerability screening and management programs (eg, the French initiative "parcours de santé pour les personnes âgées en risque de perte d'autonomie" [PAERPA]) to avoid institutionalization and hospitalization, improve older citizens' quality of life, and reduce health care costs  [76, 77]. Finally, assessing patient complexity and vulnerability, both at the individual and population levels, may contribute to better planning of appropriate health and social care services  [76, 77]. Developing or adapting individual thromboembolic or bleeding risk scores based on administrative health data is the third major strength of our study. These scores will be included in GPSI risk-adjustment models and may also contribute to assessing the quality of anticoagulant management by comparing patients' risks with the treatment they actually received.
As insured or cultural groups may differ in their risk factors, compliance with anticoagulant treatments, access to and utilization of health services, and geographic distribution (eg, employees may be underrepresented in rural areas), the internal and external validity of our study might be affected by selection bias  [78]. Albeit figuring that this bias should not significantly affect our indicators, we will thus generalize our findings to the study populations and gather information on older inpatients excluded from our study. In particular, we will make a request to CNAMTS to access their data on French residents enrolled in other schemes than the general one. French data will thus cover the entire population. Second, insurance claims data may also be incomplete or "selected. " For example, they will not include coded diagnoses on adverse events occurring in outpatient settings and will not provide information on patients' adherence to treatment and surveillance.
However, we expect to capture almost all postdischarge adverse events by screening readmissions, prescriptions or modifications of anticoagulant or antihemorrhagic treatments, outpatient procedures (eg, lower limb venous ultrasonography), and laboratory tests (eg, hemoglobin and international normalized ratio tests). Moreover, we believe that the quality of anticoagulant management is accurately assessed using large administrative databases linked over time. Swiss insurance claims may also be missing differentially according to health insurance deductibles (ie, SwF 300 to 2500 per year)  [78] [79] [80]. Indeed, policyholders with the highest deductibles tend not to claim reimbursement of medical expenses when their annual amount does not reach the deductible, which leads to missing claims.
However, this potential selection bias should not be significant, since health insurers estimate that only 2% to 3% of all invoices are not sent for reimbursement  [52]. Finally, French data from SNIIR-AM may also lack some information regarding fully reimbursed long-term conditions. Indeed, long-term conditions are often underreported in patients fully covered for several long-term conditions or benefiting from a complementary insurance, or in nursing home residents  [81]. We will overcome this limitation by deducing missing long-term conditions from medications coded in insurance claims or from diagnoses coded in hospital discharge records. Third, hospital discharge data may also provide incomplete information. For example, data on inpatient medications will be missing for both countries, as they are not mandatory for reimbursement.
We will then infer the prescribed anticoagulant treatment during hospital stay from that prescribed just after discharge using insurance claims data. Also, in Switzerland, we will not be able to identify reasons for readmission or retransfer to the same hospital within 18 days after discharge if the second stay is grouped in the same Major Diagnostic Category as the initial one. Indeed, according to the SwissDRG billing rules, such a readmission or retransfer is merged with the initial admission, leading to a single stay and discharge record. Furthermore, Swiss hospital discharge data are limited to acute care hospitals and do not cover hospital-at-home facilities, and rehabilitation or psychiatric hospitals. Thromboembolic and hemorrhagic adverse events occurring in these settings will thus be missed except severe ones that would necessitate transfer to acute care facilities.
Acknowledgments The authors acknowledge the independent experts who reviewed the protocol:  Dr Claudine Berr, MD, PhD  (INSERM,  U1061 ,  Faculty of Medicine, Hôpital La Colombière, Montpellier, France; Université Montpellier 1, Hôpital La Colombière, Montpellier, France );  Dr Jacques Donzé, MSc, MD  ( Division of General Internal Medicine, Bern University Hospital, Bern, Switzerland ;  Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA ;  Harvard Medical School, Boston, MA, USA) ;  Prof Marcel Goldberg, MD, PhD  ( Versailles St-Quentin University UVSQ ,  UMS 011 ,  UMR-S 1168 , Garches, France; Population-based  Epidemiologic Cohorts Unit, INSERM ,  UMS 011 , Villejuif cedex, France; INSERM, VIMA: Aging and chronic diseases, Epidemiological and public health approaches,  U1168 ,  Villejuif cedex, France) ;  Prof Thomas Perneger, MD, PhD  ( Division of Clinical Epidemiology, Geneva University Hospitals , and  Faculty of Medicine, University of Geneva, Geneva, Switzerland).
We also read Lévi-Strauss, with special emphasis on The elementary structures of kinship  ([1949] 1969)  and The savage mind  (1966). When it came time for me to take a Ph. D. qualifying exam on Africanist anthropology, my committee presented me a list of eighty titles to devour, many of them classics from British anthropology, including  Meyer Fortes' (1949 , 1959)  books on the Tallensi of Ghana, Siegfried  Nadel's (1942)   Bambara (1951) , and, of course, Rouch's La religion et la magie  Songhay (1960). By the time I arrived in Niger to begin field studies among the Songhay people, I possessed a broad knowledge of the classics in anthropology and linguistics, but had no firm idea how such knowledge might help me understand, let alone write about, the Songhay world. In the field, I collected data on kinship, patterns of economic exchange, elements of social change.