dataesr/scientific-paragraphs-categorization huggingface dataset:
The ones are all associated with "high recall" combinations of high threshold values for indicator n. 1 and low threshold values for nr. 2 and nr. 3 (such as, respectively, 70-5000-60; see figure  1 ), while the zeroes will be associated with "high precision" combinations (low threshold values for indicator n. 1 and high threshold values for nr. 2 and nr. 3; such as, respectively, 30-8000-80). Rajiv Laroia will be confirmed having CoO=India only in the high recall case, but not in the high precision case (for which indicator nr. 1 is too high). In practice, the high precision combination leaves the door open to Rajiv Laroia's CoO being the UK, and to Rajiv Laroia being possibly of Indian descent, but with no ties to India or to Indian migrants in the US. A -48 We further check the reliability of our data by comparing them to both WIPO-PCT data (which, as said above, provide information on nationality of inventors) and to estimates by  Kerr (2008) , who also uses a namebased ethnicity assignment algorithm, based on a different source than IBM-GNR (and for a more limited spectrum of countries of origin).
i)   foreign nationality, 1995-2005 (ii)  z (iii)  p-value (iv)   (  (i) source: EP-INV database (ii) source: WIPO-PCT dataset (see  Miguelez and Fink, 2013). ( iii) Normalized difference between (1) and (2) (iv) p-values for z-test on H0 : (1) = (  2 ) Still, we observe cross-country variations that may be due either to lack of precision in the Ethnic-INV algorithm or to differences in the propensity to acquire nationality for each migrant community (this in turn may be due to the average time spent by the migrants in the US, the number of US-born second-generation migrants, and the frequency of mixed marriages). In particular, we notice larger differences, in relative terms, for Germany, Italy, and Poland, where the share of foreign nationals is about double the share of foreign-origin inventors.
Unique identifiers for inventors in the EP-INV database come from the  APE-INV  project (Academic Patenting in Europe), funded by the  European Science Foundation. The pilot project for assigning inventors to specific countries of origin was funded by the  World Intellectual Property Organization (WIPO) , which also made available to us the WIPO-PCT dataset. We received useful suggestions by participants to the following conferences :  MEIDE (Santiago de Chile , November 2013),  PATSTAT  ( Rio de JaneiroDiego Useche  provided valuable research assistance. We owe the tip on the IBM-GNR system to  Lars Bo Jeppesen , while  Curt Baginski  assisted us in its implementation. Lissoni and  Miguelez  acknowledge financial support from the  Regional Council of Aquitaine  ( Chaire d'Accueil programme  and  PROXIMO  project).
This is the main issue we address here. We propose to conduct a meta-analysis of the economic value of ecosystem services provided by lakes. We wish to identify if there exists a valuation function that relates the ecosystem service value of a lake to its physical, economic and geographic characteristics. Our analysis relies on the most extensive global database of non-market and market valuations of ecosystem services provided by artificial and natural lakes (699 values extracted from 133 studies). We argue that the results of this meta-analysis might be useful for decision-making. First, there remain substantial debates on the economic value of ecosystem services provided by lakes  (Magat et al. , 2000; Viscusi et al. , 2008; Sander and Polasky, 2009). A good understanding of the physical, economic and geographic characteristics of lakes upon their economic value may inform decisions related to their use, conservation or restoration.
Building of the Meta-Database The scientific references have been selected through systematic searches of the keywords "Valuation and Lake", "Value and Lake", "Willingness to pay or WTP and Lake", "Stated preferences and Lake", on search engines and on the websites of major publishers of academic journals (Scopus, Science Direct, Wiley, Web of knowledge, RepEc, AgEconSearch). Similar searches were also conducted on databases specialized in environmental valuation. Lastly, the "grey literature" was searched using various search engines (including Google Scholar and Science. gov). This is important to reduce the influence of a potential publication bias in the meta-regression analysis. In total, the literature search process took about one year (December 2013-December 2014) and it led us to examine over 300 studies.
Based on their abstract, studies have been first classified as irrelevant, potentially relevant and relevant. Irrelevant studies (studies without any reference to one or several lakes or those which did not report any economic valuation results) where disregarded at this first step. Further investigations were then conducted on potential relevant studies in order to reclassify them either as irrelevant or relevant. Lastly, all studies considered as relevant were downloaded and an additional screening process was conducted to decide if they had to be included or not in the final database. This selection procedure led us to retain 133 studies, see Supporting Information S2 for the full reference list. A vast majority of the database is made of peer-reviewed articles (110 studies), the second category the most represented being institutional reports (11 studies).
Studies are quite recent on average. Among the 133 studies of the database, 58 have been published after 2010, 52 between 2000 and 2010 and the remaining before 2000. All continents are represented in our database, with an overrepresentation of North-America. North-America ranks first with 71 studies (68 studies deal with United States). The second continent the most represented is Europe with 30 studies. As already mentioned, United States are by far the country for which we have the most of studies. This may result from a selection bias since our systematic searches for lake valuation studies have been done in English. It may also reflect the fact that hedonic price approaches have been extensively used in this country for valuing housing amenities. A single study may report multiple lake values, either because several lakes are considered or because of the use of several valuation methods or scenarios.
Ecosystem Services Provided by Lakes For each primary valuation study (or for each observation in case of multiple observations per study) we have identified the ecosystem services provided by the considered lake. In total, we have gathered economic values for 12 different ecosystem services provided by lakes, see Fig. 2. These services belong to three categories of ecosystem services (provisioning services, regulation and maintenance services, cultural services) presented in Supporting Information S3. Not surprisingly, the vast majority of ecosystem services for which a lake value is associated with belongs to the cultural service category. We have categorized the cultural services in our database slightly differently compared to the list presented in Supporting Information S3. In particular, the "recreation service" has been split into several sub-services: fishing, boating, swimming, camping, sightseeing and unspecified recreational services.
In addition, the "amenity" sub-service has been created for studies based on the hedonic price (HP) approach. As explained in  Lansford and Jones (1995) , an HP study of shoreline and "near-the-lake" properties captures an important component of the recreational and aesthetic values that are provided by the existence of such a lake. There is however no direct correspondence between these amenities and the cultural service category defined in Supporting Information S3. In our database, among the cultural service category, the "amenity" service ranks first (244 observations) followed by the different recreational services such as "fishing" (265 observations) and "boating" (183 observations). For provisioning services, we have only 25 observations for the "water for drinking" and the "water for non-drinking purposes" services.
Accounting for Heterogeneity in Space and in Time In our meta-database, lake values have been obtained for different countries (30 countries) and for different periods of time  (from 1972 to 2012). This requires some normalization procedures. Following  Ghermandi et al. (2010) , differences in purchasing power among countries have been accounted for by using the Purchasing Power Parity (PPP) index provided by the PennWorld Table. As a result, all currencies have been converted in USD PPP. Second, the problem of having different years of observation is usually solved by using appropriate price deflators, see  Ghermandi and Nunes (2013)  for a recent example. Values reported for price levels other than 2010 have been converted using national customer price indexes (CPI) provided by the International Monetary Fund (World Economic Outlook 2014).
, 2006). For HP studies, no monotonic relationship seems to emerge between the lake value per respondent and its size. This is consistent with previous findings reporting constant returns to scale with respect to size for some ecosystem service values. Indeed, both  Brander et al. (2006)  and  Woodward and Wui (2001)  conclude that the economic value of wetland services is not significantly influenced by the size (area) of wetlands, i. e. that wetland values exhibit constant returns to scale. When considering studies not relying on HP, the picture is quite different. We find in that case a positive relationship between the size of the lake and its value. This may indicate the presence of increasing return to scale but the result may also be related to the fact that the biggest lakes in our meta-database are located in the United States (Lake Michigan, Lake Erie and Lake Ontario), a country for which we would expect a priori high values for lake ecosystem services.
In Fig. 5 , we have split the annual value of a lake according to the presence or not of a specific ecosystem service (and still according to the valuation method used in the primary study). Values reported in this figure should not be interpreted as the value for the considered service since each lake in our meta-database provides often more than one service (on average 2. 2 different services per observation). This figure calls for a few comments. In our meta-database, cultural ecosystem services provided by lakes are highly valued. This is particularly true for the "spiritual and symbolic appreciation" and for the "amenity" services. Interestingly, the two regulation and maintenance services in our meta-database (i. e. "flood protection" and "maintaining populations and habitats") provide relatively high economic values.
The third set of moderators, X b , includes some characteristics of the water body (natural lake, lake size, special area or Unesco World Heritage site). The last group of moderator variables X c , includes spatial context variables supposed to have an influence on the willingness to pay, but which were not directly available from the primary studies. Johnston et al. (2016)  provide a recent example of this type of "augmented" meta-analysis for the case of water quality in the United States. Our choice of additional spatial variables has been primary guided by economic theory (which predicts which factors are supposed to influence willingness to pay) and by data availability at the global scale. Since we expect a relationship between household wealth and willingness to pay, we has first introduced in the metaregression the GDP per capita.
Second, since willingness to pay has been shown to be related to water resource pressure and to water scarcity we have introduced three geospatial variables measuring water stress, variability in water and risk of drought. These variables have been extracted from GIS layers developed within the Aqueduct project of the World Resources Institute, and they are defined at the river basin level  (Masutomi et al. , 2009). Third, we expect the economic value of an ecosystem service provided by a lake to be related to the geospatial availability of lake substitutes  (Schaafsma et al. , 2012). Using the Global Lakes and Wetlands Database, we computed a measure of lake abundance defined as the number of distinct 2 Erosion and flood control have been excluded due to the very limited number of observations where these services are present.
(2006)  have reported a positive coefficient for contingent valuation studies. Peer reviewed studies tend also to produce higher lake values which suggests a publication bias and supports the inclusion of grey literature in the meta-analysis. Our result contradicts  Chaikumbung et al. (2016)  who report lower values for estimates of ecosystem services published in journal articles, but is in line with results provided by some other studies  (Ghermandi and Nunes, 2013). Lastly, studies differ depending upon the type of scenario used for inferring the lake value but the impact of these scenarios on estimated lake values remains very limited. In model ML4 and ML5, we have added some characteristics describing the type of water body (natural versus artificial, size of the water body) and we have augmented the database with external and geospatial data extracted from GIS layers.
Conclusion While there is a widespread recognition that lakes provide valuable ecological services, there remain substantial debates on their economic value. This lack of consensus may come from the fact that lake valuation studies are extremely diverse in terms of type of value estimated (i. e use and non-use values), type of lake considered and valuation method used. As a result, lake ecosystems are often undervalued in decisions related to their use, conservation or restoration. In this paper, estimates of values attached to different ecosystem services provided by lakes have been compared and synthesised in a meta-analysis using the most extensive global database of nonmarket and market valuations of ecosystem services provided by lakes. The meta-analysis provides insights into the factors that have to be considered when attempting to transfer lake values on the basis of the valuation studies.
From a methodological point of view, instead of relying solely on information retrieved from the primary studies, we have introduced spatial context variables expected to have an influence on lake values. More specifically, we have augmented our meta-database with external spatial variables extracted from geographic information systems. One of the key results of our meta-regression analysis is the relevance of some of these spatial variables (i. e water variability or lake abundance) in explaining variation in lake values. In terms of empirical results, we have provided an estimation of the average value of ecosystem services provided by lakes (between 106 and 140 USD$2010 per respondent per year for NHP studies and between 169 and 403 USD$2010 per property per year for HP price studies) and we have offered insights on how people value different lake ecosystem services.
Acknowledgments This work belongs to the institutional program of the  Joint Research Centre  of the  European Commission. The authors would also like to express their gratitude for the funding received under the  FP7-ENVIRONMENT European Mars project  (grant agreement no:  603378 ). We thank  Alia Gizatulina  for a careful reading of this manuscript, and we are grateful to the four anonymous referees of  Ecological Economics  for their valuable comments.
In the final step, we aim at measuring the effect of compliance with Basel III on banks' default risk. One problem is that banks would need to optimally trade off several regulations, and thereby, the final impact on default probabilities depends on how banks achieve the balance sheet adjustments needed for compliance. To account for these trade-offs and substitution effects among the different balance sheet components, we simulate a model of bank behavior where banks maximize their profits subject to their balance sheet constraint, as well as to the new Basel III regulations. Thus, for each bank and each period, we obtain simulated balance sheet data (e. g. , simulated CAR, high-quality-liquid assets) from 2003q2 onwards. We then use our regression results to predict the Z-score and ROA based on our simulated data.
3 This dataset is confidential and cannot be distributed. Other available datasets provide a level of granularity far below the one required for a consistent estimate of both ratios. Our main results are as follows. In our econometric analysis, we find that the new regulations induce a statistically-significant decrease in banks' probabilities of default. The disaggregated analysis on the components of the measure of distance-to-default allowed us to identify channels through which Basel III would mainly impact. We show that the impact on default risk would essentially be driven by the regulatory limits to the leverage ratio (i. e. , inverse of CAR) and the components of the NSFR. Indeed, our findings point out that a higher NSFR relates to a higher level of bank capitalization for those banks that have a sufficient amount of stable funding, and it also relates to a higher return-on-assets.
Moreover, considering that we have a panel dataset with 35 periods, the dynamic-panel bias may be low  (Judson and Owen 1999). In such a case, a seemingly unrelated regression estimator on the transformed equations (i. e. , first difference or Helmert orthogonal transformation) would produce unbiased estimates. Thus, as a robustness check-up and in order to take into account the information conveyed by the non-linear constraints on the β coefficients in the system of equations, we resort to a two-step feasible generalized non-linear least squares estimator 17.
Conclusions In this article, we contribute to the recent discussion on the usefulness and effectiveness of the Basel III regulations by analyzing how the capital-to-assets ratio, the liquidity coverage ratio and the net stable funding ratio are likely to impact banks' distance-to-default. We used the Z-score index as the indicator of banks' distance-to-default. We have also studied the components of Z-score, namely ROA and CAR. This approach allows us to account for the endogenous relationship between banks' profitability and capitalization and then to provide an accurate picture of the potential benefits (i. e. , reduced default risk) and costs (i. e. , reduced profits) of the Basel III regulations for the Luxembourg case. We focus on a unique panel dataset based on banks in Luxembourg covering the period 2003q2-2011q3.
Experimental Results To assess the impact of above and belowground interactions, we performed simulations on three scenarii: scenario Above gives a significant importance to above interactions by multiplying the effect values related to shade by 10, similarly scenario Below attaches a great importance to roots by multiplying their effect values by 10 and the last scenario Equilibrate ascribes equal importance to the impact of both above and belowground interactions. We solved these scenarii using IBM ILOG cplex v12. 7 with its new fully-automatic Benders decomposition strategy for the MIP formulation and with default options for the BQP formulation, except saving the branch-and-bound tree to disk and using 4 cores of an Intel Xeon CPU E5-2680 at 2. 5 GHz with 256GB running Linux Debian 8. 7.
26 In a study of patient and physician perspectives on the use of alirocumab, both physicians and patients responded positively to use of an injectable device, suggesting that alirocumab treatment by either prefilled pen or syringe would not deter its use in patients requiring additional LLT. 27 However, long-term adherence to self-injected alirocumab has not been rigorously assessed. In this analysis, data from 6 studies within the ODYSSEY Phase III program were used to explore treatment adherence to alirocumab over a period of at least 1 year. 1 ). 9] [30] [31]  In short, FH I, FH II, and HIGH FH evaluated alirocumab in patients with HeFH,  28, 29  whereas COMBO I and COMBO II investigated alirocumab in patients with high CV risk. 19, 30 LONG TERM assessed alirocumab in patients with HeFH or those at high CV risk.
Endpoints Treatment adherence Patient adherence to treatment with alirocumab or control agents was based on information recorded by the patient in a specified patient diary. For each injection received, patients were required to record the date and time of the injection and treatment kit number. These data were then entered into electronic case report forms (eCRFs) at the study sites; the study monitor crosschecked the consistency between information recorded in the eCRFs against that recorded in patient diaries and against the number of returned unused syringes of a corresponding kit. Adherence to injections was reported as the percentage of days that patients received injections according to the dosing schedule. Patients' adherence levels to injections were divided into 4 categories: 100% adherent, those with below-planned dosing, those with above-planned dosing, and those with both below-and above-planned dosing.
Statistical considerations Adherence and safety data were analyzed using the safety population (randomized and treated) and were summarized using descriptive statistics. The percentage change from baseline to Week 52 in calculated LDL-C was analyzed using the modified intent-to-treat population, defined as all randomized patients who took at least 1 or part of a dose of the study drug and had an evaluable primary efficacy endpoint during the efficacy treatment period (on-treatment approach). Missing data at Week 52 were imputed using the last value on-treatment.
Effect on LDL-C The percentage change from baseline to Week 52 in calculated LDL-C in alirocumab-treated patients only (ie, excluding the control arms) was evaluated according to the 4 treatment adherence categories and alirocumab initial  dose, using an on-treatment approach (on-treatment LDL-C values). A total of 2738 patients in the alirocumab arm had data for LDL-C during the on-treatment period. The mean percentage change from baseline to Week 52 in calculated LDL-C ranged from 245. 8% to 261. 9% and was broadly comparable across all 4 treatment adherence categories (Fig. 3 ). A total of 1148 patients were initiated on alirocumab 75 mg Q2W, whereas 1590 patients were initiated on alirocumab 150 mg Q2W (LONG TERM and HIGH FH). Of those initiated on 75 mg alirocumab Q2W, 25. 1% to 30. 6% received dose increase to 150 mg alirocumab Q2W at Week 12 (Fig.
Safety analyses All 4212 patients were included in the safety analysis. Pooled safety data from the 6 studies are shown in Tables  5 6 7. The rates of TEAEs, treatment-emergent serious adverse events, and TEAEs leading to discontinuations or deaths were generally comparable between alirocumab and control arms (Table  5 ). The most common TEAE leading to treatment discontinuation in the alirocumab group was myalgia, occurring in 6 (0. 3%) and 3 (0. 6%) patients in the placebo-and ezetimibe-controlled pools, respectively (Table  6 ). Within the control groups, myalgia was also the most common TEAE leading to discontinuation in the ezetimibe group (2 patients [0. 8%]); whereas back pain was the most common TEAE leading to treatment discontinuation in the placebo group (4 patients [0. 3%]). There were no cases of myalgia leading to  treatment discontinuation in the placebo group (Table  6 ).