dataesr/scientific-paragraphs-categorization huggingface dataset:
Abstract screening and full-text screening Synthesis of the literature followed a two-step approach, in line with recently published SLRs (e. g. Feser 2022). First, abstract screening led to the inclusion of 110 papers, with 298 removed (see Fig. 1 ). This involved removing all articles which failed to explicitly examine EL or TL, were deemed inadmissible by type but had passed the database filtering, were duplicates the first screening had missed, or were not published in English. For example, many articles were found which mentioned either TL or EL only once or twice, included references in the bibliography that contained those terms, or had abstracts in English but were written in another language. Such articles were removed from the sample. Finally, a full reading of the remaining 110 articles was carried out, leading to the exclusion of a further 85 papers.
Limitations The present contribution claimed to clarify the interplay between EL and TL, seeking to curb the catachresis plaguing EL research. However, this paper is subject to a number of limitations. First and most importantly, the scope of the review was not exhaustive. The present SLR followed a structured and literature-based approach. Despite this, it is impossible to guarantee that all relevant articles were included in the databases chosen. As stated in Sect. 2. 1, only articles that explicitly mentioned TL and EL were examined. For a more comprehensive picture, this pool could be expanded along two dimensions. First, research could be included that explicitly examines TL within organizations that are emphasizing entrepreneurial action, such as those in the process of spinning off sub-units, acquiring startups, or engaging in corporate venturing.
In short, this could identify organizational contexts in which leadership is examined that falls under a broad definition of EL, but where the specific term is not used. Conversely, all papers could be included which explicitly examine EL, and from these only those that examine leadership behaviors typical of TL without explicitly employing the term. A second limitation lies in the comparative brevity with which some of the articles were necessarily treated for the sake of space. The statistical results in  Cai et al. (2019) ,  Newman et al. (2018) , and  Lee et al. (2020)  would for example permit a far more in-depth discussion with respect to RQ3. Finally, several review articles with interesting insights fell outside the bounds of this sample because they were either thematically off-point, not found in any of the databases examined (e.
To demonstrate the practical use of our model, we collected hourly data from a large European retail chain over a time horizon of 2 years for 66 stores. We analyze sales data and selling prices for five perishable products (fruit and vegetables) with a range of maximum shelf lives and age-dependent perish probabilities. The store managers currently use their own judgment to make order decisions, and the retail chain is considering using commercial software solutions in the future that would contain standard methods such as the periodic review or newsvendor model. In this paper, we compare such standard methods to the optimal solution and the heuristics we develop. The paper proceeds as follows. In Section 2, we review the literature on periodic review models with perishable prod-ucts. The following sections make several contributions to this literature.
Real data We compare our leading heuristics E1 and ADPRV both to each other and to the basic heuristics NV and PRV using real data from a large European retail company to better understand their performance in practice. We consider different types of perishable products (fruit and vegetables) that have different maximum lifetimes and perishability patterns. Table  8  contains the perish probabilities of the products, which start from 0. 3 or 0. 4 for the first day, and increase or stay the same for the days thereafter, reaching 1 by a maximum of 8 days. At the end of the day, the store manager checks which items are still salable for the next day and can be carried over. All perished items are discarded. The managers use their own judgment to place an order for the next day. We evaluate these products with sales data of 66 stores.
We collected daily selling prices and current batch sizes for each date, since the retailer can only order products in given batch sizes. Shortage penalty costs consist of the difference between selling price and ordering costs adjusted with discount factor  = 0. 999, the latter chosen to ensure that costs over the entire time horizon are reflected in the results. Due to confidentiality reasons, we normalize the data so shortage penalty costs equal one. The daily unit inventory holding cost rates are 0. 25% of the penalty costs. The outdating cost equals the penalty cost multiplied by 1. 12 to reflect the cost of a lost sale in addition to disposal and handling costs of the outdated item. For each product-store combination, we have over 500 days of data. Since we are only able to collect sales data and lost sales are unobserved, we estimate demand using hourly sales data and product-and store-specific demand patterns based on the nonparametric approach developed by  Lau and Lau (1996).
Using the database provided by SOMENOR, the company in charge of data management for SOS Médecins, we retrieved the data for a random sample of consultations from the period from 1 January to 31 December 2019. The population of patients between 18 and 75 years of age comprised 27,431 consultations during this period. We used a random sample of 5,061 consultations (18% of the sample size), of which 59 were excluded due to missing data for the main outcomes. For each case, we randomly selected controls from among the 3,785 patients who did not re-consult within 30 days after an initial consultation during the study period using an ad hoc matching algorithm. Then, each case (i. e. , patient with repeat-consultation within 30 days) was matched with one control (i. e. , a patient who did not return for repeat consultation within 30 days after the initial visit).
Endpoints and variables recorded The primary outcome was a repeat consultation with 30 days after an initial consultation with SOS Médecins unscheduled care services. Secondary endpoints were the time of day when the consultations took place; need for hospital admission; the diagnosis retained at each consultation. Using the database provided by SOMENOR, the company in charge of data management for SOS Médecins, we retrieved the data for a random sample of consultations from the period from 1 January to 31 December2019. For each consultation, we recorded the patient's age and sex. Regarding the consultations, we recorded: triage category (when the patient calls the SOS Médecins unscheduled care service to ask for an appointment, the calls are categorized by degree of urgency, namely: non urgent, relative emergency, life-threatening emergency); reported motive for consulting; timing of the consultation (weekend, holiday, out-of-hours (8 pm to midnight, and 6.
, 2018; Cao & Henderson, 2021). Table  1  presents information about the respondents. T a g g e d E n d T a g g e d P The interviewer transcribed each interview in the same week it occurred. A second researcher checked all interview transcripts by listening to recordings. Since our research addresses the impact of AI T a g g e d E n d T a g g e d P tools on human resource performance, we gathered our data from multiple sources, primarily focusing on the human resources department, which includes different functions that will all be impacted by AI tools. We conducted our interviews with the human resources director, compensation and benefits manager, talent acquisition manager, onboarding specialist, and government relations officer to clearly understand how AI tools would impact their scope of work and overall performance from a financial, productivity, and employee engagement perspective.
Compliance with required on-farm biosecurity practices reduces the risk of contamination and spread of zoonotic and economically important diseases. With repeating avian influenza epidemics in the poultry industry, the need to monitor and improve the overall level of biosecurity is increasing. In practice, biosecurity compliance is assessed by various actors (e. g. , academic, private and public institutions), and the results of such assessments may be recorded and gathered in databases which are seldom shared or thoroughly analyzed. This study aimed to provide an inventory of databases related to the assessment of biosecurity in poultry farms in seven major poultry-producing European countries to highlight challenges and opportunities associated with biosecurity data collection, sharing, and use.
The institutions in charge of these databases were contacted and interviewed using a structured questionnaire to gather information on the main characteristics of the databases and the context of their implementation. A total of 20 databases were identified, covering the gamut of poultry species and production types. Most databases were linked to veterinary health authorities or academia, and to a lesser extent interbranch organizations. Depending on the institutions in charge, the databases serve various purposes, from providing advice to enforcing regulations. The quality of the biosecurity data collected is believed to be quite reliable, as biosecurity is mostly assessed by trained farm advisors or official veterinarians and during a farm visit. Some of the databases are difficult to analyze and/or do not offer information concerning which biosecurity measures are most or least respected.
Materials and methods To identify poultry biosecurity databases, correspondents in each of the seven European countries involved in the NetPoulSafe consortium  (20)  were contacted. These correspondents then identified stakeholders who managed or owned poultry biosecurity databases and interviewed them using the questionnaire provided by this study's authors. The questionnaires collected information on the characteristics of each database (one questionnaire was filled in per database). The questionnaires were completed between May 2021 and February 2022. A database was considered as any source of biosecurity data (digital or physical) obtained using a given questionnaire, in a given context, by a given organization (private or public). Although a database is supposed to gather data in a single place or file, biosecurity assessment systems in which data were collected but not gathered and assembled also were included.
As the aim of the study was to describe active or "recent" systems, it was decided to include databases with data produced between May 2011 and May 2021. The questionnaire used in this study aimed to gather information describing the context and the content of the databases. It included five parts: (1) the number of farms, the type of production, and the sampling procedures (13 questions), (2) data ownership, storing and sharing conditions (eight questions), (3) the aims of data collection and the intended feedback (three questions), (4) how the data were obtained (two questions), and (5) the types of biosecurity practices which were assessed (21 questions). The questionnaire mainly contained closed questions, but spaces for comments were left periodically so that additional information could be recorded when closed questions would not suffice.
Types of farms and production stages The biosecurity compliance databases covered various poultry species and types of production. Broiler chickens were included in 16 databases, egg layers in 14 databases, turkeys in 14 databases, ducks in 11 databases, and others types of poultry (e. g. , guinea fowl and game birds) in eight databases. The databases also covered various production stages: final production in 19 databases, selection and multiplication (i. e. , all stages involved in reproduction) in 10 databases, and hatcheries in five databases.
Database owners and aims The most common origin for a database was veterinary health authorities (n = 10), followed by research institutes (n = 9) and interbranch organizations (n = 4). Interbranch organizations are defined as organizations gathering actors involved in the production chain of a product. For example, in a given country, the interbranch organization for broiler chicken includes representatives from different producer organizations (also called integrators), syndicates, hatcheries, breeder stock and genetics companies, feed companies, slaughterhouses, and distributors. A producer organization and a quality scheme were also at the origin of one database. No veterinary organization was at the origin of any database. The origin of the databases is presented by country in Figure  1.
The aims of data collection were the following: enforce a regulation (n = 8), provide an inventory (i. e. , an overview of current practices; n = 8), give advice to farmers (n = 7), research (n = 7), provide a certification (n = 4), serve as a condition to give an incentive to farmers (n = 2), inform risk analysis (n = 1), and sensitize farmers to the importance of biosecurity (n = 1). The relation between aim and origin is given in Figure  2. Among the eight databases related to the enforcement of a regulation, some were specifically dedicated to the control of a given pathogen (two on avian influenza and two on Salmonella) and some other were not specific to any given pathogen, although set in a context of avian influenza epidemics.
Participation and coverage The participation and the coverage of the poultry farms targeted were related to the origin of the databases. Different situations consequently were identified. The first consisted of mandatory annual inspections targeting 100% of the study population carried out under veterinary health services inspection programs. The second also involved mandatory annual inspections carried out by veterinary health services, but these only targeted a fraction (estimated to be below 30%) of the total number of poultry farms due to their high numbers. The third situation consisted of a non-mandatory sanitary certification program managed by veterinary health services which granted farmers additional compensation if a mandatory culling protocol for Salmonella control was implemented.
Conditions of biosecurity assessment The conditions of biosecurity assessment refer to the type of person involved in data collection, the items included in the checklist, and the way the data were collected (postal, telephone or face-to-face interview, and/or inspection of farm documents and premises). In most cases, data were collected during farm visits (n = 18) and the people most commonly involved in data collection were farm veterinarians (n = 7) and public health authority veterinarians or technicians (n = 6). The complete results for the conditions of data collection are presented in Table  2.
Format and feedback Half of the databases (n = 10) were not structured in a single file. Among these, two databases were not digitalized and the data, only stored on paper, were sometimes stored in different places. In general, data were first collected on paper (n = 13) and then digitalized, individually or gathered in a single file. Providing feedback to farmers was considered to be done by 16 databases. In most cases, the feedback consisted of an individual report given to the farmer (n = 15), sometimes containing a progress plan (n = 2) or providing a benchmark for the farmer (n = 2). In some research projects, the projects' overall results also were given to the farmers (n = 5) or published in a public report (such reports could not be quantified, but were produced in at least one case) or in a scientific publication (when data were collected for scientific purposes).
Database accessibility by other institutions Among the 18 databases existing in digital format, seven were associated with a research project. Among the 11 non-research databases, four were owned by private sector organizations, while seven were owned by public sector organizations. Again among these 11 non-research databases, eight could be shared under certain conditions, according to their owners. In this case, data sharing refers to obtaining non-processed data on biosecurity assessment. One of the database owners declared that when data were collected on biosecurity, it was specifically agreed with the farmers that the data could not be shared. The conditions for data sharing were to establish a specific agreement between the owner and the institution in charge of the analysis. Some specific requirements also were set with regard to the choice of the institution in charge of the analysis, for example, public health services only, or public institutions from the same country only.
In addition to that, most requirements for data sharing included a step of anonymization and/or the removal of any personal data. Finally, when the eight database owners (those who declared that they could share their databases under certain conditions) were asked to share some parts of their databases with the NetPoulSafe project, two of them eventually refused. The reason given was that if poultry industries from different countries were compared, there could be a negative economic impact (in terms of international trade). The overview of the number and type of non-research databases accessible for analysis by another institution are given in Figure  3.
Biosecurity aspects covered by the databases The types of biosecurity practices which were included most frequently in the databases comprised vermin management (20/20), cleaning and disinfection protocols (19/20), anteroom presence and equipment (19/20), carcass management (19/20), and farm delimitation  (19/20). The types of biosecurity practices which were included least frequently were egg management procedures (12/20), water quality (13/20), poultry flow management (13/20), equipment sharing between farms (13/20), description of the farm characteristics (15/20), farm management in all-in/all-out or multiple-age system (15/20), and vehicle flow management  (16/20). The main types of biosecurity practices covered by the different databases are given in Figure  4.
Discussion The originality of this study is to draw from experiences of biosecurity data collection systems in a variety of European countries to identify challenges and opportunities, and provide recommendations for further improvements. This study has some limitations related to the identification of databases. It is possible that this study overlooked some poultryrelated databases when the databases or their owners were unknown to the investigators at the country level. In particular, some academic research databases with (still) unpublished results may have been missed. Furthermore, in countries where the management of biosecurity is operated at the provincial or regional level rather than the national level, some initiatives may have been missed, given the Origin of the 20 referenced poultry biosecurity compliance databases, in each of the seven participating countries.
When a database involved several countries, it was counted once for each country. Another limitation to the study was the difficulty of assessing with precision, for each database and then for each region or country, the percentage of poultry farms included. On the one hand, the coverage for research-associated databases was low  (23, 24)  for both practical reasons and because they did not necessarily require high coverage. In Aim of the 20 referenced poultry biosecurity compliance databases, in relation to their origin. Some databases had multiple origins or aims, therefore all combinations were accounted for. databases fed by auto-evaluation tools, the number of farms covered also can vary greatly depending on participants' willingness to use the tools and whether the tools are user-friendly  (25).
Moreover, in such databases, because assessments are anonymous, it may be not be possible to know if several assessments were produced on the same farm. On the other hand, databases associated with official inspection programs presented for each country or region cover an important share of the poultry production. In this study, veterinary health services emerge as the main actors in biosecurity assessment. However, we found that the biosecurity assessment systems used by veterinary health services vary greatly. The other types of organizations involved in biosecurity assessment also are highly diverse. One of the main aims of the reported databases was the enforcement of biosecurity regulations. Therefore, most countries had at least one biosecurity assessment system managed by official veterinary services.
The only exception was the Netherlands, where the main biosecurity assessment system is managed by an interbranch organization. Nevertheless, it covers a very important share of the poultry farms in the country. Interestingly, in some countries, the official veterinary health services may manage several biosecurity databases. In France, for instance, diverse biosecurity assessment systems were developed in response to the emergence of specific pathogens over time; the oldest system is linked to a Salmonella control plan, whereas the newest is linked to a plan to control the more recent highly pathogenic avian influenza (HPAI) epidemics. Furthermore, biosecurity databases managed by veterinary health service may also serve purposes other than the enforcement of biosecurity regulations. For example, when a Salmonella outbreak takes places on a farm that has been shown to meet biosecurity requirements, the farmer obtains a larger financial compensation.
Similarly, in Belgium and Germany, financial compensations for HPAI outbreaks in poultry farms currently depend on the level of on-farm biosecurity (26, 27). Surprisingly, giving advice to farmers can also be the aim of a biosecurity monitoring system managed by official veterinary health services. In France, in order to provide advice to farmers, some mandatory visits involving a biosecurity assessment are conducted by farm veterinarians and funded by the veterinary services  (12). For certification purposes, biosecurity assessment was sometimes included in a broader framework, in addition to other quality-related aspects, such as animal welfare or traceability (three different databases, described in Belgium and France). In the landscape of poultry biosecurity databases presented in this study, the Biocheck database presents some unique features in terms of objectives and origin.
It consists of an online self-assessment tool which also records the biosecurity data entered by the end-user. Since this tool was developed by researchers, the data obtained and the tool itself are regularly used for research purposes worldwide. This study aimed to identify the challenges and opportunities associated with biosecurity assessment systems and their databases. This can be considered at two levels: database construction (conditions of data collection) and data use (sharing, analysis). To begin with, at the level of data collection, we expected the quality of the data to be heterogeneous. One reason for this is that the.
Farm veterinarian 7 Public health authority veterinarian or technician 6 External auditor 4 Researcher 4 Technician 4 Farmer 3 In some databases, biosecurity could be assessed by different types of people and by different means. Frontiers in Veterinary Science 07 frontiersin. org person conducting a biosecurity assessment is known to play a role in the reliability of the information obtained  (7). In many of the databases described, biosecurity was assessed by a trained person (other than the farmer) and following a standardized procedure. Moreover, in many cases, farms visits were conducted, allowing the use of farm observations and written documents, adding to the reliability of the data obtained. However, some of the databases relied on data collected through self-assessments, which may be biased.