dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, the price of output (wheat in our example) and expenses on mineral fertilisers for each plot would be needed to fine-tune the shadow price computation and investigate alternative policy measures, such as a tax on mineral fertilisers. Further analyses could compare the yield loss incurred by farmers in the case of an extension of the Nitrates Directive and the case of a tax on fertilisers. Collecting additional economic information in the SAPM survey would be possible, as would matching SAPM data with economic databases such as the Farm Accountancy Data Network (FADN). In France, such matching is rarely carried out. It has, for example, been done to investigate pesticide reduction  (Jacquet et al. , 2011)  but remains to be implemented in the case of fertilisers. Overall, plot-level analysis can complement farm-level analysis, even more when information about the variation among plots within farms exists.
ACKNOWLEDGEMENTS The work is part of the LIFT (' Low-Input Farming and Territories-Integrating knowledge for improving ecosystem based farming ' )  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 770747. Remote access to the French plot-level data, provided by the  Ministry of Agriculture  and used in this work, was made possible within a secure environment offered by  CASD (Centre d 'accès sécurisé aux donnée, Ref. 10. 34724/CASD). Our thanks are due to anonymous reviewers for their constructive comments on an earlier draft.
DBES and LCA framework The COMFIE  [25]  model of the software Pleiades  [26]  is used to run the DBES. In this model, whose reliability has been validated  [27, 28] , the building is divided into thermal zones of homogeneous operative temperature before being meshed. A thermal balance is applied to each mesh to obtain the zone temperatures, as well as the heating and cooling loads. This provides a precise assessment of the energy consumptions during the building use phase  [6] , which is the life cycle phase having the highest share of impacts  [2]. Since climate and user behaviour have a strong influence on the energy performance  [29] , two additional models linked to COMFIE are used to generate a set of realistic meteorological years  [30]  and occupancy scenarios  [31]. The model EQUER  [32, 33]  of Pleiades  [26]  is used to run the LCA simulations, based on the DBES results.
g. lower (LB) and upper (UP) bounds) are given in Table  2  along with the data source for the uncertainty characterisation. 20 uncertain factors are continuous. The remaining four factors are categorical. One of them have two possible levels: the choice of the blowing agent for the polystyrene extrusion process. The three other categorical factors may have many-level. For the variability of climate, a set of 2,000 realistic climate files was generated with the model of Ligier et al. [30]. Similarly, a set 2,000 realistic occupancy scenarios was generated with the model of Vorger et al. [31]. Finally, a set of 2,000 background environmental data was generated using the uncertainty distributions provided by the ecoinvent environmental database  [46]. For the Sobol method, a Latin hypercube sampling of size 𝑁 = 1,000 was applied, allowing the 2,000 levels available to be used for the categorical inputs with many-level.
However, visually the ranking of MA-Morris is a little closer to the one of Sobol. This is confirmed by the results of the linear regressions performed: the R² is higher for MA-Morris (0. 971) than for 2LA-Morris (0. 947). The ranking similarities are quantified using the Kendall rank correlation 𝜏 in Table  4. 𝜏 is more adapted than the R² to quantify the extent to which the order of factors is respected between two datasets. A value of +1 for 𝜏 indicates that the ranking is the same for all factors, while a value of -1 indicates a perfect ranking inversion. Correlations are weak when the 𝜏 value is close to zero. In this study, 𝜏 being generally close to +1, there are overall strong positive correlations between Sobol and A-Morris. For almost all indicators, 𝜏 is higher for the correlations between MA-Morris and Sobol, indicating that the MA-Morris ranking is closer to the Sobol ranking, than the 2LA-Morris ranking.
During phase II, data are examined of whether they conform against the standards established during the phase I exercise. Thus, phase I plays a crucial role, as phase II performance will heavily depend on a successful phase I calibration analysis. Since phase I estimation requires the process to be stable, under the IC distribution, undetected violations (like parameter(s) shifts), will contaminate the estimation and misplace the control limits, risking the phase II performance. More limitations of phase I/II set-up have been reported in the literature. In certain applications, like in medical laboratory's quality monitoring, we need to have online inference from the start of the process, prohibiting any phase I offline training. In addition, in short production runs, the low volume of data will not permit the employment of a phase I exercise.
Namely, we will introduce the Predictive Ratio CUSUM (PRC) methodology, which will be general enough to host any (discrete or continuous) univariate distribution, that is a member of the regular exponential family. Being in the Bayesian arena, PRC will employ a power prior to allow the contribution of IC historical data (if available) and utilize any subjective (informative) prior information, but we will also provide the option to have a non-informative initial prior in absence of prior knowledge. The cumulative ratio test of competing predictive distributions (i. e. the core idea in PRC), can be used with any distribution, but it will be presented only within the regular exponential family, where conjugate priors will guarantee a closed-form mechanism that will be straightforward to apply in practice.
The vectors t h ðYÞ ¼ ða 0 n 0 , a 0 P n 0 l¼1 h 1 ðy l Þ, :::, a 0 P n 0 l¼1 h k ðy l ÞÞ and t n ðX n Þ ¼ ðn, P n j¼1 h 1 ðx j Þ, :::, P n j¼1 h k ðx j ÞÞ refer to the sufficient statistics of the power prior and the likelihood respectively. The normalizing constant, Kðs n Þ will be given by: Kðs n Þ ¼ ð H cðhÞ ½ s n, 0 exp X k i¼1 g i ðhÞs n, i ( ) dh < 1, [4] where H is the parameter space (for discrete h, we replace the integral sign by summation). For detailed proof of the posterior derivation, see  Bernardo and Smith (2000)  or  Bourazas, Kiagias, and Tsiamyrtzis (2022). It is of great importance to clarify that the null state (IC) is not fixed, but sequentially updated, every time a new data point arrives. Likewise, the alternative (OOC) scenario cannot be fixed, but it should be constructed sequentially and designed suitably in order to increase the detection power.
In (  5 ), if we replace the current posterior distribution, p 0 ðhjs n Þ, with the OOC posterior, p 0 ðhjs 0 n Þ, corresponding to the shifted parameter scenario, we will derive the shifted (OOC) predictive distribution: f 0 X nþ1 jX n ð Þ¼ ð H f X nþ1 jh ð Þp 0 hjs 0 n À Á dh ¼ K s 0 n þ t f ðX nþ1 Þ À Á K s 0 n ð Þ gðX nþ1 Þ [6] Thus, we have two competing states of the predictive distribution, with the currently available predictive (5) representing the IC state (i. e. no parameter shift) and its variant (6) corresponding to the OOC scenario, i. e. where the unknown parameter has been shifted. As a new data point x nþ1 arrives we need to weigh the two competing predictives using an appropriate function. The proposed PRC is based on the sequential comparison (via their ratio) between the current predictive distribution f ðX nþ1 jX n Þ, which includes all the relevant information from the process up to the current time, and the corresponding shifted predictive, f 0 ðX nþ1 jX n Þ, representing the OOC shifted parameter scenario.
PRC inference The control chart associated with PRC has the familiar form of a CUSUM, where the monitoring statistic S nþ1 (from either (8) or (  9 )) is plotted versus time with a horizontal decision limit h, that acts as an upper/lower control limit in detecting upward/ downward shifts. Issues regarding the design of PRC, like the derivation of h based on false alarm tolerance, along with robustness properties and its illustration in real data applications are covered in  Bourazas, Sobas, and Tsiamyrtzis (2023). The area between the horizontal axis and h is considered the IC region, so when S nþ1 plots beyond the control limit h, then we raise an alarm and our suggestion is to stop the process and examine for an assignable cause, triggering a potential corrective action. From a root cause analysis point of view, a CUSUM alarm will indicate not only that the IC state has been rejected, but it will also offer an estimate of the time where the OOC state was initiated, which is simply the first observation A parallelogram corresponds to an input/output information, a decision is represented by a rhombus and a rectangle denotes an operation after a decision making.
In addition, the rounded rectangles indicate the beginning and end of the process. ★ For the likelihoods with two unknown parameters and total prior ignorance (i. e. initial reference prior and a 0 ¼ 0 in the power prior) we need n ¼ 3 to initiate PRC, while for all other cases, PRC starts right after x 1 becomes available. right after the latest time for which we had S nþ1 ¼ 0: Once we correct the problem, then PRC is suggested to be reinitiated, using all past IC recordings as historical data in the power prior. If we will not react to an alarm, then due to the dynamic update of PRC, OOC data will be involved in the learning process, affecting what is considered IC state. As a result, the monitoring statistic will start moving back to the IC region. This is a well known issue for the self-starting methods, reported in the literature as "window of opportunity" for a control chart to alarm, before the running statistic stops to alarm (in contrast to the fixed parameter CUSUM, where there is no updating and so an alarm will tend to persist).
Data collection Our primary data was collected through in-depth, qualitative interviews with various actors from the focal firms across two stages. We used a broad thematic interview guide (see Appendix) and semistructured interview format to give participants the freedom to explore their use of MSPs and its consequences from their subjective perspectives  (Creswell & Poth, 2016). The interviews were conducted via Microsoft Teams, lasted around 60 min each, and were fully transcribed. The first stage includes 15 exploratory interviews with key actors from each firm to understand the relationships between various actors and identify how MSPs are used to connect various firms. These interviews were conducted between January 2021 and June 2021. In the first stage, we conducted 11 interviews with participants from the focal supplier firm, one with a participant from the focal reseller firm, and three with participants from the three different installer firms (one from each).
Data analysis Our data analysis proceeded in three key stages. In the first stage, we analyzed our primary and secondary data to identify all the MSPs adopted by the firms in the focal business network. This revealed a total of 15 different MSPs operating in the network. We then categorized these MSPs into three types: i) transaction, ii) non-transaction, and iii) hybrid, in accordance with the categories in the extant MSP literature  (Trabucchi & Buganza, 2021). We then analyzed our primary and secondary data again to identify and capture how each actor had adopted each MSP, and what that decision had meant for the actor's role in the MSP's ecosystem. We subsequently assigned MSP roles to actors for each MSP and aggregated the findings across the three MSP categories. Table  3  provides an overview of the different MSPs identified in our study, and describes the role assumed by each actor, depending on the actor's decision to build (orchestrate) or join (complement or use) the MSP.
Note that each actor in our study adopted multiple MSPs and assumed various roles. In the second stage, we applied inductive coding strategy to analyze our interview data  (Gioia, Corley, & Hamilton, 2012). We started by open-coding the data, following an incident-to-incident approach  (Charmaz, 2006) , where we treated any information on a firm's MSP adoption strategies as an incident. This revealed several reasons why firms adopted MSPs (first-order codes), which we clustered into thematically similar groups that described the 'goals of MSP adoption by firms' (second-order themes). During this stage, we iterated between our empirical data and the wider network management literature to understand how these intentions could be linked to network management strategies. At the end of this process, we identified three higher-order categories that described network management strategies used by firms when adopting MSPs (network shaping, optimization, and expansion).
Fig. 2  provides an overview of our coding structure and empirical findings as they related to the emerging network management-driven MSP adoption strategies. In the final stage of our analysis, we revisited our interview data to identify any information that described the consequences of MSP adoption for various network actors. We clustered the consequences into thematically similar higher-order categories and divided them based on whether, according to the participants, the consequences were intentional or unintentional. We then cross-analyzed the data to compare how the consequences changed when a firm assumed different roles (orchestrator, complementor, user) based on its MSP adoption strategies. This helped us capture a holistic understanding of the different consequences of MSP adoption for firms in different roles.
Stroop stimuli Based on  Aïte et al. (2016) , 16 color-word stimuli were created by combining 4 different color names  ('red', 'green', 'blue', and 'yellow')  with 4 corresponding ink colors (RGB color codes 255;0;0, 0;255;0, 0;0;255 and 255;255;0). We used these stimuli to create 64 congruent and 64 incongruent Stroop experimental trials. Before the main experiment, participants were presented with a set of practice trials (Section 2. 1. 4. 2). For the color practice, 4 circle stimuli were created each filled with either red, green, blue, or yellow ink (RGB color codes 255;0;0, 0;255;0, 0;0;255 and 255;255;0). All stimuli were presented in the center of the screen on a gray background (RGB code 135; 135; 135) in randomized order. Participants were instructed to press the key 'd' if the word was presented in the color red, the key 'f' if it was presented in blue, the key 'j' if it was presented in green, and the key 'k' if it was presented in yellow (we chose these 4 response keys as they have the same position in the 3 most common keyboard layouts: QWERTY, QWERTZ, and AZERTY).
Participants We recruited our participants online on Prolific Academic (www. prolific. ac). Only native English speakers from Canada, Australia, New Zealand, the United States of America, or the United Kingdom were allowed to take part in the study. Participants were paid £4. 50 for their participation (£5 hourly rate). Based on  Raoelison et al. (2020) , Study 2) correlational two-response study, we aimed to recruit 160 participants. Due to a software error, the Reasoning task data of one participant could not be recovered, so we ended up with 159 participants (69. 2% female), with a mean age of 33. 1 years (SD = 13. 6). This allowed us to pick up small to medium-size correlations  (. 22 ) between the Stroop and Reasoning task performance with a power of 80%. The majority of participants (45%) had a highschool degree as their highest education level, 37% had a bachelor's degree, 15% had a Master's degree, and 3% had not completed high school.
Materials The Stroop task was run on Gorilla Experiment Builder (gorilla. sc) and the Reasoning task was run on the Qualtrics (www. qualtrics. com) software server. We first ran an initial batch of 10 participants that was identical to the main study. This was done to ensure that no technical problems would occur during the transition from Gorilla Experiment Builder to the Qualtrics platform. Data from one participant of this first batch could not be analyzed (see above). We then ran the main study batch, which consisted of the remaining 150 participants. The Color-Word Stroop task that was used in this study was identical to the Stroop task described in Study 1. The Reasoning task included 3 different types of reasoning problems (i. e. , bat-and-ball problems, base-rate problems, and syllogistic reasoning problems).
Background: Drug-related problems (DRPs) can lead to serious health issues and have significant economic impacts on healthcare systems. One solution to address this issue is the use of computerized physician order entry systems (CPOE), which can help prevent DRPs by reducing the risk of medication errors. Objective: The purpose of this study is to provide an analysis on scientific production of the past 20 years in order to describe trends in academic publishing on CPOE and to identify the major topics as well as the predominant actors ( journals, countries) involved in this field. Methods: A PubMed search was carried out to extract articles related to computerized provider order entry during the period January 1st 2003-December 31st 2022 using a specific query. Data were downloaded from PubMed in Extensible Markup Language (XML) and were processed through a dedicated parser.
Bibliographic research This study utilized the MEDLINE bibliographic database, which contains a vast collection of scientific articles from the biomedical field spanning several decades. The database's search engine, PubMed, enables the use of MeSH thesaurus keywords for precise indexing of articles, and it also permits searching for terms in unstructured fields such as article titles and abstracts. Additionally, the Booleancompatible syntax, featuring the use of operators such as AND, OR, and NOT, provides exceptional flexibility for conducting queries. The following query was constructed with the help of a medical librarian (EL) to select articles indexed with the keywords "Computerized Physician Order Entry" (MeSH identifier D050316) between 2003 and 2022 (a period of 20 completed years).
To increase recall, the terms "CPOE", "Computerized Physician Order Entry", "computerized physician order entry system", "computerized Provider Order Entry", "Computerized Provider Order Entry System", "prescription tool", "prescription support tool", and "medication alert system" were searched in the title. In order to minimize noise (false positives), some terms were excluded such as "laboratory test" or "laboratory". The final equation is as follows: "medical order entry systems"[MH] OR "medical order entry system*"[TW] OR CPOE[TW] OR "Computerized Physician Order Entry"[TW] OR "computerized physician order entry system"[TW] OR "computerized Provider Order Entry"[TW] OR "Computerized Provider Order Entry System"[TW] OR "prescription tool*"[TW] OR "prescription support tool*"[TW] OR "medication alert system*"[TW] NOT "laboratory test*"[TW] NOT "laboratory"  [TW]  Extraction and processing of data The bibliographic references retrieved from PubMed through the query are in XML (eXtensible Markup Language) format.
A specialized program is used to extract relevant data from selected metadata, including the year of publication, author names and affiliations, journal, MeSH keywords, publication type, and language. Two additional metadata were automatically added using specialized algorithms. First, the country of publication was inferred from the affiliation of the first author, which is typically the most informed and representative of the article's content. A computer program utilizing the Google Maps API and a dedicated database was used to identify the country from the author's email domain or location. Second, medical specialties were assigned to each reference based on the MeSH keywords using a categorization algorithm developed previously  (9). The algorithm leverages the MeSH hierarchy and relations defined by domain experts to associate each keyword with one or more medical specialties  (10).
We conducted a human verification of a random sample of 100 articles related to CPOE (evaluation, design, and use of CPOE) by two individuals (LG and JG). This verification involved assessing the titles and abstracts of the articles to ensure that the retrieved results are relevant to CPOE. Further information on the yearly publication count in MEDLINE was directly obtained from PubMed. Moreover, the total number of publications from these journals during the study period was compiled in the data table sourced from PubMed. Population data, including population size and Gross Domestic Product (GDP) per capita by country, were sourced from the World Population Review (http:// worldpopulationreview. com) and the World Bank (https://data. worldbank. org/indicator). Bradford's law was used to rank the journals.
The European Farm Accountancy Data Network (FADN) provides such a database. Until recently, the focus of FADN has been on the economics of agriculture, with little capacity to develop environmental indicators  (Kelly et al. , 2018). However, when utilised in conjunction with innovative methodologies, it is possible to provide valuable information about environmental aspects of farming systems for policymakers. This paper tackles these issues by using the concept of 'technological heterogeneity', which takes both farming intensity and geographical location into account in the analysis of farm performance. Importantly, this paper provides a blueprint for the replication of the approach using the widely available FADN dataset. Specifically, we develop a model to distinguish between intensive and extensive dairy farms and assess their performance in a cross-country context.
Specifically, we analyse specialist dairy farms in three EU countries that are important for European dairy production, namely Ireland, France and Austria, using FADN data from 2015 to 2018. In this context, we provide further findings for the empirical literature on the analysis of extensive technologies in dairy farming; these relate to separating variables for latent intensive and extensive technologies in dairy farming, productivity and efficiency, as well as drivers of inefficiency. Our final contribution lies in the description of how our novel metafrontier approach can be used to design future green payments within the CAP and, in particular, the minimum threshold required to receive such payments. The approach particularly addresses the need for a pragmatic, yet consistent way of identifying extensive and intensive production technologies, based on data sources that are readily available across member states, namely FADN and IACS.
The proposed approach can also be easily amended to use other indicators for identifying extensive technologies, as soon as better environmental indicators become available in the near future, for example via the Farm Sustainability Data Network. More generally, the approach developed can be extended to other settings of firm performance analysis, where technological heterogeneity is also assumed to arise through two distinct channels. Our findings indicate worse environmental but better economic performance of intensive farms, compared to extensive farms in dairy farming in the three countries considered, Ireland, France and Austria, over the period 2015-2018. Against the background of the EU's Green Deal objectives and the resulting implications for the farming sector, we thus provide further evidence that productivity losses need to be expected along the way.