dataesr/scientific-paragraphs-categorization huggingface dataset:
We assessed and described the characteristics of the defined zones in terms of the grapevine yield gap (as the difference between the average grapevine yield and the label production limit; i. e. , 90 hl•ha -1 •year -1 ). For every zone, we calculated the selected indicators in Section 2. 2, the distribution of grapevine varieties, the number of municipalities and the total grapevine cultivated area. Tukey's range test was then used to compare the average values between zones using 'multcomp' R package  (Hothorn et al. , 2009). Anova test (R Core Team. , 2014) was used to estimate the variability of the grapevine yield distribution explained by the zone classification grapevine yield, and to compare it to the unexplained variability inside each zone. Chi-Square test of goodness of fit using 'summarytools' R package  (Comtois, 2021)  was used to analyse the proportions of grapevine varieties in each zone (with a significance level alpha = 0.
Descriptive statistics of grapevine yield data In our database, the vineyard cultivated area and wine volume as declared by the Pays d'Oc quality label were, on average, 73718 ha•year -1 and 5 950 302 hl•year -1. However, we aggregated individual yields weighted by their area at the municipality level, obtaining an average yield per municipality and year. Yield values for all the municipalities were as follows: a mean of 65. 3 hl•ha -1 •year -1 , a median of 67. 03 hl•ha -1 •year -1 , a minimum of 6 hl•ha 1 •year -1 , and a maximum of 100 hl•ha -1 •year -1 (the latter corresponding to a municipality with rosé wines -permitted maximum yield being 100 hl•ha -1 •year -1 ). If the yield gap was filled, the extra production expected could be 684 318 hl•year -1 (i. e. , over 11. 5 % of the current wine volume) with regards the label's maximum yield requirements for red and white wines (90 hl•ha -1 •year -1 ).
Limitations of climate and soil data and selected indicators Soil data availability at the landscape scale is often the bottleneck when zoning terroir and in grapevine production  (White, 2020) ; in particular, the lack of an SAWC map and measurements constrains many classifications, such as that of the Italian Chianti, as suggested by  Priori et al. (2019). As a result of the GlobalSoilMap initiative, new soil databases are available at the regional scale for Languedoc-Roussillon  (Vaysse and Lagacherie, 2015). In this study, we showed that not only climate, but also soil variables (SAWC and soil pH) are relevant when studying regional grapevine yield gaps. However, GlobalSoilMap products are less useful for doing these analyses on more local scales. Although the SAWC map explained a small amount of the total SAWC variance (20 % according to  Styc and Lagacherie (2021) , the aggregation of SAWC at the municipality level reduced this variance, as observed by  Vaysse and Lagacherie (2017).
The use of SAFRAN climate data raises a number of questions. On the one hand, the 8 km by 8 km resolution reduced out the local variability of the climate, which can sometimes be very important in magnitude over small distances and participate in a significant way to the heterogeneity of the vineyard environment  (Le Roux et al. , 2017). On the other hand, SAFRAN is a reanalysis, which necessarily produces biases, especially in areas where relief is more pronounced  (Quintana-Segui et al. , 2008). SAFRAN tends to minimise extreme temperatures  (Ollat et al. , 2021) , thus decreasing the number of Very Hot Days and Days of Frost. Because of the scale used, we could not consider certain local phenomena such as hail, that can drastically damage grapevine yield  (González-Fernández et al. , 2020).
CONCLUSION The present study proposes a method for selecting theoretical climate and soil factors that may have significant influence on grapevine yield at the municipality level. By working to such a scale, it is possible to gain more knowledge about winegrowing landscape characteristics that could contribute to future studies on vineyard management practices. Our analysis evidenced 6 relevant factors that explained grapevine yield at R 2 = 0. 546, thus explaining only part of the grapevine yield. Further research should consider longer yield and the time-span of the climate database (e. g. , 30 years) to improve the accuracy of the indicator selection. We opted to perform clustering to help analyse the types of municipality that have similar characteristics in terms of soil and climate. The choice to apply this zoning approach was also motivated by the fact that it provides a basis for formulating R&D recommendations.
ACKNOWLEDGEMENTS We thank all the  Pays d'Oc PGI  collaborators in this project, who provided funding, and shared data and expertise, in particular  Laure Lacombe ,  Florence Barthès ,  Olivier Simonou ,  Jacques Gravegeal ,  Damien Onorré  and  Gérard Bancillon. We highly appreciated the support of the  Wine and Vine chair of the University of Montpellier ,  INRAE  and  Institut Agro  for facilitating our partnership with the private sector and scientific exchanges.
The communication concept Besides the data and models that are openly available on a public Gitlab repository (https://gitlab. seismo. ethz. ch/efehr), further products were needed for the public release of the model (e. g. , maps; Figure  5 ). To this end, the model developers at the EUCENTRE and the Swiss Seismological Service (SED) at ETH Zurich, and the communication team at SED joined forces and developed products tailored to the target audiences' needs and knowledge; thus, this core team was responsible for the corporate communication  (Christensen and Cornelissen, 2013)  by managing all communications related to the ESHM20 and ESRM20. The basis of these efforts was the elaborated communication concept, which follows the theoretical frameworks of  Zerfaß and Piwinger (2014). In this concept, we defined the communication strategy, the realisation plan, and the controlling and More precisely, the communication concept defined the overall vision, the communication principles and goals, the target audiences, the products, and the dissemination channels and activities.
Detailed (technical) report A long report which contains various contextual information about seismic hazard and seismic risks in Europe, describes the models and the datasets used to build those models as well as the methods (technical descriptions). Furthermore, the main results are presented. -Modellers and researchers -Scientific community https://doi. org/10. 5194/nhess-2023-107 Preprint. Discussion started: 5 July 2023 c Author(s) 2023. CC BY 4. 0 License.
Web viewer of the risk model In December 2020, we conducted an interactive online survey with 17 professional users of the risk web viewer (Figure  7 ), representing researchers, civil engineers, cat risk modellers, and civil protection (Fig. S1 ). The aim of the survey was to assess the user needs with respect to the information presented on the web viewer; i. e. its relevance, usability, understandability, userfriendliness, and completeness. were in line with the actual content. In QB2, we assessed participants' correct interpretation of the information provided, map and layer preferences, and additional information needs. In QB3, we let them evaluate the design, and, in QB4, they had the chance to provide final comments. The entire questionnaire is listed in Supplement S1. The survey was set up with Unipark at ETH Zurich, and the results were statistically analysed with the Software SPSS.
Risk poster and risk map We conducted an online survey with 83 students across Europe to test the risk poster in July 2021. The aims were to assess i) if the communication goals are reached with the information on the poster; ii) if the risk information on the poster is understood correctly, perceived as useful and well designed, and complete; and iii) what influence the design (colour-scale) and participants' characteristics have on these factors. The survey was set up with Unipark at ETH Zurich and analysed with the Software SPSS. We developed three versions of the risk map with three different colour-scales (see Figure  8 ). We thus tested three versions of the risk poster by replacing the map in the middle (Table  S8 ), whereas all the other poster elements stayed the same. To test which version works best, we conducted a between-subject experiment, thus participants were randomly assigned to one of the three poster versions but responded to the same questions.
Acknowledgements The authors would like to thank the EFEHR ambassadors ( Josip Atalić ,  Philippe Gueguen ,  Gottfried Grünthal ,  Kyriazis Pitilakis ,  Vassilis Karastathis ,  Benedikt Halldórsson ,  Sevgi Ozcebe ,  Radmila Salic Makreska ,  Mathilde B. Sørensen ,  Radu Vacareanu , and  Constantin Ionescu ) for supporting the outreach of the communication products and release of the models. Further, we thank all participants for filling in the survey on the interactive web viewer or the risk poster. The research and efforts described herein has received European funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements No  730900  ( Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe  ' SERA ' project), No  821115  ( Real-time Earthquake Risk Reduction for a Resilient Europe 'RISE' project ), and No  101021746  ( sCience and human factOr for Resilient sociEty 'CORE ' project).
7, S2. 8 Resolution preferences -Gridded map (e. g. , 1km x 1km) > National level > NUTS19 -Resolution: countries, cities and municipalities -The risk results for all levels of resolution should be included in the same map viewer so that all information is together. S2. 7 Layer preferences -Populated places / density of the population -Significant earthquakes (according to the NCEI WDS database) 🡪 By clicking on the event, detailed information about an event should pop up. -Active/major faults -Return periods: 50, 200 and 500 years -Relevant infrastructure and lifelines -Shaded relief not desired S2. 9 Perceived purpose of the risk model -To give estimates of risk levels at various return periods of the mapped economic exposure. -To provide an overall view of seismic risk in Europe and to compare seismic risk in the different EU countries.
Methods A systematic review of CVD prevention guidelines was conducted between September 2017 and March 2023 using the Turning Research Into Practice medical database, the Guidelines International Network, and a purposive search. The ADAPTE procedure was followed. Two researchers independently conducted the searches and appraisals. Guidelines published after 01/01/2012 addressing non-pharmacological, dietary interventions for primary CVD prevention or CVD risk factor management, in the adult general population in primary care or in community settings were included and appraised using the Appraisal of Guidelines Research and Evaluation II score. Individual dietary recommendations and the studies supporting them were extracted. Then supporting data about each specific dietary intervention were extracted into a matrix.
Guideline, recommendation, and study selection Two data collection phases were performed. During the first phase, the two researchers independently selected the guidelines. In the second phase, guidelines were evaluated according to the "Appraisal of Guidelines Research and Evaluation II" (AGREE II) tool integrated in the ADAPTE procedure  [10]. The AGREE II tool consists of 23 items arranged into six domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. The two researchers independently scored each guideline by domain. An overall assessment was then performed and ranged from 1 (strongly disagree) to 7 (strongly agree). This score was independent from the other domain scores. A scientific committee consisting of three members was created to select guidelines based on the overall assessment scores.
Data extraction and synthesis For each included guideline, publication year or year of latest update, country, developing organization, language and title were extracted. From each included guideline a list of all dietary recommendations was created to provide an overview of relevant content. Recommendation themes were created inductively by grouping similar topic recommendations. The strength of recommendation, level of evidence, intervention description, outcomes, implementation strategies and evidence gaps were extracted from each recommendation if reported. The recommendations and studies they cited were then compiled into two matrices. After reading the full text, study title, implementation or intervention description, intervention frequency and duration, setting, material used, psychological model used where applicable, mass media use, and delivered intervention status were extracted into the matrices.
Strengths and limitations One study strength was using international clinical guidelines as information sources with searches being conducted on specific guideline databases, TRIP and G-I-N. However, these databases were incomplete, meaning other databases and a purposive search were required to complete the selection process and find the missing guidelines. The selection process was rigorous due to the two double-blind phases and the involvement of a scientific committee. Information bias was controlled through evaluation triangulation. It was assumed that selection and publication biases were previously addressed by each guideline provider. Assessing each guideline with the AGREE II tool meant that the researchers expected to select guidelines with the most reliable bibliographies and intervention implementation information could be captured.
Acknowledgements The authors would like to acknowledge the SPICES Team:  Pr Paul Van Royen ,  Pr Harm van Marwijk ,  Dr Linda Gibson ,  Dr Bowyer Mark ,  Pr Tholene Sodi ,  Pr Mbuyiselo Douglas. And the EA 7479 SPURBO team for the systematic review:  Lucie Morvan ,  Anne Catherine Lecuyer ,  Christelle Le Gaffric ,  Hélène Roux ,  Marion Janyk ,  Nicolas Vimfles ,  Marion Le Bars ,  Pol-Maël Falhon ,  Louis Soulier ,  Cécile Guérin ,  Marine Réale ,  Hugo Vittori ,  Charlotte Donou ,  Eloïse Lissillour ,  Thomas Le Bras ,  Gabriel Eliot. The authors thank  Charlotte Wright  and  Amy Whereat  of Speak the  Speech Consulting  for their assistance editing and reviewing the manuscript. This article is supported by the  French network of University Hospitals HUGO ('Hôpitaux Universitaires du Grand Ouest ').
Funding This work was funded by the  European Commission , as part of a  Horizon 2020  project grant for the  SPICES  project. Project ID:  733356 , Funded under:  H2020-EU. 3. 1. 6-Health care provision and integrated care. Funding bodies had no role in the study design of the study; the collection, analysis and interpretation of the data; nor the decision to approve publication of the final manuscript.
), International Dispute Resolution: Towards an International Arbitration Culture (ICCA Congress Series, no. 8) (Kluwer, 1998). Cf. M. Sornarajah, The International Law on Foreign Investment, 4 th ed. (Cambridge University Press, 2017), 61-7 (highlighting the Western bias of investment arbitration); A. Roberts, Is International Law International?  (Oxford University Press, 2017) (using empirical data to challenge that international law today is 'universal' and to show how Western academic approaches have had a disproportionate influence in defining what counts as the 'international'). 8 H. P. Glenn, Legal Traditions of the World: Sustainable Diversity in Law, 5 th ed. (Oxford University Press, 2014), 28. 9 W. W. Park, 'A Fair Fight: Professional Guidelines in International Arbitration', Arbitration International, 30  (2014), 409.
To find answers to our research questions, we use the qualitive approach of semi-structured interviews. Based on the data collected from health workers (specialist doctors and nurses) of public and private sectors, we compare the PM systems implemented in UAE and KSA. Further, we look at the differences of PM in the Middle East in comparison to the PM in healthcare in other parts of the world. In the next sections, we elaborate on past literature and research, both specific to PM in healthcare sector and generally in other sectors. We then move to the methodology adopted in this study and elaborate on the qualitative method of research with semi-structured interviews carried out in the UAE and KSA and further conduct thematic analysis to highlight emerging themes. In the sections to follow, we discuss our findings in detail and provide answers to our research questions and list out implications for practice with future research directions.
We managed to conduct 10 interviews (6 in Saudi Arabia and 4 in the UAE) and saw a repetitive pattern in the answers obtained and data saturation was almost reached by the end of tenth interview. The interview questionnaire was designed based on the three research questions put together by the authors. This questionnaire is presented in Appendix 1. The interviews with our respondents were conducted virtually through zoom application by one of the authors and was recorded with consent from respondents for transcription and analysis by other authors. Upon completion of the interview, the transcription of the recording was immediately completed by using an online software called Trint. The author conducting the interview was not involved in the analysis phase in order to eliminate any bias. Thematic analysis was used for the analysis of this qualitative data.
, 2021; Bueff et al. , 2022)  and fairness  (Kozodoi et al. , 2022)  for credit scoring models. These previous studies analyze accepted credit applications and assess the fairness of credit default. As most of the literature studies the customer classification based on default records, however, to the best of our knowledge, no analysis has previously focused on fairness for access to finance for SMEs. This gap in the literature is mainly due to the lack of data availability on the outcomes of SMEs' credit applications. To fill this gap, we analyze the SME Finance Monitor survey, the largest survey in the UK on finance for SMEs. This survey provides data not only on accepted applicants but also on those that have been rejected. Explainable Artificial Intelligence (XAI) methods have been developed to open up black-box models and provide insights into the mechanisms of ML algorithms.
However, a limitation of the classical Shapley value-based explanation methods is that its calculation involves feature value combinations that may be infeasible, known as the extrapolation issue  (Rudin, 2019; Kumar et al. , 2020; Hooker et al. , 2021; Mase et al. , 2022). In this analysis, we consider the Cohort Shapley value proposed by  Mase et al. (2019 Mase et al. ( , 2022) )  to address this issue. As the survey data are weighted to make the observations representative of the population, we further suggest a Weighted average of the Cohort Shapley (WCS) value. Furthermore, we propose a Relative Fairness Score (RFS) as a function of the WCS values. We apply our proposals to data collected by the SME Finance Monitor survey, which represents the largest survey on SME finance in the UK. We consider 1,102 SMEs that sought a bank loan in the last 12 months from the second quarter 2018 to the third quarter 2020.
There is an extended literature on the disadvantages faced by companies whose owners are from ethnic backgrounds  (Cowling et al. , 2023; Fraser, 2009; Smallbone et al. , 2003). We obtain that owners with a Asian background are significantly discriminated against, while the Black ethnic group is highly advantaged in the credit decision process. The paper is organized as follows. Section 2 illustrates the methodology on the Cohort Shapley value. Section 3 describes SME data set, and Section 4 provides the main empirical results. Finally, Section 5 presents the conclusions.
( 5 ) A positive value of Cohort Shapley 𝜙 𝐶𝑆 𝑡,𝑗 implies that the corresponding feature 𝑗 has a positive impact on the outcome 𝑦, while a negative value implies a negative impact. Notably, traditional explainable methods such as SHAP necessitate the existence of an ML model, for example, computing Eq. (  2 ) using ŷ𝑖 = 𝑓 (𝐱 𝑖 ). In contrast, by Eq. (  5 ), Cohort Shapley values can be computed from the observed response values {𝑦 𝑖 } 𝑛 𝑖=1 , enabling its direct application to a provided dataset even without a built ML model. We apply the Cohort Shapley value to the observed outcomes of the credit applications and not to the ML techniques as the survey data are provided by several UK financial institutions that adopt different ML techniques. Furthermore, using real observations can avoid that the results are dependent on the particular ML technique  (Rudin, 2019).
A relative fairness score Each categorical feature 𝑗 may divide the population into several groups, to summarize the degree of fairness for the 𝑘th group with respect to a feature 𝑗, we calculate the Weighted average 𝑊 𝐶𝑆 𝑘,𝑗 of the Cohort Shapley values 𝜙 𝐶𝑆 𝑡,𝑗 for all observations in the group: 𝑊 𝐶𝑆 𝑘,𝑗 = ∑ 𝑖∈group𝑘 𝑤 𝑖 𝜙 𝐶𝑆 𝑖,𝑗 ∑ 𝑖∈group𝑘 𝑤 𝑖 , ( 6 ) where 𝑤 𝑖 is the survey weight associated with each observation in the SMEs Finance Monitor survey analyzed in this paper. 𝑊 𝐶𝑆 𝑘,𝑗 < 0 indicates that group 𝑘 is, on average, disadvantaged in the system, while 𝑊 𝐶𝑆 𝑘,𝑗 > 0 implies an advantage. To further gauge the severity of the disadvantage, we normalize the weighted averages by the minimum and maximum values that a group could obtain given the dataset. For a disadvantaged group, we create a biased system where observations in that group receive no financial support, while all observations in the privileged group receive financial support.
Data We analyze data gathered from the SME Finance Monitor survey over the period from the second quarter 2018 to the third quarter 2020. This is the largest survey on SME finance in the UK, with 4,500 companies interviewed every quarter. Coherently with the criteria established by the European Commission, SMEs are eligible for the SME Finance Monitor interview if they meet the following conditions: (i) not 50%+ owned by another company; (ii) not run as a social enterprise or as a not-for-profit organization; (iii) turnover of less than £25 m and (iv) number of employees lower than 250. Quotas are set by size, sector and region to have a representative sample. Initially, we consider only the SMEs that sought a bank loan in the last 12 months, given by 1,620 companies. We exclude records with missing or unknown values, resulting in a dataset with 𝑛 = 1, 102 SMEs.
Empirical results To evaluate the fairness of funding allocation to SMEs, we consider some firms' characteristics based on the literature on access to finance for SMEs  (Cowling et al. , 2023; Hewa-Wellalage et al. , 2022; Department for Business, Innovation and Skills, 2014). Particularly, we examine ethnicity, start-up, women leading, and enterprise size following the classification of the European Commission 1 based on turnover and number of employees. When computing the Cohort Shapley values, we use the given observations directly and no ML model is built, i. e. , use 𝑦 𝑖 in Eq. (  5 ), as the several financial institutions represented in the survey data adopt different ML techniques. Figs. 1-5 display the distributions of the Cohort Shapley values for gender, ethnicity, number of employees, turnover and firms' age, respectively, while Fig.