dataesr/scientific-paragraphs-categorization huggingface dataset:
All the files used to generate the a and K coefficients are available in https://github. com/cyrilvoyant/ARTU. git (MðRÞ matrices and codes). For a forecast at the horizon h, the procedure is as follows: I Calculate r(h) and r(2h) using in-sampling data; IObtain the a and of K values by consulting on Figs. 1  and 2  or generating them more precisely using the code available in Appendix D. 5 and the matrices ðMðRÞÞ; IDerive forecast using Eq. (  15 ). In the end, let us suppose that we seek to use the filtration previously stated, for a signal concerning h ¼ 1, if one estimates r(1) ¼ 0. 4 and r(2) ¼ 0. 3; we therefore have xtþ1 ¼ Sy t À Py tÀ1 þ ð1 þ P À SÞy, Cf. Eqs. 11e15, and so: Ix tþ1 ¼ 0:33y t þ 0:16y tÀ1 þ 0:51y for R ¼ 0. 01, a ¼ 0. 60 and K ¼ À0. 27 hence S ¼ 0. 33 and P ¼ À0. 16; Ix tþ1 ¼ 0:34y t þ 0:15y tÀ1 þ 0:51y for R ¼ 0.
conclusions: IIrradiance time series (I GH ) are measured in several sites around the world with different climatic characteristics. For each of them, the K€ oppeneGeiger classification (KG)  [34] , the expected forecastability (F)  [35]  and the geographic coordinates are provided. At least two years of data are available for each site and used during the simulations. Usually one year of data is considered sufficient to validate a forecasting method, though two years are preferable; IThe models are evaluated during daytime irradiance values only, filtering the checked data (according to quality control  [36, 37] ) of the solar zenith angle (I GH where zenith angle q Z > 85 are excluded); IThe data was acquired every 15 min or every hour, and the corresponding forecast horizons are between 15 and 150 min, in the first case, and between 1 and 10 h, in the second case.
Sensitivity to change in data type In this Section, we study the sampling frequency of solar irradiance measurements. Moreover, here, we focus on inclined (or tilted) radiation (TGI of 30 )  [41] , which is generally more difficult to predict because the anisotropy of the sky diffusion plays important role and is difficult to quantify. No hypothesis for the nature of radiation has been formulated in Section 2; it is therefore a question of checking if the preceding conclusions are valid in this particular case. The data used were measured on the Tilos site, a small Greek island with a Mediterranean climate (36 26 0 00 00 N, 27 22 0 00 00 E, alt. 100 m). This site has a forecastability F ¼ 82. 5; the data were acquired every 15 min and the climate of the site has a KG of Csa type. For the MASE calculation, m (the period in Eq.
Data sources A secondary data analysis was conducted on three nationally representative surveys from Burkina Faso. A before-after study design was used to compare socioeconomic inequalities among under-5 children with febrile illness seeking care before and after the implementation of the FHCP in Burkina Faso. Data were collected from the Burkina Faso 2010 Demographic and Health Survey (DHS) and the 2014 Malaria Indicator Surveys (MIS) before the implementation of the FHCP, and the 2017-18 MIS after the FHCP implementation. The DHS was only available for 2010; hence, the use of the MIS for 2014 and 2017-18 as the required indicators were available. All surveys used a stratified cluster sampling method for data collection. Overall, 547, 252, and 245 enumeration areas or clusters were selected for the 2010, 2014 and 2017-2018 surveys, respectively.
Limitations Despite rigorously controlling for several confounders, this study is not without limitations. First, data were obtained from surveys that employed a cross-sectional design which could have resulted in some information bias. Furthermore, factors that may affect care-seeking, such as the readiness of a health facility, severity of illness, and the differences in sociocultural conception of illness and its aetiology, were not considered. Another limitation is the fact that the wealth index is a proxy used in the DHS across many countries whilst the variables used in the Principal Component Analysis (PCA) do not really vary across studies over the years as the nature of household's assets evolve over time. Therefore, to effectively control and account for all determining factors, further complementary studies combining both qualitative and quantitative approaches are needed to conclude on possible causality.
We thank Dr. Ackley and Prof. Glymour for pointing out that data limitations could bias the results, which is important for proper interpretation of the results. Nonetheless, their statement that ". given that all of these biasing processes are likely,. Pang et al. analysis may be too optimistic. " is not an accurate characterization because some of the limitations could in fact lead to pessimistic estimates. For example, study patients included in our meta-analysis represent a heterogenous population. If some of these patients do not derive benefit by Aβ removal, the inclusion of them will dilute the average effect of Aβ removal. Informative loss to follow-up could bias the result in either direction depending on the nature of the loss to follow-up. Furthermore, the lack of standardization across Aβ PET radiotracers means there are measurement errors that are not accounted for in both the original BMJ publication and our updated meta-analysis.
As the main task of the instrumental variable analysis in essence is to regress the change in a clinical endpoint on the change in Aβ, ignoring  In summary, we acknowledge that the results from all meta-analyses are subject to uncertainty due to sampling error and other limitations from data and methodologies. Nonetheless, our updated meta-analysis provides meaningful improvement upon the original BMJ publication by correcting multiple data errors and incorporating additional data as they have become available. Our updated meta-analysis also appears to be in keeping with the recently disclosed topline results of lecanemab confirmatory phase 3 trial (CLARITY AD, NCT03887455), which met its primary endpoint and key secondary endpoints, showing a highly statistically significant reduction of clinical decline in a large study of participants with early Alzheimer's disease (AD).
Specific to publication bias, we believe that this is a limitation of meta-analyses in general. It is worth noting that the authors excluded the PRIME trial (aducanumab, NCT01677572) from their original BMJ publication despite the fact that (1) a paper on PRIME5 was cited in their references, which included all the necessary data for their instrumental variable analysis; and (2) the PRIME trial met all the inclusion/exclusion criteria for their study. 1 As shown in Figure SE. 2 of the supporting information of our updated meta-analysis, 2 the effect sizes after correction of data errors in the BMJ publication and addition of the PRIME trial only are 0. 095 (P = 0. 0046), 0. 3 (P = 0. 021), and 0. 12 (P = 0. 052) for Clinical Dementia Rating-Sum of Boxes, Alzheimer's Disease Assessment Scale-Cognitive subscale, and MMSE, respectively.
Methods This study uses two different study designs to examine the impact of a user fee removal policy on contraceptive use across a panel of 1400 households randomly selected across eight health districts. Data were collected using a standardized socio-demographic questionnaire at three different time points during the pilot and scale-up phases of the fee abolition program. The questionnaire was administered six months after the launch of the pilot fee abolition program in four health districts. For the remaining four health districts, the survey was conducted one year prior to and six months after the implementation of the program in those areas. All WRA in the households were eligible to participate. A cross-sectional study design was used to determine the association between knowledge of the fee abolition policy among WRA and actual use of contraceptives by WRA six months after the policy's implementation and across all eight districts.
Study design The study used data from three rounds of surveys that were originally planned for another study (SYNERGIE) and were conducted in July 2019 (round 1), February 2020 (round 2), and February 2021 (round 3). Rounds 1 and 3 surveyed the same set of households in four health districts that were not part of the pilot, while round 2 surveyed households in four health districts located in the pilot area, as presented in Figure  1. The three rounds followed the same sampling and survey procedures. This platform allowed the research question to be explored through two different study designs. The first is a cross-sectional assessment of the knowledge of the policy's existence among the targeted population and its association with the use of contraceptives by WRA, conducted six months after the policy was introduced.
Data from rounds 2 and 3 were used for this analysis. The second is a pre-post design with a non-randomized control group (ie, a quasi-experimental design) applied to evaluate the policy's effects on contraceptive use among WRA. This longitudinal design used data from rounds 1 and 3, which took place one year before and six months after the introduction of the new policy, respectively. This was a natural experiment; the study was not initially designed to assess the new policy's effects.
Survey procedures A standardized sociodemographic questionnaire was administered individually to all participants. Questions were extracted from the Demographic and Health Surveys instruments. The surveys gathered data on household features and the participants' main sociodemographic characteristics. A module of the questionnaire specifically targeted reproductive health indicators -notably, the participants' knowledge about the existence of the new policy, their current use of contraceptives, their sexual activity, their childbearing intentions and pregnancy history, and whether they were recently in contact with a health professional. Surveys were individually administered at the participants' homes by female research assistants with relevant experience and training in survey administration.
, 2018). Although the effect of R&D on the decoupling growth-CO 2 emissions phenomenon has been demonstrated  (Li & Jiang, 2020; Wang & Wang, 2019; Wang and Zhang, 2020) , empirical evidence showing the positive effect of innovation on the wasteincome nexus is scarce. To the best of our knowledge, the seminal contribution from  Gardiner and Hajek (2020)  is the only existing study that deals with that issue. The authors collected data on 284 European regions from 2000 to 2018 and assessed the causal relationship among R&D intensity, income, and MSW generation; heating energy, capital, labour, and urbanization are also included. Panel VECM and Granger causality results claimed evidence of a bidirectional causality among MSW generation and R&D intensity as well as between MSW and income. Moreover, they also considered MW landfilled and MW incinerated, and found that GDP positively drives the amount of waste incinerated, although the landfilling of waste displays a negative relationship with income.
Data collection To implement our causality model, we derived the following data for Korea: environmental innovation, per capita GDP, municipal waste for recovery operations (recycling), municipal waste for disposal operations (landfill), municipal waste for incineration, capital, labour force, and urbanization. Environmental innovation is proxied by the number of patents in environment-related technologies (climate change mitigation technologies related to waste management) and collected by the OECD Environment Statistics. 2  Per capita GDP is expressed in constant Local Currency Unit (LCU) and taken from the World Development Indicators database. 3 Data on municipal waste for recovery operations (recycling), municipal waste for disposal operations (landfill), municipal waste for incineration are all expressed in thousand tonnes and are derived from the OECD Environment Statistics.
As a proxy for urbanization, we use the urban population, expressed in percentage (%) of the total population. Similarly, capital is proxied by the GFCF and expressed in constant LCU. Employment is expressed in total numbers of employed persons, as in  Sari and Soytas (2004) ,  Shahbaz et al. (2013) ,  Younes et al. (2015) ,  Gardiner and Hajek (2017). All these three latter variables are taken from the World Development Indicators database. The data cover the period 1985-2016. Table  6  in Appendix summarizes the data information. 4.
ANNs results The time-series analysis was complemented by an ANNs algorithm. In this section, we test the previous results through an experiment in ML with the use of ANNs. We use the same dataset, implementing an automatic learning algorithm developed in the Oryx 2. 0. 8 protocol. Table  4  shows the construction of the ANNs. It comes with a big dataset. In fact, we construct a NN characterized by 36 inputs (GFCF, TD, RMW, LMW, IMW, and their mathematical transformations) and three targets, which represent the variables, in terms of variation (first differences), found to be significant in the theoretical literature (dRPCGDP, dEMP, dUP). In total, we have a combination of 513,000 data processing. The constructed NN entirely respects the principle of a Machine Deep Learning (MDL) model capable of processing automatic information without supervision even in the face of enormous datasets.
The total number of instances is 272. The number of training instances is 164 (60. 30%), the number of selection instances is 54 (19. 85%), the number of testing  instances is 54 (19. 85%), and the number of unused instances is 0 (0%). These results can be explained as follows: the training instances have designed the best process for the neural algorithm (Fig. 5 ). It has a design accepted 60 times out of 100, and its value is higher than a different algorithm (selection instance), and the same value as the testing instance. Thus, the algorithm suggests the construction of the first phase of NNs. Finally, after observing the behaviour of the data concerning the processing in ML, we can analyze the results of the ANNs using the Perform Input Selection process (Fig. 6 ). We exploit this ML process in a big dataset where inputs can be redundant, and it affects the error of the NN.
Concluding remarks and policy implications The aim of this paper was to investigate the causal relationships operating among innovation in environment-related technologies, per capita income, and three major waste disposal operations (landfill, recycling, and incineration) for Korea, a country characterized by a singular system of waste management and a declining trend of MSW. To do so, we collected data spanning the period 1985-2016 and incorporated urbanization and production factors (GFCF and employment) within our multivariate framework. Following  Magazzino et al. , (2021a, b, c, d, e) , we applied two independent empirical methodologies: a time-series Granger causality procedure on the frequency domain together with ANNs experiments. Above all, econometric results revealed no significant causal linkage amongst innovation in environment-related technologies and the three types of waste disposal operations in Korea.
Foreign productivity shocks reducing the probability of an export failure may increase imports enough to prompt the governments of importing countries to increase their tariffs. Thus, the true effect of tariffs on export failure may be biased by the confounding effect of productivity shocks. Tariff endogeneity has been addressed in studies about trade liberalization, including  Trefler (1993) ,  Goldberg and Pavcnik (2005) , and  Buono and Lalanne (2012) , but not in export and import duration studies. We address the effect of variable marginal costs and of tariff endogeneity on the duration of trade with a rich dataset covering 176 destinations and 235 agri-food products shipped by six large 1 Trade models allowing for heterogeneous firms provide valuable insights about how changes in trade costs can bring about the creation of new trade flows and the disappearance of old ones.
| Data sources The dataset covers 235 product categories over the 2005-2014 period. The products, defined at the six-digit HS level, can be classified into six broad groups: cereals, fruits and vegetables, meat, oilseeds, dairy products, and cocoa. Exports originate from six large exporting countries (Australia, Brazil, Canada, France, Germany, and the United States) and are shipped to 176 potential destinations. Using disaggregated data allows us to more accurately characterize the dynamics of trade because a successful export episode for one product can hide a multitude of failures when analyzing highly aggregated data. Bilateral import values X k ijt and tariffs T k ijt come from the website of World Integrated Trade Solutions (WITS). The Tariff Analysis Online tool from the World Trade Organization (WTO) was used to obtain tariffs that were not in WITS.
Most countries have an average agricultural tariff of 15 to 18%, but tariffs in excess of 100% are relatively common. Data series on languages, distances, colonies, and borders were downloaded from the GeoDist database of the Centre d' Etudes Prospectives et d'Informations Internationales (CEPII). The religious openness index data primarily came from the CIA World Factbook. The Britannica Book for 2013 was used to fill in missing observations. The religious openness index (ROI) is defined as in  Helble (2006) , ROI ij ¼ P i d ri P j d rj with i ≠ j, where d ri is equal to unity if each of the world's five main religions r are present in country i and zero otherwise. A higher ROI ij indicates that more religions are present in both countries. Real exchange rate volatility was constructed from the IMF's International Financial Statistics data on monthly nominal exchange rates and the Consumer Price Index (CPI).
ACKNOWLEDGMENTS This study has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 861932  (BatModel). The views expressed here are solely those of the authors' and may not in any circumstances be regarded as stating an official position of the  European Commission  (the funding agency). [Correction added on 10 June 2022, after first online publication: An acknowledgement statement has been added to this article. ].
Overview Understanding the agronomic and socio-economic background of the case study area and the potential for future SI required data from different domains. We combined different methods to integrate quantitative data on the potential of SI with qualitative data on the role of actors (triangulation;  (Olsen 2004 )) (Fig. 3 ). To understand actor interactions, we mapped and analyzed the stakeholder network (section Interviews). Following  Hersperger et al. (2010) , we use the term "actor" when addressing "the decisions of people and institutions that execute these actions" and, following  Spyra et al. (2018) , we use the term "stakeholder" when addressing those "having a particular interest as they represent a community or group interest". To get insight into the potential for and impact of SI, we collected and analyzed data on farm structure and land cover, and used workshops to highlight the challenges and desired future solutions to the case study area (section Workshops, Surveys).
4 ) that was informed by a decision of a farmer. We constructed BBNs for the fruit and dairy sectors separately, as stakeholders suggest different dynamics in the dairy and fruit supply chains. Linkages and conditional probability tables were further specified based on the stakeholder interaction (section Stakeholder interaction) and auxiliary data (Supplementary material 2). A preliminary version of the BBN was verified by two stakeholders who attended all workshops, and a local expert. This led to minor modifications of the quantification.
Parameterization of farmer's behavior and biophysical environment Farms and farmers: We synthesized different data sources to create a dataset with information on farm location, farm size, involvement in direct sales, presence/absence of small landscape elements, farmer age group, and the presence of a successor per individual farm of the case study area (Supplementary material, Table  S2a ). Farm addresses were coupled to a database with address coordinates  (Kadaster 2020). A farm's involvement in the direct sales was checked through a field  (Zethof 2018)  and Google Streetview inventory of direct sale outlets. Farm size (ha) was derived from the farm's basic payment (€260 per hectare) as recorded in the public Common Agricultural Policy database (Rijksdienst voor Ondernemend Nederland 2020).
Maps of small landscape elements were combined and overlayed with the parcel map (Supplementary material, Table  S2a ) in ArcGIS 10. 4, to identify whether each parcel contained, or was adjacent to, small landscape elements. Because no map of parcels belonging to a certain farm is publicly available, we assigned parcels to farms based on proximity and used the constructed parcel map to calculate the percentage of farms with small landscape elements. We fed the farm size as derived from the Common Agricultural Policy database into a mass allocation in ScapeToad Cartogram and overlayed this with the parcel map, to couple parcels to farms  (Zagaria et al. 2017). This was done separately for dairy and fruit farms, where orchards were coupled to fruit farms and grasslands and fodder parcels to dairy farms.
The farms that receive a payment for agrienvironmental measures were also classified as having small landscape elements. Farms who receive payment for young farmers were classified as farms with a young (<40 years) owner. Furthermore, the farmer age distribution in the dairy and fruit sector and the average percentage of farmers with a successor was derived from the Farm Accountancy Data Network database (European Commission 2018) for the case study area as a whole (Supplementary material, Table  S2a ). Farm location was classified as close to built-up areas, remote, or located at an intermediate location. This was derived from the land cover map (Supplementary material, Table  S2a ). The distance was classified using natural breaks. The uptake of efficiency (FoA2) measures was derived from the Farm Accountancy Data Network dataset for the Netherlands as a whole, for the farming types "specialist orchardsfruit" and "specialist milk".
The Farm Accountancy Data Network database (European Commission 2018) is the only accessible database that provides the required farm-level data, and was therefore used to calculate a proxy for the uptake of FoA2 SI measures. Based on  (Weltin et al. 2018) , we made a list of farm-level measures that affect resource use efficiency, knowledge management, and livestock fodder. We next checked the list of Farm Accountancy Data Network variables to find variables that directly provided indicators for the level of implementation of the SI measures, or could be used to calculate the level of implementation. This process was done by three experts individually and discussed afterwards, and resulted in a list of 12 measures (Supplementary material, Table  S2b ). After the measures were defined (Table  S1 ), the uptake at farm level was calculated in R, using the package gmodels.
Beyond intrinsic motivation, local conditions influence the uptake of SI measures: we found that farms with direct sales tend to be located closer to villages than farms without, which we parameterized as farms close to villages having a higher probability of taking up direct sales (see supplementary material 3). We also used farm size (in hectares) as an indicator for the likelihood of uptake of new measures, because the Farm Accountancy Data Network dataset shows that farmers who have SI measures in place tend to have larger farms (European Commission 2018). Furthermore, in the workshops, stakeholders indicated that particularly young farmers or farmers with a successor are the key innovators. We, therefore, assigned a higher probability of measure implementation to young farmers and farmers with a successor.