dataesr/scientific-paragraphs-categorization huggingface dataset:
Other explanatory variables In addition to the French knowledge stock, we also use the US knowledge stock and some climate proxies as explanatory variables. The US stock of knowledge is used as a proxy to represent the foreign stock of knowledge, following  Sheng et al. (2011). This variable is computed from the research investment taken from  Alston et al. (2010) , using the same assumption as that used to build the French stock of knowledge. Agricultural productivity can be affected by variations in climatic conditions from year to year and by the recent climate warming trend. The climatic variables were compiled at the national level with data from Météo-France (SAFRAN database). The following four variables were compiled: radiation, rainfall, temperature and the difference between rainfall and evapotranspiration.
Estimation results The econometric framework presented previously has been applied on our French dataset covering the period 1959-2012. Different models were estimated, and their characteristics are summarized in Table  1. First, we consider a model defined by a distribution of research lag with a peak at 25 years (δ=0. 9, λ=0. 7 in Eq. (  3 )). Two models (1 and 1') are first estimated depending on whether the spillovers from the foreign stock of knowledge are taken into account. These first two models allow us to investigate Hypothesis 1. Then, to investigate Hypothesis 2, we use different gamma distributions allowing for increasing lags (models 2, 2', 3 and 3'). Finally, we incorporate different measures of knowledge to capture the evolution of research priorities and test the impacts (models 4, 4', 5 and 5').
Stability of knowledge elasticity over time In line with hypothesis 1, we now analyse the stability of the elasticity of knowledge over time. To do so, we consider a specification that is a modified version of models 1 and 1' presented above and in which the full period  is divided in two successive periods P 1 an P 2. A dummy variable indicating the corresponding period 1 {t∈P2} = 1 if t ∈ P 2 and 0 if t ∈ P 1 is created. In the specification of the model, the stock of knowledge is interacted with this dummy variable, leading to a short-run and long-run estimated elasticity that are specific to each sub-period. The number of years in the dataset prevents us from considering more than two different sub-periods. Equation 8 becomes: ∆ ln T F P t =a 0 + a 1 t + ζ ln T F P t-1 -1 {t∈P1} θ ′ 1 ln(x t-1 ) -1 {t∈P2} θ ′ 2 ln(x t-1 ) + p-1 i=1 ψ i ∆ ln T F P t-i + p i=0 1 {t∈P1} ϕ ′1 i ∆ ln(x t-i ) + 1 {t∈P2} ϕ ′2 i ∆ ln(x t-i ) + ǫ t ( 11 ) 21 The stock of knowledge results from the weighted sum of research investments over the past 50 years.
A. 3 Public research investments between 1909 and 1944 The computation of the knowledge stock for a given year requires having data on the research expenses over the previous 50 years. The oldest stock of knowledge is for year 1959 and is estimated from the research investment from 1909 until 1959. The French public agricultural research investment over the period 1909-1944 was small, but no precise data are available. We hence extrapolate our research spending dataset from 1909 until 1944 using the trend of US spending. This assumption has a minor effect because the research spending in 1945 (and in the years before) represents less than 1% of the spending in the recent years. Alternative assumptions were tested and have minor impacts on the estimations. More precisely, these alternative assumptions were either to consider a stable spending from 1909 to 1944 equal to that in 1945 or to consider a linear trend from 1929 to 1939.
Data The main source of our data is the Bloomberg Professional Terminal Service (Bloomberg). We extract all loan amendments in Europe with effective dates between January 1999 and December 2017. This first data set contains description of amended terms, such as changes to amount, maturity, covenants, pricing grid, and definition (which is a non-material amendment). We extract all loans issued to European borrowers (excluding Financial and Government entities) with effective dates between January 1999 and December 2017. This second data set contains information at origination on loan agreements, such as facility amount, spread, maturity, covenants, collateral, date, type (revolver, term…), purpose (corporate, refinance, acquisition…), currency, etc. We also have information on lenders, such as the number of lenders, the retained shares of the loan, the nationalities (country of incorporation), the roles (or titles), and the identity (names).
We merge both datasets (loan amendments and loan agreements at origination with lenders' information) using unique loan identifiers. 3 Next, we use the borrower identifiers to gather firms' characteristics, including descriptive information (name, industry sector, country, identifiers…) and accounting variables and financial ratios. 4 We also use data from the World Bank, Demirguc-Kunt et al. (2012),  Djankov et al. (2007 ), Favara et al. (2012)  to obtain (borrower) country level data related to economic growth, credit and stock markets development, the rule of law, and creditors' protection in case of debt restructuring.
Design of the online interface to elicit swing weights We summarize the purposes of the interface hereafter. First, the interface should enable to elicit the weights from the respondents (i. e. facilitate preference learning), and save the elicited weights in a database. Second, in order to meet the prior aim, because the targeted audience is laypersons, the interface should inform the laypersons about the main decision issue, and the objectives to consider when deciding how to address the main issue (i. e. facilitate factual learning). Third, it should be compliant with the swing weight elicitation process, as described in the textbooks e. g. [23]  (i. e. facilitate process learning), and follow the recommendations from the existing building code  [44]. Fourth, it should improve a previous attempt by Lienert, Duygan and Zheng  [30].
They had faced the following inconsistencies: rating (i. e. assigning weights) inconsistent with ranking of objectives, and rating of the worst-case hypothetical alternative different from zero (where all objectives are on their worst level and which should be zero according to swing convention). They suggested using the inputs of the ranking step to present the hypothetical alternatives in the right order for the following rating step. In addition, we list some constraints that we were facing. We had to develop the tool using a survey platform (no resources for a stand-alone software). We chose the Qualtrics platform (www. qualtrics. com, retrieved on 19. 07. 2018), as it was used for the previous attempt  [30]. The following part describes the survey in more detail. For screenshots, please see the supplementary information (Section SI 2a).
In our case, only 36 respondents (12. 1%) rated the worst-case hypothetical alternative as the least preferred alternative. This would lead to the deletion of 87. 9% of the dataset. Hereafter, we reflect on the interface we proposed. We aimed to improve the previous interface developed with the standard Qualtrics package  [30]. In particular, (1) in the ranking phase, participants could drag and drop the vignettes in their preferred order, instead of writing a number (the rank) in an open box; (2) in the rating phase, participants could see the vignettes ordered according to the rank that they gave, which was not the case in the previous version and was suggested as a cause for the high inconsistency; and (3) in the rating phase, participants used sliders to rate, instead of writing a number (the rate) in an open box.
The default standard options in Qualtrics are not sufficient, and presumably, a more flexible program to code surveys is required. For future research, we strongly recommend setting-up and pre-testing different interfaces before programming the actual survey. Additionally, it is highly recommendable to test different design interfaces in controlled experiments. The two other research questions (RQ3 and RQ4) did not directly lead to recommendations for future online swing weight elicitation. Thus, we discuss them in the next sections.
II. Data and Methodology In this section, we shall provide details on our dataset (section 2. 1) and describe our methodology (section 2. 2) -see Figure  1  for a snapshot of the design of the methodological framework. For analysis purposes, this study uses intraday stock prices of 100 firms listed on Pakistan stock exchange and belong to the KSE-100 Index representing 34 sectors. The data consist of 925,641 transactions recorded from September 12, 2018 to May 31, 2019. Using the high frequency package of R, this transaction data has been categorised into 5-minute frequency and data on stocks with incomplete trading days was discarded. As a result of the cleaning and reorganisation of the initial data, we are left with a dataset of 168 days for 48 companies having 12,415 transactions.
Conclusions and Recommendations In this study, a complex network of intraday KSE-100 index stocks, weighted by jump risk correlations, was built using intraday five-minute high-frequency stock price data from September 12, 2018 to May 31, 2019, and its vertices or stocks analysed by means of centrality measures. Analysis of vertices with the highest betweenness centrality as well as the highest closeness centrality suggest that the two leading car manufacturers of Pakistan (i. e. Pak Suzuki and Indus Motors-Toyota) along with MCB Bank lead the list. Then, a minimum spanning tree (MST) was determined using Prim's algorithm. The connected vertices (companies or their stocks) in the MST shows that a stock price jump in one stock will cause price jumps in other stocks, which are either directly or indirectly connected to it.
Evaluating extent From its inception, the movement for the establishment of PAs focused on expanding their coverage  1. Evaluating total extent, however, requires a global inventory of sites, based on agreed criteria about what constitutes a PA. The world's official inventory is the United Nations List of Protected Areas, first published in 1963. In the early 2000, with the advent of Geographic Information Systems and the internet, this list evolved towards the spatially explicit, publicly available World Database on Protected Areas (WDPA)  5. Currently including 258,389 designated sites  5  , the WDPA rendered it straightforward to monitor progress in PA extent, including not only PA creation but also downsizing and degazettement  15. This facet of PA effectiveness has taken a central place in conservation policy targets: Aichi Target 11 aimed for the protection of 17% of terrestrial area and 10% of the oceans by 2020 (not met  5  ), and an even more ambitious 30% target for both land and seas is anticipated under the post-2020 Global Biodiversity Framework 4.
Evaluating management Possibly as old as PAs themselves, is the concern that they may not be up to expectations. PAs vary in purpose (as reflected in IUCN's PAs management categories, going back to 1978 (ref. 1  )), but there may in addition be a mismatch between the intended outcomes and what is achieved through management. A 2000 WCPA framework set the conceptual basis for assessing the effectiveness of PAs  18  , triggering the development of a diversity of methodologies usually involving questionnaires to PA managers to evaluate management elements of planning, inputs, processes, outputs and outcomes  7. Over 55,000 such evaluations have already been completed and are available through the Global Database on Protected Area Management Effectiveness  19. Analysis of a subset of these evaluations revealed that less than a quarter of PAs reported had adequate resources in terms of staffing and budget  7.
Evaluating threat abatement Many of the world's PAs have ongoing and increasing human pressures  9  , but that does not mean they are not making a difference to abating threats. Investigating this requires contrasting observed threat levels with a counterfactual scenario of 'what would have happened in the absence of protection'. Such counterfactual thinking has only recently started being applied to evaluating the effectiveness of conservation interventions  22. With experimentation not feasible at the scale of PAs, quasiexperimental designs are used instead, whereby counterfactuals are derived statistically by contrasting PAs with suitable control, non-PAs. For some types of habitat loss and degradation, suitable datasets can be obtained from high-resolution satellite imagery (e. g. , on forest cover change over time) showing, for example, that on average, PAs reduce pressures to terrestrial habitats, as seen by lower deforestation rates than in comparable nonprotected sites  10.
Evaluating biodiversity outcomes A counterfactual approach is also needed for evaluating biodiversity outcomes. Again, fine-scale field data are needed, on the distribution of the biodiversity features of interest from both PAs and non-PAs. Suitable datasets are scarce, and highly biased towards high-income nations and better-known taxa (particularly birds). Improving such datasets, particularly in biodiversity-rich tropical regions, is thus crucial to outcome-based evaluations of PA effectiveness. Analysing biodiversity outcomes also requires formalising the facets of biodiversity that PAs are expected to impact  12. Among the few available studies, some report a positive effect of protection on local species richness and overall abundance  11, 23. Others however found no effect on species richness, but a positive effect of some specific groups, like specialist  12, 14  , vulnerable  13, 14  or narrow-range species  14  , consistent with an effect of PAs in avoiding community homogenisation.
5 Chapter 1: Introduction ocean areas and also indicates, in some cases, a lack of specific research based on fair databases or models. In this document, we therefore focus on regions and cases for which the most reliable information is available. This document is organized into ten chapters: • In Chapter 2 we describe and define the diversity of ENSO events. • In Chapter 3 we describe how ENSO events are forecast and identify the current prediction limits on the type, intensity and duration of a given ENSO event. • In Chapter 4 we synthesize the knowledge on the evolution of the ENSO in the context of climate change. • In Chapter 5 we describe the impact of ENSO on ocean conditions and marine fisheries at a global scale. • In Chapter 6 we describe the impact of ENSO on ocean conditions and marine fisheries in different world regions.
This allows the prediction (to some extent) of variation in fish captures in the coming year in the Humboldt Current system  (Figure 3. 2b). This relationship also holds for the tropical Indian Ocean, which is subject to a lagged ENSO signal carried into the Indian Ocean through atmospheric teleconnections (see Section 5). This prediction skill can be extended up to two years in extra-tropical regions such as the north Atlantic and south Pacific, as a consequence of the re-emergence of subsurface nutrient anomalies from one winter to the next. The majority of analyses of ENSO predictability are dependent on hindcast datasets. Since the climate model tuning process in some cases considers the adequate representation of ENSO variability  (Hourdin et al. , 2017; Schmidt et al. , 2017) , such studies could overestimate the actual forecast skill, particularly associated with events with previously unobserved characteristics.
KEy mESSagES • We described known climatic effects of ENSO variation and estimated ENSO effects on aquaculture production across 22 broad geographical regions. • Aquaculture production was largely (2016, 92 percent) derived from different regions in Asia. • In each region there was no significant effect of ENSO event category or El Niño event type on aquaculture production. • However, certain conditions (La Niña and extreme El Niño) were more often associated with larger variation in production anomalies, indicating that they have more scope to produce shocks in the aquaculture sector. We next examined potential effects of ENSO variation on aquaculture production at a regional level (Figure  8. 2). As before, we used FAO summary data on aquaculture production (marine, brackish water and freshwater combined) for countries classified into 22 different global regions, as defined by FAO-FishStatJ (2019b), across which ENSO variation has markedly different effects on climate.
• Put together, the most commonly cultured species showed relatively limited variation associated with ENSO, but plants and molluscs were the most affected groups. Of the different El Niño event types, CP El Niño (plants, molluscs) and extreme El Niño (molluscs, crustaceans, fish) were associated with the largest production anomalies. Modern aquaculture has led to a diverse range of taxa being cultured across the world. In 2016, the FAO database included 438 different categories (FAO-FishStatJ. , 2019b), some representing single species, others representing broader categories where results are pooled by taxonomy or function, e. g. marine mollusc nei (nei = not elsewhere included). In order to examine the potential effect of ENSO variation on production of key aquaculture products, we estimated production anomalies associated with the different ENSO event categories or types of El Niño for the five species/categories making the largest contribution to global aquaculture production for four broad categories that include unfed and fed taxa: aquatic plants (unfed), molluscs (unfed), crustaceans (fed) and fishes (fed).
7 percent). • Production anomalies in land-locked developing countries were most notable during extreme El Niño (-3. 0 percent) and EP El Niño (+3. 1 percent) events. The FAO database (FAO-FishStatJ. , 2019b) included catch data from inland fisheries from 127 different countries/territories in 2016. National fisheries yields (2016) varied between 1 tonne and 1. 45 million tonnes, with a mean ±SD national contribution of 47 342 tonnes ± 180 413 tonnes. Data were available for 341 different species/categories captured from inland waters, with contributions ranging from 1 tonne to 5. 97 million tonnes and a mean ±SD annual yield in 2016 of 33 246 tonnes ± 327 428 tonnes. At a global level (Figure  9. 1), annual inland fisheries yield increased in a linear manner from 1. 9 million tonnes in 1950 to 11.
, 2018; Ward et al. , 2010). It is less simple to define which level of geographical resolution is suitable for such analyses. Capture statistics held in the FAO database for inland fisheries are reported at a national level, not at the level of individual fisheries or basins, which would be optimal. Furthermore, national data aggregates information from a range of different habitat types and fishing gears, each of which may respond differently to ENSO-associated variation. A further issue is that many inland fisheries operate in basins that extend across national boundaries, reducing the utility of national level data. In previous work using FAO-derived data on inland fisheries  (Funge-Smith, 2018; Harrod et al. , 2018a)  it has proved useful to gather information at the level of subregional clusters that include countries that display common characteristics, e.
Acknowledgements The authors would like to express their gratitude to those who provided feedback on this work:  Francis Marsac  (IRD),  Gilles Domalain  (IRD),  Timothée Brochier  (IRD),  François Colas  (IRD),  Patrick Lehodey  (CLS),  Mariano Gutiérrez  (IHMA) and  Lionel Dabbadie  (FAO). Special thanks go to the families of the authors and to FAO for their extreme patience.
Through these determinants, we also seek to document how modelling practices have changed over time, why understanding these changes is important, and to discuss how they may evolve in the future. In a first section, we show that the representation of facts in OEconomia -Histoire | Épistémologie | Philosophie, 10(3) : 521-553 FSM is dependent on features of the target, the forest sector, in particular its boundaries, the nature of the facts it comprises, and the roles played by empirical data and theory. In a second section, we argue that models are shaped by the historical context of their development. We recount how different methodologies were developed since the 1970s, with different goals, and highlight how research up to today has been conditioned by these early developments. In the third section, we focus on how the applied uses of FSM for decision support influences modelling practices.
Representations, Theory and Empirical Data In FSM, different methods are employed to represent facts of different natures, and we want to emphasize the varying role played by theories and empirical data. Natural dynamics in FSM are often represented in "forest inventory projection models"  (Wear and Coulston, 2019). These take the form of transition matrices where volumes of wood or forest areas are measured in biophysical units and categorized into several compartments (e. g. tree size, tree species, ownership categories), while rules describe fluxes between these compartments (e. g. yearly tree OEconomia -Histoire | Épistémologie | Philosophie, 10(3) : 521-553 growth). These categories and metrics correspond to those used by real world forest managers, and empirical data from actual forest inventories is used to calibrate models and make them "fit" to reality: the representation of natural facts is constructed to mimic reality.
Other FSM call on the optimal harvesting framework  (Faustmann, 1849)  to represent timber supply behaviours (e. g. Lobianco et al. , 2016b; Pohjola et al. , 2018). While theory provides the building block for representing economic facts, empirical data also has a role to play. Similarly to natural facts, model calibration uses databases such as FAOSTAT, and the quality of these databases may influence the quality of representations  (Kallio et al. , 2018). When basic calibration fails to fully explain observed patterns, methods such as positive mathematical programming may also be used  (Johnston and van Kooten, 2017). Empirical data also plays a role in estimating model parameters, in particular demand and supply elasticities, which originate from econometric studies based on statistical theory  (Rougieux and Damette, 2018; Sauquet et al.
, 2011). Econometrics then feed FSM, or, seen from the other side, "forest sector models are tools that can translate the behavioural information of econometric studies"  (Toppinen and Kuuluvainen, 2010, 6). Empirical data can also be used to validate FSM and assess the accuracy of simulation results against observed data for a past period. Then, what type of models are FSM? Economists often establish a broad distinction between mathematical models, based on economic theory, and econometric models, which also incorporate elements of statistical theory  (Morgan and Knuuttila, 2012). Even though econometrics play a role in representing the forest sector, FSM are to our opinion more akin to the former. FSM can also be considered computational models in the sense that they are composed of sets of procedures and rules, in particular regarding natural and technological facts, but also because they are used for simulation, "grow their results from the initial conditions," and sometimes do not allow for analytical proof  (Gräbner, 2018, 5).