dataesr/scientific-paragraphs-categorization huggingface dataset:
With the development of the novel machine learning model, the Expanded Framework of Group Method of Data Handling (EFGMDH), a significant advancement in flood modeling for the Ottawa River is marked. Its primary objective is to provide decisionmakers with explicit equations for estimating three crucial hydrodynamic characteristics: floodplain width, flow velocity, and river flow depth. The EFGMDH model relies on a comprehensive dataset generated from the numerical model, encompassing a wide range of potential future floods to achieve accurate predictions. The model takes into account several inputs, including the location of the desired cross-section, river slope, Manning roughness coefficients for different river sections (right, left, and middle), and river flow discharge. Various input combinations are rigorously tested and assessed to establish practical models for each hydrodynamic characteristic.
The goal is to identify the most optimal inputs that yield precise estimations. Figure  3  provides a detailed overview of the input combinations. An essential aspect of the model development process is the division of the dataset into training and testing sets for validation. In this study, 70% of all samples, amounting to 397 samples, are randomly selected to form the training dataset. These samples are used to train the machine learning model, enabling it to learn from the data and establish explicit equations for estimating the hydrodynamic characteristics. The remaining 30% of samples, totaling 170 samples, are set aside as the testing dataset. These samples are used to validate the model's performance on unseen data, serving as a measure of its generalization capability. By evaluating the model's predictions on this independent testing dataset, one can assess the model's accuracy and reliability when confronted with new or previously unseen scenarios.
Conclusions This study has delved into the significant impact of climate change on flooding events within the Ottawa River Watershed. Notably, the occurrence of unprecedented floods, exemplified by the 2019 event surpassing a 100-year flood magnitude, underscores the urgency of developing effective strategies. Addressing this challenge necessitated the integration of advanced numerical modeling and machine learning techniques. This approach employed an expansive dataset of river flow discharge generated by implementing a sophisticated numerical model. This dataset encompasses a diverse spectrum of potential future flooding scenarios. Building upon this foundation, the Expanded Framework of Group Method of Data Handling (EFGMDH) has been devised, a novel model that Developed flow velocity forecasting using the EFGMDH-based technique involves five input variables: X, Y, Slope, the Manning coefficient at the middle of the cross-sectional area (n Middle ), and flow discharge (Q).
Conclusions This study has delved into the significant impact of climate change on flooding events within the Ottawa River Watershed. Notably, the occurrence of unprecedented floods, exemplified by the 2019 event surpassing a 100-year flood magnitude, underscores the urgency of developing effective strategies. Addressing this challenge necessitated the integration of advanced numerical modeling and machine learning techniques. This approach employed an expansive dataset of river flow discharge generated by implementing a sophisticated numerical model. This dataset encompasses a diverse spectrum of potential future flooding scenarios. Building upon this foundation, the Expanded Framework of Group Method of Data Handling (EFGMDH) has been devised, a novel model that provides decision-makers with actionable equations for estimating three pivotal hydrodynamic variables: river flow depth, flow velocity, and floodplain width.
Acknowledgments: The authors acknowledge the support of the  Natural Sciences and Engineering Research Council of Canada (NSERC)   Discovery Grant  (# RGPIN-2020-04583 ) and the  "Fond de Recherche du Québec-Nature et Technologies", Québec Government  (# B2X-315020 ). The last author (H. B. ) would like to extend their sincere gratitude to the  International Research and Experiential Learning (IREX)  office at the  University of Ottawa  and the  International Office of ENGEES  for their invaluable support in organizing and facilitating the visit of students from  ENGEES  to uOttawa.
g. , resource allocation or financial incentives); and; 3) preparing for the next infectious disease pandemic. However, data on COVID-19 epidemiology in Japan are limited. For example, studies have been focused on hospitalized patients or populations with specific risk factors or have relied on regional registry data  [11] [12] [13] [14] [15]. In this report, we describe hospital-based COVID-19 epidemiology in Japan in 2020 using a nationwide multihospital database. The aim was to expand knowledge of the broader patient population by examining both outpatient and inpatient care.
DATA SOURCE We used the RWD database  [16, 17] , which is maintained by the Health, Clinic, and Education Information Evaluation Institute (Kyoto, Japan) and managed by Real World Data Co. , Ltd (Kyoto, Japan). The database contains the records of approximately 20 million patients from around 160 medical institutions, as of 2020. Contributing hospitals are located across Japan, although they tend to cluster in western regions. The database comprises demographic data, diagnoses, prescriptions, and procedures from both outpatient and inpatient services. Prescription data from electronic medical records are drawn T from physician medication orders; this enables the extraction of information on any drug prescriptions, including those not covered by insurance (e. g. , drugs used in clinical trials).
Other data were obtained from insurance claim records and/or electronic medical records, such as physician orders. The data were automatically extracted from the electronic medical records of each medical institution. Patient records are managed by the allocation of unique identifiers to each individual; these identifiers are used consistently within each institution. In addition, two hospitals outside the RWD database offered data on patients with COVID-19 in response to a request from the Japan Medical Association. These datasets were combined with the RWD dataset and analyzed.
PATIENT INCLUSION CRITERIA Patients with COVID-19 were identified using the International Statistical Classification of Diseases and Related Health Problems, 10th revision, code U07, which is a specific code for COVID-19. Patients assigned a U07 code who had an additional "suspicious" supplemental disease code were excluded from the analysis. The study period was from January 1, 2020, to December 31, 2020. Patients who did and did not require hospitalization were included. If a patient visited a medical institution as an outpatient but was subsequently hospitalized during the same disease course, they were defined as an inpatient. If a patient had multiple episodes of COVID-19 infections, only the first episode was analyzed. There were no exclusion criteria. To ensure that all patients with COVID-19 were enrolled, we did not use a data lookback period  [18].
It is also possible that healthcare providers in this previous study (which ended in July 2020) were encouraged to participate in clinical studies of favipiravir when they queried the Japanese government regarding the drug's availability  [11]. This may have prompted them to prescribe favipiravir to hospitalized patients, even though data on treatment efficacy were limited. As shown in this example, COVID-19 epidemiological data should be interpreted in the context of how and when data were collected during the ongoing pandemic. Our findings could serve as a benchmark for future infectious disease pandemics, particularly during the early pandemic phase. First, this study provides data on how patient characteristics and care delivery changed over the course of the pandemic across different care settings.
Our findings may help in the development of healthcare resource preparation and allocation plans. Second, epidemiological reports on the early phase of the COVID-19 pandemic often focused on the care of inpatients  [37] [38] [39] [40] [41] , particularly those with critical illnesses, whereas our study also included outpatients. As the time evolved through each epidemic wave, the characteristics of inpatients appeared to shift toward more advanced age and more frequent comorbidities, which shift was not apparent in the outpatient setting (Supplemental Fig. 1 ). Although the reason for this was unclear, we speculate that the threshold for hospitalization has changed to more selective as the pandemic progressed, such reasons as the better understanding for high-risk condition of severe COVID-19 or hospital overload (though we did not have this metric in the database).
The study enrollment criteria did not define a data lookback period to ensure that all COVID-19 patients were included, and thus prescription history records were incomplete for a subset of patients. As a result, for example, statins may have been prescribed to regular statin users with hyperlipidemia or naïve users with COVID-19 to expect an anti-inflammatory effect  [42]. However, we assume that the prescription of other drugs, such as remdesivir, favipiravir, and dexamethasone (particularly when used in the inpatient setting), was based on their likely indications for targeting COVID-19. Third, tracking patient outcomes was only possible within the same medical institutions. Therefore, the outcomes of outpatients who were subsequently hospitalized at other facilities or inpatients who were transferred to other hospitals may have been underestimated.
Fourth, we could not access the results of the COVID-19 diagnostic tests (e. g. , polymerase chain reaction); therefore, there is a possibility that COVID-19 was misclassified in some patients. However, public medical insurance payment for patients with COVID-19 is required by law. Thus, we believe it is unlikely that test-negative patients were classed as COVID-19 cases, even though we relied only on International Statistical Classification of Diseases and Related Health Problems, 10th revision codes to determine the diagnosis of COVID-19. Finally, medical institutions that submitted data to our database were not evenly distributed across Japan. Thus, our data may not be fully representative because of regional differences in practice patterns (e. g. , prescription preferences), population composition, and hospital load.
This paper studies how opioid analgesic sales are related to socioeconomic conditions in France. Using the OpenHealth database on prescription opioid retail sales at the district level from 2008 to 2017, we show that increases in the poverty rate induce more sales: a one percentage point increase in poverty produces, approximately, a ten percent increase in opioid sales. Our analysis further shows that opioid sales are positively related to the share of middle-aged people and the share of individuals with basic education only, while they are negatively related to population density. We identify the causal effects of economic conditions on opioid sales by using two alternative strategies. First, we implement a Two-Stage Least Squares (2SLS) approach, where we instrument for poverty by exploiting a reform aimed at reducing poverty of low-income individuals.
4 in 1999), which is 23 times larger than the 3. 8 in France in 2016 (1. 3 in 2000) (CDC, 2019 and CépiDc-Inserim, 2019). 2  We investigate sales trends at the national level, whether there are local variations in opioid use and whether these variations are caused by variations in economic opportunity while controlling for socio-demographic indicators. This was made possible by using the OpenHealth database that enables access to high-resolution, high-frequency retail sales data for all opioid active ingredients available on the French market between 2008 and 2017. Although similar studies have been performed in the US, the French health, economic and social systems, as well as laws regulating access to narcotic medications, are deeply different from those in the US. This may induce relevant differences in consumption behavior, prescribing patterns and the way opioid use relates to indicators of economic opportunity.
Consumption Data and Methodology The OpenHealth database contains information on opioid retail sales in France since 2008, both in terms of turnover and in terms of consumer units sold, where consumer units indicate the number of packs sold for each product. Sales data concern the 94 French departments composing Metropolitan France. 5  Data are provided at the national, regional and department levels and on a monthly, quarterly and yearly basis. For our investigation, we exploit annual data both at the national and at the department level. For each item, the database indicates the product's denomination, the name of the pharmaceutical company marketing it, the number of packs sold, the number of pills in each pack and the quantity of the active ingredient (in milligrams) contained in each pill.
This is important since one can compute the total quantity (in mg) sold of each active ingredient and convert this to the number of DDDs consumed, which is the methodology recommended by the WHO for drug consumption studies. DDD means Defined Daily Dose and is defined by the WHO as "the assumed average maintenance dose per day for a drug used for its main indication in adults", that is the amount (in mg) of an active ingredient that should be administered to an average weight adult patient (70 kilograms) daily for a drug's main indication. Using this metric allows comparing consumption trends across different products as well as aggregating consumption data for different active ingredients. Throughout the paper, drug usage is measured in terms of number of DDDs per 1000 inhabitants per day, by exploiting the following formula: DU = 1000 * n * p * mg/p 365 * DDD * h where DU denotes drug usage, n is the number of packs sold, p is the number of pills in a pack, mg/p is the number of milligrams per pill, h is the number of inhabitants in the geographical area of interest and 365 is the number of days in a year.
Econometric Analysis In this section, we assess the responsiveness of local per capita opioid analgesic sales to changes in socioeconomic indicators. To do this, we run a series of panel regressions of per capita opioid use on socioeconomic determinants in 94 French departments (Metropolitan France) for the years between 2008 and 2017. Our main focus is on the relationship between opioid consumption and economic conditions. Retail sales data are extracted from the OpenHealth database, while data used to proxy department socioeconomic status are collected from the Institut National de la Statistique et des Études Économiques (INSEE) and Organization for Economic Cooperation and Development (OECD) websites. Appendix B offers a detailed description of these data. Table  1  provides a list of the variables, together with summary statistics.
To construct our population density variable, we exploit population data and data on the area of each department (in square kilometers), both provided by INSEE. C The French Social System and the "Revenu de Solidarité Active (RSA)" Reform France, as a country, is characterized by a strong social system and a large variety of benefits paid by public authorities to individuals in precarious situations, to ensure them a minimum revenue. 18 Until June 2009, these included, among others, the "Revenu Minimum d'Insertion (RMI)" and the "Allocation de Parent Isolé (API)". The RMI was created in 1988 to guarantee minimum resources to anyone aged 25 or over and/ or who was responsible for at least one child born or to be born. The API was created in 1976 and it was addressed to individuals who were taking care alone of at least one child born or to be born.
D. 2 Opioid-related Death In this subsection, we analyze mortality data related to opioid consumption in France. These  Unfortunately, in the French database, MCOD T-codes are not available, so that we are not able to determine to which specific substance the death is related. Indeed, the X42, X62 and Y12 codes include poisonings for codeine, heroin, methadone, morphine, opium, but also cannabis, cocaine, LSD, and mescaline. axis reports the number of deaths per 1000000 inhabitants. For this graph, we restrict attention to data concerning accidental deaths only (codes F11 and X42). In France, opioid-related deaths rose from 1. 3 to 3. 8 per one million population, which is a 192% increase over the 2000-2016 period and almost 5 deaths per week in 2016. Even though these numbers are still far from those characterizing the opioid crisis in the US, these trends arise safety concerns and induce authorities to reflect on appropriate measures to prevent a potential opioid epidemic in Europe.
data are extracted from the Centre d'épidémiologie sur les causes médicales de décès (CépiDc-Inserim, 2019) database on all causes of death in France from 1979 to 2016. In this database, the causes of death are coded and classified according to the International Classification of Diseases (ICD-10) realized by the WHO. In order to identify deaths related to problematic use of opioids, we selected the codes F11, X42, X62 and Y12. 20.
gratefully acknowledge  Catherine Commaille-Chapus Amaral-Garcia  and  Mathieu Parenti  for useful comments and  Federico Gallina  for his help in managing the data. This research has benefited from the Funding of the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7-IDEAS-ERC)/ERC Grant N. 339950 , and from the financial support of  Baillet-Latour Fund. 1  Department Start Date Start Date RSA -RMI RSA -API Aisne.
Participants were recruited between 20 June 2022 and 22 July 2022. Initially, a purposive sampling approach was used, with individuals invited to participate based on their expertise or key role within the poultry sector. Snowball sampling was subsequently used, with interviewees asked to suggest other relevant actors. The sample size was determined both pragmatically and with regards to the concept of information power, whereby sample size is considered within the context of the study aim, participant specificity, use (or not) of established theory, quality of dialogue, and analysis strategy  [37]. A relatively large sample size (for qualitative research, where sample sizes can range from one to 50 interviews depending on the study methodology  [38] [39] [40] ), was used based on the broad, exploratory nature of the study aim and analysis strategy, the sample specificity (the target to include multiple stakeholders from several different groups of stakeholders), and the highly variable quality of dialogue identified during data collection.
Data collection instruments -interview guide A semi-structured approach was selected to ensure that information on the topics of interest was collected, while allowing participants to introduce topics which were important to them. Six different versions of an interview guide were developed for six different categories of interviewees (an example is provided in S1 Appendix in the Supporting Information). Four broad themes of questions were consistent across all versions of the interview guide: transversal topics (general questions about the interviewee's role and the main challenges they face), disease prevention and control, AMU and stewardship, and health information and monitoring, and were used as points of departure for forming interview questions and responding to interviewees. The interview guide was revised by all authors before being piloted internally with five of the authors, followed by an external stakeholder (an Indonesian poultry farmer).
Data collection method -interviews Interviews were conducted between 27 June 2022 and 22 July 2022. The interview team consisted of two international researchers and nine Indonesian personnel (three researchers, one intern, and five translators), with two to five interviewers present at each interview. Interviews were conducted in-person where possible but when not feasible an online video conferencing tool was used. In-person interviews were carried out at a site chosen by the interviewee, usually their place of work (e. g. , farm office for poultry farmers) or a neutral site (e. g. , a cafe). Interviews were led by one of the Indonesian-speaking researchers to facilitate communication and build trust with the participants. Simultaneous translation was provided for the English-speaking researchers to facilitate note taking and familiarisation with the interview content, and to allow additional follow-up questions to be asked at the end of the interview.
Data processing Thirty-five interviews were conducted. Thirty of the interviews were conducted in Indonesian -of these, 25 were recorded in the original language (Indonesian) and five were recorded in the English translation. Five interviews were conducted and recorded in English. All interviews were transcribed verbatim and anonymised using codes for each stakeholder. The Indonesian transcripts were subsequently translated to English by external professional translators and verified by the Indonesian-speaking authors against the original transcripts and audio recordings.
General approach The dataset consisted of notes taken in the field during the interviews and the interview transcripts. Reflexive thematic analysis following the method of Braun and Clarke  [41, 42]  was employed, whereby the researcher plays an active role in engaging with and generating themes from the data to identify patterns of shared meaning. The interpretivist framework applied meant that there was a focus on the interviewees' descriptions of their experiences related to AMU and their explanations of these experiences. Analysis of the interview notes from all authors and transcripts was performed by the first author using NVivo  [43]. Efforts to assure the integrity and trustworthiness of data were made through inclusion and consideration of material from all categories of stakeholders, to ensure a diversity of perspectives on the same topic was examined.
They are generally used as dynamical databases for rangeland management, not based on time but on events. Note that similar graphs representing states and transitions were anecdotally used in the 1970s under the term "behavior graph"  [3]  or "replacement sequence"  [91]  where, in the latter, transitions have specific time durations. While succession diagrams and STMs are not formal models, several authors have proposed using Markov chains for modeling vegetation succession  [92, 93]  and rangeland dynamics  [94]. Central STM concepts have been well defined by  [90] : (1) A state is "a recognizable, resistant and resilient complex of two components, the soil base and the vegetation structure"; (2) A transition is "a trajectory of system change away from the current stable state that is triggered by natural events, management actions, or both"; (3) Reversible transitions occur within states, which may consist in several community phases (e.
Each modeling framework emphasizes new aspects of a system and thus widens the range of questions that can be addressed. Each of them simultaneously imposes methodological and practical limitations. On the one hand, methodological limitations over-determine both the systems or phenomena that can be modeled. They originate from the methodology or the mathematical tools employed to model the system. For instance, STMs cannot be used to predict unobserved transitions or loop analysis cannot be applied to non-equilibrium systems. On the other hand, practical limitations generally stem from the relationships between the data and the model (e. g. , limiting data availability). For instance, insufficient data may impede quantitative and robust parameterization of compartment models. In ecosystem models such as the dendrochronological study previously cited, the fine tuning of the tree growth module clearly shows limitations in terms of calibration, of computation time required, of uncertainty and, ultimately, of choices in representing the phenomenon  [8].