dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset included data from five environmental subzones and the different soils found within the Rioja AOC. In all, the 'red Grenache' dataset consisted of a total of 950 blade and petiole analyses. The vineyards chosen for this study had mainly nonirrigated and mechanically-tilled soils. Planting density ranged between 2,700 and 3,205 vines•ha -1 , and the training systems were Gobelet, Vertical Shoot Position (VSP) Double Cordon Royat, and VSP Guyot. In general, most of the vineyards had optimal production (higher than 3,000 kg•ha -1 ) and the average bunch weight was 236 g. Grape quality was within the usual values for the Rioja AOC. Average total soluble solid content was 22. 5 ºBrix, pH was 3. 36, and titratable acidity was 6. 87 g•L -1 expressed as tartaric acid. The average cumulative annual rainfall was 451 mm for the period 1992-2008.
40 mmhos•cm -1. Exchangeable bases K, Ca and Mg in soil (1 M ammonium acetate extraction) were determined by flame atomic absorption (Ca and Mg) and flame emission (K) spectrometry. The concentration of bases for the soils included in the dataset was, in general, adequate for grapevine cultivation : Ca ranged between 8. 2 and 22. 2 mmol c •100 g -1 d. w. , Mg ranged between 0. 32 and 1. 53 mmol c •100 g -1 d. w. , and K ranged between 0. 62 and 5. 91 mmol c •100 g -1 d. w. On the other hand, P (Olsen method) was higher than 5. 38 mg•kg -1 d. w.
Statistical data analysis Prior to the analysis, the dataset was checked to eliminate anomalous data due to unhealthy vines, young vineyards less than six years old or outliers (higher or lower than ± 3σ from the average value). Therefore, data from vineyards that could affect the variability due to their age or their sanitary status were ruled out. However, yield and must quality were not used as discriminative factors to eliminate data before the calculation of both SR and DOP references. This was based on the assumption that the objective was to obtain general references for the 'red Grenache' variety and the selected vineyards had, in general, optimal yields for this region. Data were statistically evaluated from a descriptive statistical approach (modified from  Stringari et al. , 1997; García-Escudero et al.
, 2013). The procedure began with the verification of the normal distribution for each nutrient by means of the Kolmogorov-Smirnov non-parametric test, as a prior step to study the distribution of the population as a whole, with respect to the average value and standard deviation. With respect to the SR method, the reference ranges that characterize the different nutritional status of the dataset were delimited by means of μ ± k•σ, where the constant k is calculated for each percentage in normal distributions where the average is 0 and the variance 1. Population was divided into five subgroups, considering the central 20 % population ( μ ± 0. 25σ) as the optimal reference level for each nutrient and 60 % (μ ± 0. 84σ) of the central population to show the populations with higher and lower nutrient contents with respect to the optimal range  (García-Escudero et al.
, 2013). Furthermore, DOP references were obtained from the central value (μ) of the dataset selected for each nutrient. When a normal distribution was not verified for a specific nutrient, data was transformed logarithmically and the SR subgroups and the DOP reference for each nutrient recalculated from the average value of the Log dataset. Finally, when the Log-transformation to normal distribution did not normalize the distribution, the percentiles (P20, P40, P60 and P80) were calculated to obtain the SR subgroups while the DOP reference value which represented the optimal status for each nutrient was calculated using the median of the population, or percentile P50. Data analysis was performed using SPSS (version 15. 0; SPSS Inc. , Chicago, IL, USA).
Comparison of the 'red Grenache' dataset with respect to international references Tables  1  and 2  show the percentage of samples from the 'red Grenache' leaf blade dataset that are within the optimal range according to references published by other authors for different tissues, phenological stages, grapevine cultivars and regions; Tables  3  and 4  show the same analysis for petiole. For leaf blade at flowering (Table  1 ), the dataset optimal percentage for N ranged from 7. 8 % with respect to the Trentino references (Italy)  (Failla et al. , 1993a)  to 98. 8 % of the optimal values when using the Bordeaux references  (Loué, 1990). Phosphorus ranged from 20. 4 to 98. 2 %, K from 19. 8 to 100 %, Ca from 21. 6 to 92. 8 %, Mg from 13. 2 to 97 %, Fe from 23. 0 to 100 %, Mn from 12. 0 to 99.
4 %, Zn from 0. 6 to 18. 7 %, Cu from 0 to 93. 8 %, and B from 2. 5 to 97. 5 % (Table  1 ). 2 ) and leaf petiole at flowering (Table  3 ) and veraison (Table  4 ) also showed strong differences between diagnosis results using international references on the 'red Grenache' dataset.
Leaf blade at veraison (Table For leaf blade at veraison (Table  2 ), the dataset optimal percentage ranged between 18. 5 and 98. 4 % for N, 27. 1 and 100 % for P, 7. 4 and 93. 1 % for K, 22. 8 and 98. 4 % for Ca, 18. 5 and 100 % for Mg, 13. 3 and 100 % for Fe, 23. 0 and 100 % for Mn, 11. 6 and 58. 1 % for Zn, 1. 2 and 100 % for Cu, and 11. 6 and 87. 8 % for B. Therefore, the biggest difference between ranges was observed for Cu between the Italian references  (Bavaresco et al. , 2010)  and the Trentino references  (Failla et al. , 1993a). With respect to petiole at flowering, N ranged from 6. 6 to 97 %, P from 0 to 100 %, K from 26. 3 to 100 %, Ca from 9. 0 to 99. 4 %, Mg from 15. 0 to 100 %, Fe from 4. 4 to 95. 6 %, Mn from 5. 2 to 70. 7 %, Zn from 0 to 59. 3 %, Cu from 20. 8 to 98. 1 %, and B from 1.
Dataset percentages For petiole at veraison, N ranged from 2. 6 to 99. 5 %, P from 17. 5 to 96. 3 %, K from 12. 2 to 96. 8 %, Ca from 3. 7 to 100 %, Mg from 13. 8 to 100 %, Fe from 10. 2 to 99. 2 %, Mn from 13. 3 to 65. 6 %, Zn from 4. 7 to 51. 6 %, Cu from 8. 7 to 71. 4 %, and B from 17. 2 to 100 %. The largest difference between dataset optimal percentages was observed for N using 'Tempranillo' references  (García-Escudero et al. , 2013)  and Bordeaux references  (Loué, 1990). Considering the Bordeaux references  (Loué, 1990) , 99. 5 % of the 'red Grenache' dataset would be in an optimal nutritional status for N, while using the 'Tempranillo' references 97. 4 % would be in a non optimal nutritional status, in spite of the fact that 'Tempranillo' references were suggested for the same region as the 'red Grenache' dataset.
Dataset percentages Table  4  -Dataset percentages of nutrient concentration in petiole at veraison of 'red Grenache' within the optimal values for different cultivars and/or regions. † Fráguas et al. , 2003; ‡ García-Escudero et al. , 2013; § Bavaresco et al. , 2010; ¶ Loué, 1990; d. w.
Dataset percentages Fe and B in leaf blade at veraison, P and Fe in petiole at veraison, and K, Fe, Mn, Zn and Cu in petiole at flowering. Finally, P20, P40, P60 and P80 were calculated in cases where the Log-transformation to normal distribution was unlikely. That was the case for Mg and Zn in leaf blade at flowering and P, Mg, Zn and Cu in leaf blade at veraison. For leaf petiole, percentiles were calculated for Mg at flowering as well as for K, Ca, Mg, Cu and Mn at veraison. The calculated percentiles were used to define the 20 % and 60 % of the central data. The sufficiency ranges for 'red Grenache' grapevine calculated for ten essential elements in leaf blade and petiole at flowering and veraison are shown in Tables  5 to 8.
Comparison of the 'red Grenache' dataset with respect to international references It should be considered that all the surveyed grapevines included in the dataset were of good productive, sanitary and vegetative status. Thus, low diagnosis coincidences are incongruent with respect to a dataset that corresponds to representative 'red Grenache' vineyards. The comparison shows important differences. For example, the biggest difference in the interpretation of the nutrient status of the 'red Grenache' dataset, using blade at flowering, was observed for B with respect to references from Trentino  (Failla et al. , 1993a)  and Australia  (Weir and Cresswell, 1993). According to these references, the 'red Grenache' dataset would be in an optimal nutritional status for B when using references given for Australia  (Weir and Cresswell, 1993) , while its status would be non optimal when compared to Trentino references  (Failla et al.
, 1993a)  (Table  1 ). The high variability found for the dataset comparisons indicates the importance of using adequate references to determine the nutritional status of a determined cultivar. Thus, references must be adapted to each variety, soil and climatic condition, even within the same winemaking region. This fact has also been highlighted by other authors  (Failla et al. , 1993a (Failla et al. , , 1995;; Ciesielska et al. , 2002). In general, the Bordeaux references  (Loué, 1990)  covered a greater number of optimal cases for all nutrients, while the Spanish references for 'Tempranillo'  (García-Escudero et al. , 2013)  covered fewer cases (Tables  1 2 3 4 ). This is due to a broader optimal range of nutrient concentration in Bordeaux references, while 'Tempranillo' references have more limited and perhaps sensitive optimal ranges, specific to a variety with an earlier maturation and senescence than 'red Grenache'.
SR and DOP reference concentrations The database was split to assure that SR and DOP references would be specific to the combination Vitis vinifera L. cv. red Grenache grafted on Richter 110 and to reflect the total production and agro-climatic conditions of the region where references had been obtained. The SR method divided the data population into five classes (low, below optimal, optimal, above optimal and high) considering the central range as the optimal nutritional status, whereas the higher and lower ranges of nutrient content with respect to the optimal range suggest that a corrective fertilization program should be considered. However, the use of Cu-based products in vineyards for phytosanitary purposes prompts the adsorption processes of Cu by the leaf surface. This adsorption increased the total Cu concentration analyzed and therefore a Cu reference with a real physiological meaning for DOP as well as for SR was not obtained.
This fact is critical at veraison as by then the  phytosanitary applications have already started. The CV(%) for Cu's DOP references shows the high variability found for both tissues at veraison (Table  9 ). Each nutrient reference for the DOP method is the mean or centred value, which represents the dataset population for each nutrient as a whole (Table  9 ). Concentrations below or above the DOP reference value, for each nutrient, produce negative or positive DOP indexes, respectively, and therefore a corrective fertilization program or a reduction of the nutrient in the fertilization programs must be considered. The lower CV(%) of DOP references for blade suggests that blade has a higher sensitivity than petiole to show deficiencies or excesses of N, P, K, Mn, and Zn at both flowering and veraison stages, Mg at flowering, and Ca and Fe at veraison.
CONCLUSION SR and DOP references are proposed for the combination Vitis vinifera L. cv. red Grenache grafted on Richter 110 in order to reflect the nutritional status of vineyards under similar production and agro-climatic conditions as the region where references have been obtained. The proposed references will be a useful tool to assess the nutritional status of 'red Grenache' around the world, but they will be less accurate when the rootstock, the variety or the regional conditions of the studied vineyard differ from the ones selected to create the dataset ; therefore, a proper use of the references requires a technician to evaluate the differences regarding soil, climate, rootstock, as well as other factors. Finally, references for 'red Grenache' will be enhanced in the future because of a continuous increase of the foliar database as well as the introduction of new criteria in the calculation, such as must quality and/or different yield ranges.
Acknowledgements : This study was supported by the  National Institute of Agricultural Research (Spain)  and the  Regional Government of La Rioja (Spain)  with Projects  INIA-SC00-016 ,  PR-01-03 ,  PR-01-04  and  PR-03-05 , among others. We also thank  Mrs. M. Carmen Arroyo , the staff at the  Regional Laboratory of La Grajera (La Rioja ), the staff at the  Viticulture and Oenology Section (SIDTA-ICVV) , and the vine growers who helped develop the database.
48-3. 71 0. 18-0. 48 0. 61-1. 94 0. 94-2. 65 0. 13-0. 46 - - - - - Dataset percentages Number of cases 167 167 167 167 167 161 167 155 128 157 Trentino † 7 8 4 2 5 1 9 8 6 3 5 5 3 3 100 99 4 5 8 9 3 8 2 5 Italy ‡ 37 7 4 9 1 8 8 6 8 0 2 6 8 3 9 3 2 9 5 2 1 8 7 2 7 3 6 3 1 North-central Italy § 60 5 6 8 3 88. 0 61,7 68 9 72. 0 80 8 3 9 0. 0 8 3 Australia ¶ 57 5 5 2 1 4 0 7 9 2 8 5 3 9 - 9 7. 0 0 6 2 7 3 9 7 5 cv. Tempranillo (Rioja) † † 16 2 2 0 4 2 6 3 2 1 6 1 3 2 23. 0 12. 0 18 1 1 5 6 2 6 1 Bordeaux ‡ ‡ 98 8 9 8 2 100 89 2 97. 0 - - - - - † Failla et al. , 1993a;‡ Bavaresco et al. , 2010;  § Failla et al. , 1993b;¶ Weir and Cresswell, 1993;† † García-Escudero et al. , 2013;‡ ‡ Loué, 1990; d. w. , dry weight; -, data not available.
La visualisation des semis de points en 3D est faite ici par le biais du logiciel NetLogo3D  (Wilensky, 1999). Bien que cet environnement de programmation soit principalement destiné à la simulation mutli-agents, NetLogo3D permet aussi de positionner des agents (ici des arbres de tailles et d'essences différentes) en 3D. Si la dimension fractale est un indicateur très utilisée en géographie pour caractériser la structure d'objets surfaciques (structures urbaines par exemple), elle l'est en revanche beaucoup moins pour les objets ponctuels comme les semis de points. Or, les semis de points sont des éléments importants dans les structures géographiques. Ces points géométriques sont des objets et/ou des lieux géographiques qui peuvent faire l'objet d'une analyse spatiale afin de répondre à des questionnements concernant leur localisation, leur distribution ainsi que leurs interactions  (Pumain & Saint-Julien, 2010).
Empirical strategy Our goal is to empirically investigate the e¤ect of emigration on the quality of institutions in the sending country. We will use several indicators of institutional quality, I t , and measures of openness to emigration, m t , available for origin country i = 1; :::; N and year t = 1; :::; T. In our benchmark regressions, the emigration rate is computed as the sum of emigrants from country i to OECD destination countries j at time t, P j M ij;t , divided by the native population of country i, N i;t. In this section we present our empirical model, discuss a number of econometric issues and describe the data sources used for the empirical analysis.
Data Our data set is a …ve-year unbalanced panel spanning the period between 1985 and 2010, where the start of the date refers to the dependent variable (i. e. , t = 1985, t 1 = 1980). In our sample, we are considering only developing countries (according to the World Bank Classi…cation), and they enter the panel if they are independent at time t 1. The country sample is selected on the basis of the availability of the data described in subsection. Table  A3  in Appendix A presents the list of countries in our sample (corresponding to the largest number of observations in panel speci…cations). Democracy. Data on democracy are taken from the Freedom House data set, from the POLITY IV data set, and from the Economic Freedom of the World Project. The Freedom House published the poliitical rights (PR) and civil liberties (CL) indices.
Another measure of democracy from the POLITY IV data set is also considered. Indicators of democracy measure the general openness of political institutions and combines several aspects such as: the presence of institutions and procedures through which citizens can express e¤ective preferences about alternative policies and leaders; the existence of institutionalized constraints on the exercise of power by the executive power; and the guarantee of civil liberties to all citizens in their daily lives and in acts of political participation. In our data set we consider a composite index (Polity2), that ranges from -10 to + 10. This index is also normalized from 0 to 1. Note that while the "political rights" and "civil liberties" indices are based on public perception measures and can therefore be seen as a re ‡ection of contemporaneous de facto institutional quality, the Polity 2 indicator is based on expert coding of legal documents and can therefore be interpreted more as a de jure measure.
11 inally, we also consider Economic Freedom of the World (EFW), an index which measures the degree to which countries' policies and institutions support economic freedom. Five broad areas are distinguished: (1) size of government; (2) legal structure and security of property rights; (3) access to sound money; (4) freedom to trade internationally; and (5) regulation of credit, labor and business. This index is also normalized between 0 and 1. The ratings are determined by combining real indicators (such as "size of governement", taken from IMF) with answers to survey questions on other modules (such as "independence of the judicial system" taken from perception reports -e. g. , the Global Competitiveness Report form the World Economic Forum, or "regulatory restrictions" taken from the World Bank's "Doing Business" database).
Migration. For emigration data, we use the estimates provided in Bruecker, Capuano, and Marfouk (2013). Focusing on 20 OECD destination countries, they computed emigration stocks and rates of the population aged 25 years and older by gender and educational attainment in 5-year intervals from 1980 to 2010. Data are obtained by harmonizing national censuses and population registers statistics from the receiving countries. On the whole, the 20 destination countries covered represent more than 90 percent of the OECD total immigration stock. Other data. Data on human capital are based on Barro and Lee (2013). Data on GDP per capita, population, trade, and o¢ cial development assistance (ODA) are taken from the Penn World Tables and from the World Development Indicators. Data on legal origins are from La Porta et al.
Cross-sectional analysis Tables 1. a to 1. d report OLS and 2SLS estimates for the long-run speci…cation (2) using data for 2005 for all variables. Standard errors are robust and clustered by country. In OLS regressions (column 1), the estimated coe¢ cient of the emigration rate is positive and statistically signi…cant for each indicator with the exception of the Freedom House Political Rights Index (PR). In columns 2 to 8, we correct for endogeneity using 2SLS regressions. The emigration rate is instrumented using predicted bilateral stocks generated by a pseudo-gravity model, as explained above. 13 The baseline regression in column 2 shows that the e¤ect of emigration is positive and statistically signi…cant for all indicators. Compared with OLS, the coe¢ cient of the 2SLS regression is larger.
METHODS The source of the data analyzed in the present study was from the Ontario HIV Treatment Network Cohort Study (OCS)  (16). The cohort's source population consists of individuals ≥16 years of age diagnosed with HIV infection who receive medical care at specialty HIV clinics. The 10 participating clinics serve more than three-quarters of HIV patients undergoing viral load testing in the province. Enrollment and ongoing participation in the cohort was voluntary. All participants provided written informed consent. Clinical data obtained as part of participants' routine health care were abstracted from clinic records. From 1995 to 2007, participants self-completed a questionnaire at enrollment. Since 2008, participants have been interviewed annually  (16). The study protocol, research instruments and forms received ethical approval from the University of Toronto Human Subjects Review Committee (Toronto, Ontario) and from the individual study sites.
Measurement of coinfection Testing data were obtained for HIV viral load, HCV and syphilis through record linkage with the provincial Public Health Ontario Laboratories (PHOL). In Ontario, serological screening for hepatitis virus infections can be performed by private laboratories, hospital laboratories or at the PHOL. The PHOL conduct virtually all confirmatory HCV diagnostic serology in Ontario and virtually all HCV-RNA viral load monitoring among the chronically infected. The PHOL is the sole provider of HIV viral load and syphilis serological tests  (17).
Analysis As of December 2011, 5933 participants had enrolled. For the present study, the cohort was limited to 3453 male participants who reported sex with men as their HIV risk factor and/or who self-identified as homosexual or bisexual, did not report injection drug use as their HIV risk factor and did not report drug injection following HIV diagnosis. The cohort was further limited to 2761 men who were under observation in 2000 to 2010 and for whom there was successful record linkage to the HIV viral load database at the PHOL, meaning that any confirmatory or follow-up HCV testing should have been observable. Finally, the cohort was then limited to 1534 men who had at least two linked HCV test records from the PHOL, for which the first was HCV negative; χ 2 and t tests were used to compare the characteristics of the 1227 men who did not meet this criterion with the 1534 men who did.
All statistical analyses were conducted using SAS version 9. 3 (SAS Institute Inc, USA). All P values were two-sided and statistical significance was determined using the traditional P<0. 05. The incidence density of HCV seroconversion per 1000 personyears (PY) of follow-up was calculated. Person-time was calculated for each subject beginning at the later of the first HCV-antibody negative result, HIV diagnosis date, or January 1, 2000, and ended at the earlier of the last known date of follow-up, date of death (if applicable) or, for cases, the date of the first HCV-antibody positive test. Similarly, the annual incidence density was calculated as the number of new HCV diagnoses during each calendar year per 1000 PY of follow-up in that year. Poisson regression was used for all incidence density calculations and these were reported with 95% CIs.
ACKNOWLEDGEMENTS: The OHTN Cohort Study Team consists of  Dr Sean B Rourke  (Principal Investigator,  University of Toronto  and OHTN),  Dr Ann Burchell  (Co-Principal Investigator, OHTN),  Dr Sandra Gardner  (OHTN),  Dr Sergio Rueda  (OHTN),  Dr Ahmed Bayoumi  and  Dr Kevin Gough ,  St Michael 's Hospital;  Dr Jeffrey Cohen ,  Windsor Regional Hospital ;  Dr Curtis Cooper ,  Ottawa General Hospital ;  Dr Don Kilby ,  University of Ottawa Health Services ;  Dr Mona Loutfy  and  Dr Fred Crouzat ,  Maple Leaf Medical Clinic ;  Dr Anita Rachlis  and  Dr Nicole Mittmann ,  Sunnybrook Health Sciences Centre ;  Dr Janet Raboud  and  Dr Irving Salit ,  Toronto General Hospital ;  Dr Edward Ralph ,  St Joseph 's Health Care;  Dr Roger Sandre ,  Sudbury Regional Hospital ;  Dr Marek Smieja ,  Hamilton Health Sciences, McMaster University Medical Centre ; and  Dr Wendy Wobeser ,  Hotel Dieu Hospital.