dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , the time spent within or outside the residential neighborhood, itself linked to daily mobility patterns, and the range of difference between residential and non-residential environments may vary according to individual profiles. This means that magnitudes of misclassification in measures of exposure based on home environments only may be related to other traits such as age, socio-economic status, occupation, or gender  [14, 15] , resulting in biased effect estimates. These two arguments call for a change in how research on place effects is conducted. We provide a number of recommendations to incorporate such change. First, spatial data on daily mobility should be collected more often and more systematically to allow specific estimates of exposures in multiple destinations and along routes.
Reliable aging techniques for wild animals are notoriously challenging to develop because of the scarcity of sizeable collections of known-age specimens. Without such techniques it is difficult to reconstruct hunting patterns, which is a significant problem for the examination of assemblages from pre-farming cultures. This paper presents a new method, based on mandibular tooth eruption and wear, for assessing the age of fallow deer. The method was developed from a large collection (n = 156) of known-age Dama dama specimens, has been blind tested by members of the zooarchaeological community and represents a user-friendly system with the potential to generate large compatible datasets through which the dynamics of human-Dama relationships can be examined.
Assessing age-related variations in crown heights Where the teeth were fully erupted, crown height measurements were taken for the deciduous fourth premolar (Dp4), the permanent fourth premolar (P4) and the first, second and third molars (M1, M2, M3). These crown height measurements were taken on the lingual surface, from the cementum-enamel junction to the highest point of the crown (Figure  1 ). The relationships between crown height and age are shown in Figure  2a-e , which present the crown height data for each individual tooth plotted against the specimen's age, with linear regression line also displayed. The strongest correlation between age and crown height is seen for the M1 (Figure  2c ), the regression line having a R 2 value of 0. 6755. By contrast, the M3 (Figure  2e ) shows surprisingly little age-related variation in crown height, with an R 2 value of just 0.
Archaeological and deer management applications The methods presented above were applied to archaeological Dama mandible bones collated as part of the AHRC-funded Dama International Project: this includes physical specimens as well as photographic records sent by researchers from across Europe. Data for 77 archaeological fallow deer mandibles were acquired from a variety of archaeological periods (Neolithic to Mediaeval) and geographical regions (Mediterranean to Britain). These were complemented by 104 modern specimens recovered from a 'poaching cache' on the island of Barbuda in the Caribbean, where fallow deer is the national animal. Despite the small sample size for the archaeological material, the results are highly interesting. As can be seen from Table  4  the Neolithic material derives exclusively from animals aged over 20 months of age (MWS F or above).
Conclusion Up to now, in the absence of a standardized dental aging method for fallow deer, examinations of human-Dama relationships have been restricted and detailed studies concerning hunting and management strategies have not been possible. By developing a tested system based on collections of known-age fallow deer, the opportunities for examining the bio-cultural history of this species are now considerable. The ability of researchers to assign stages of mandible wear consistently opens up the possibility of generating large compatible datasets that have the potential to inform on hunting strategies and the dynamics of fallow deer taming, management and domestication. This is a significant step forward for Prehistoric assemblages from the Western Mediterranean, where fallow deer were both endemic and culturally important, but also for assemblages from mediaeval northern Europe, where translocated populations became particularly well established.
Acknowledgements This paper was written during a period of research leave funded by the  University of Nottingham. The research was funded by the  Arts and Humanities Research Council  ( Standard Grant   AH/I026456/1 ), with additional PhD funding from the  University of Nottingham's Faculty of Arts  and a subvention for the ATM MNHN 'Biodiversité actuelle et fossile' 2013 for the project 'History of the faunal population from the  Pleistocene  to the  Modern periods in Barbuda. 'We are grateful to  Norma Chapman  for providing access to her collections, to  Christine Lèfevre  and  Joséphine Lesur  who facilitated access to the Dama collection at the  Muséum National d'Histoire Naturelle  and to the  National Museum of Ireland  for providing the materials and facilities that enabled us to conduct this research.
Nevertheless, the physicochemical properties range of organic compounds detected or identified with this technique is limited by sample pretreatment, chromatographic or ionization conditions. All of the detectable information is recorded and enables an a posteriori data search. HRMS is an analytical technique which allows identifying compounds and provides a level of confidence on target and non-target screening based on mass accuracy  (Hug et al. , 2014; Martinez Bueno et al. , 2012). The quadrupole time-of-flight (QTOF) analyzer can be used in environmental analysis both for identifying TPs  (Boix et al. , 2014; Durand et al. , 2006; Ibanez et al. , 2006; Jelic et al. , 2013)  and for screening  (Ferrer and Thurman, 2012; Guibal et al. , 2015; Hernandez et al. , 2015; Ibanez et al. , 2005; Leendert et al.
MS data were acquired in centroid, resolution and MS E modes over a m/z range of 50-1,200. Typical interface conditions were optimized for maximum intensity of the mixture of target compounds as follows: nebulizer and desolvatation (drying gas) N 2 (from a nitrogen generator) flows were set at 900 and 50 L/h, respectively, and source block and desolvatation temperatures were 120 and 600 °C, respectively. A cone voltage of 30 V and a capillary voltage of 0. 5 kV were used. The collision gas was Argon (purity 99. 9999 %) at a pressure of 1. 3×10 -4 mBar. The resolution of QTOF-MS was between 24,000 and 29,000 in resolution mode at m/z 556. 2771 (positive ionization). The MS E mode creates two parallel alternating scan functions with different collision energies. Function 1 operates at a low collision energy (LE) of 6 eV, whereas Function 2 operates at a high collision energy (HE) with a ramp ranging from 6 to 45 eV.
Databases Information from chromatographs (retention time) and mass spectra (accurate mass, adducts, fragment ions) enabled us to identify compounds. In order to facilitate and improve the reliability of the identification process, two databases were used: a homemade database that we developed with an injected reference standard and a database from the literature. The first database, called the "target" database, contains more than 340 compounds analyzed in positive ionization. The compounds were divided into four groups, each group containing several classes of compounds. Pesticides and their TPs represent 62 % of the target compounds and include herbicides, insecticides, fungicides, etc. PhACs and their TPs represent 33 % of these compounds and include antibiotics, antiepileptics, analgesics/anti-inflammatories, cardiovascular agents, psycholeptics, etc.
Three percent of the target compounds are industrial compounds, while PCPs and their TPs represent 2 % (Figure  1Figure 1 ). The reference standards for all of these were injected and their retention times, adducts, accurate masses and fragment ions were available. The second database, which we call the "suspect" database, contains data from the literature for about 350 compounds analyzed with positive ionization. We focused, in our study, on the 280 for which fragment information is available. Once again, we divided the compounds into four groups, each group containing several classes of compounds. Pesticides and their TPs represent 61. 3 % of the suspect compounds and include herbicides, insecticides, fungicides, pesticides, etc. PhACs and their TPs represent 46. 6 % of these compounds and include antibiotics, antiepileptics, analgesics/ anti-inflammatories, cardiovascular agents, psycholeptics, etc.
Data processing Data were processed using the ChromaLynx XS Application Manager together with 2 homemade databases and MarkerLynx XS for non-target screening. Identifying all of the organic compounds present in a sample is a laborious, difficult, and time consuming task especially when compounds are present at trace levels  (Hernandez et al. , 2012). As a result of the increased interest in suspect and non-target analysis, Schymanski and co-workers  (Schymanski et al. , 2014)  have proposed a system of 5 identification confidence levels. Based on this system, we used three approaches or levels in this study ( Figure  2Figure  2Figure 2) in the ascending order of confidence: target, suspect and non-target.
Advantages of POCIS for screening We used the same sorbent for both solid phase extraction (for water samples) and POCIS and the elution conditions were very similar. This suggests that there should be little difference in the compounds determined by the two methods. The sampling step is differentwater samples were collected by grab sampling (1 liter), which provides only a snapshot of contamination, whereas with POCIS, the sorbent might be directly exposed for several days, weeks or months to the medium  (Vrana et al. , 2005) , which corresponds to several liters of extracted water. Figure  3  presents the results of target compounds identified from POCIS (triplicate for the six sampling periods, n=18) and water samples (for each sampling period, n=6) in site 1. The proportion of the four classes of compounds is approximatively the same in the target database and samples (water and POCIS) (Figure  3Figure  3).
Target screening To highlight compounds specific to each source of contamination, all target compounds identified in both sites were compared with the target database. Both sites have agricultural sources of contamination whereas only site 1 is impacted by urban effluents. Sixty-four compounds were identified as target compounds in site 1 and 72 in site 2, of which 38 are identified in both (Figure  5Figure 5 ). Sixty-eight percent of the shared target compounds are pesticides (26 compounds), 42% being herbicides and TPs with atrazine and its TPs (atrazine-desethyl and atrazine-desisopropyl), and terbumeton and its TP (desethyl-terbumeton), and 21% being fungicides with carbendazim and cyproconazole. PhACs represent 24 % of the total, with 9 compounds including tramadol, which is an analgesic/anti-inflammatory drug and oxazepam which is a psycholeptic drug.
Suspect screening Suspect compounds were identified using data in our suspect database. The level of identification confidence of compounds identified as suspect is lower than that of target compounds because their presence is not confirmed by the injection of a reference standard. Twenty-seven of the 280 compounds in the suspect database were identified in site 1 and 32 were identified in site 2 (Table  1Table 1 ). Most of the compounds identified as suspect are PhACs (more than 70 %). Eighteen suspect compounds were detected in both sites and 11 of these in more than half of the 18 samples -4 or 5-methyl-1H-benzotriazole being one of the industrial compounds, atrazine, pirimicarb and 2-aminobenzimidazole (TP of carbendazim) three of the pesticides, guaifenesin and desmethyl-dextrophan (TP of dextromethorphan) two of the respiratory agents, tramadol and ketorolac two of the analgesic/anti-inflammatory agents, des-venlafaxine and didesmethylvenlafaxine (TPs of venlafaxine) two of the psycholeptics, and primidone one of the antiepileptics.
, 2012). And simazine-2-hydroxy have four fragments: 184. 1207, 114. 0659, 97. 0389 and 69. 0094  (Diaz et al. , 2011). The presence of two herbicide TPs confirms the principal agricultural source of contamination for site 2. Terbuthylazine-desethyl-2hydroxy has been detected in groundwater elsewhere  (Huntscha et al. , 2012). Nifedipine is also used in veterinary medicine (to decrease cholesterol accumulation). As stated above, this compound might come from either wastewater or livestock. Using the suspect database enabled us to increase the number of identified compounds by making use of literature data and databases on line while limiting the cost of purchasing and analyzing reference standards. However, retention times and complete mass spectra, needed to confirm the presence of a compound, are not included in on-line databases and can be determined only by injecting standards into the same apparatus as the samples.
Non-target screening For the non-target analysis, MarkerLynx XS software filters the markers (characterized by retention time, exact mass and intensity) that are identified in at least two samples of a sample set (containing 18 POCIS for each site). In these 36 samples, about 12,000 markers were detected for the sample sets for both sites. Many of these, such as natural or widespread compounds, are not discriminants. Among them are the compounds identified with both of the approaches described above (target and suspect). PCA enabled us to highlight specific markers for each site. 41 markers were specific to site 1 and 36 markers to site 2 after manually checking that each marker was detected in two samples in a triplicate. For each marker, the retention time, accurate mass and the trend view were provided (Figure  6Figure  5).
It is also possible to access the elemental composition of each marker based on accurate mass (Figure  6Figure 5 ). At most, twenty results of elemental compositions could be calculated, and these were ranked by mass error. The best choices must be made as a function of mass error, isotopic pattern, fragment ions, analyst knowledge and online database data. The trend view is a plot of the relative normalized intensities of a selected marker throughout the triplicates of POCIS, sampling period and sampled sites. The first 18 samples correspond to 18 samples from site 1 and the others to samples from site 2. Markers specific to site 1 were present only in the first 18 samples as shown in Figure  6Figure  6a for the marker detected at 4. 94 min with m/z 250. 1738, whereas markers specific to site 2 were present only in the last 18 samples (Figure  6Figure 6b ).
Conclusions This study demonstrates the advantage of using POCIS for organic chemical screening. More organic compounds were detected and identified with this tool than by analyzing grab samples due to POCIS's accumulation capability. HRMS makes it possible to identify, in only one injection, targeted compounds and/or suggest non-targeted compounds using a database compiled with literature and on-line data. The combination of both these tools can significantly improve environmental monitoring programs. The data processing described here enabled us to identify organic compounds in groundwater by target and suspect screening. Their presence is in agreement with the previously-known types of pollution sources for each site (agricultural and urban effluents for site 1 and an agricultural source for site 2), with the exception of some PhACs in site 2 that seem to indicate an impact of effluents from wastewater treatment plants.
This methodology revealed the presence of transformation products that had never been identified before in the sites. Their presence might be problematic with regard to current legislation on drinking water, which stipulates that the total concentration of all pesticides and their TPs must be lower than 0. 4 µg/L. The use of statistics for non-target screening facilitates the process by highlighting relevant markers to identify. A molecular fingerprint was made for both sites and should be continuously improved by identifying the unknown compounds. Various methods for accomplishing this are currently under investigation. These include i) working on the implementation of target and suspect databases, ii) ensure developing a specific suspect database, and iii) comparing the pollution footprint as a function of the site and/or the pollution source.
We develop and test a new model of knowledge flows in the emerging market multinational corporation (MNC) based on the way people are managed in its foreign subsidiaries. Extant literature argues that, to facilitate effective intra-MNC knowledge transfer, subsidiaries need to (a) possess human capital, (b) encourage inter-unit socialization of human capital. However, the impact that a subsidiary's human resource management (HRM) practices have on these relationships remains under-researched, especially for MNCs from emerging markets. Using questionnaire survey data from senior managers of 86 Korean MNC subsidiaries in the UK, France and Germany, we find that different aspects of subsidiary HRM practices exhibit different direct and indirect effects. HRM practices based on formalized procedures weaken the effect of socialization, but strengthen that of human capital, while empowering practices within the subsidiary weaken the effect of human capital, but strengthen the effect of socialization.
Firstly, the subsidiary may use formal, codified procedures, systems and databases to reduce inefficiency  (Morris et al. , 2006)  and "to encourage the continuous development of employees' skills and abilities, their motivation and the effective utilization of their labour through progressive and adaptive workplace operations"  (Barton & Delbridge, 2004: 333). Formal practices are not limited to a single aspect of HR management  (Laursen & Foss, 2003 , Barton & Delbridge, 2004). Guest (1997)  suggested that formally designed HRM practices apply in areas such as appraisal, rewards, and job design. Extrinsic motivation through rewards has been shown to have a positive impact on an individual's propensity to engage in knowledge sharing across boundaries  (Minbaeva, Mäkelä, & Rabbiosi, 2012).
Data collection We tested this model through a questionnaire survey of senior subsidiary managers in fully-owned subsidiaries of Korean MNCs located in the United Kingdom, France and Germany. These three host countries were chosen because they are major locations of outward investment by Korean MNCs to Europe and also because they represent different cultures, management styles and national institutions. We identified 227 subsidiaries of Korean firms located in the UK, Germany and France through the directories of the Korean Chamber of Commerce in each of the three countries. The Korean Chamber of Commerce compiles a list of foreign subsidiaries of Korean MNCs. Among these, 68 were not listed on the main Korean Stock Exchange (KSE) and were not included in our study, reducing the target frame to 159 subsidiaries.
In contrast with both previous studies in which participants lived in a developed country (England), our research investigated whether similar results would be obtained in a developing country (Romania). A second extension of our study is that we gathered socio-demographic data on each child and their family to ensure that we would have a fine-grained measure of children's social environment. Finally, we used a different method, the "Quality Dictator Game" (adapted from  Sheskin et al. , 2016) , that might be more adapted to young children, potentially allowing us to detect differences at younger ages than previous research. The Quality Dictator Game investigates how children allocate windfall resources that vary in quality. The Quality Dictator Game allows for an additional analysis that is not possible in studies using a pool of identical resources (e.
776 din 12. 12. 2013. Parents were asked what their combined income including social aids was, with only two response options: "less than 850 lei, " "more than 850 lei. " This value is approximately $200. All the results found by contrasting the two schools were confirmed using parental income (above or below the minimum wage) as the grouping variable (see Appendix 1 in Supplementary Material). We tested 41 children aged 6-7 years (M = 6. 9 years, SD = 0. 43 years, range = 6. 1-7. 9): 18 children were girls (44%), and 20 identified with the Roma minority (49%). A minimum  •TVs, copy machines, printers, video, projector, scanners, digital cameras •TVs, copy machines, printers, video, projector, scanners, digital cameras •Library (11,500 volumes) •Library (7,804 volumes) The data was collected from each school's Secretary Department and it is valid for the 2013-2014 school year.
Calculating the Transfer Score The data about each child's preferences collected in the toy ranking task allowed us to calculate a "transfer score" that measures the relative value of the toys allocated to each person for each child. The transfer score for each child was calculated in two steps. First, we assigned each of the 12 toys a value based on the child's rank-ordering of the toys. The best toy got 12, the second 11, etc. Thus, the value is 13 minus the rank. We then calculated the child's transfer score by adding the values of any toys transferred to the other child and subtracting the values of any toys kept for self. For example, here is the calculation for a child who had ranked the ping-pong ball third and allocated it to self, ranked the whistle fourth and allocated it to self, ranked the pencil seventh and allocated it to other, and ranked the sticker tenth and allocated it to other (see Figure  2 ): score = -(13 -3) -(13 -4) + (13 -7) + (13 -10) = -10 -9 + 6 + 3 = -10.
, on which our experiment is based, and compared the transfer scores of the children from two schools. Because two outliers were identified in our dataset, we used robust linear regression to analyze the transfer scores (outliers: N = 1 in School A with transfer score of +20; N = 1 in School B with a transfer score of +23). The mean transfer score was -9. 53 in children from School A (N = 19, SD = 11. 95, range = -29 to +20) and -15. 75 in children from School B (N = 20, SD = 12. 95, range = -30 to +23). This analysis revealed that children from the school situated in the very deprived neighborhood transferred significantly less to a stranger than children from the school situated in the less deprived neighborhood [t(37) = -2. 72, p = 0. 010; Figure 3].
We see this first with Yu Jicai's 庾季才 (a. d. 516-603) Secret Garden of the Observatory (Lingtai miyuan 靈臺秘苑) of circa a. d. 580, then again in Li Chunfeng's 李淳風 (a. d. 602-670) Yisi zhan 乙巳占 of a. d. 656, and, by point of criticism (above), in Gautama's Kaiyuan Omen Classic. 35 omething happened in the sixth century a. d. , it would seem, something big. Jiang Xiaoyuan 江曉原 once suggested that we might someday uncover evidence of Indian influence upon Zhang Zixin's work-an influence prior to the days of the seventh and eighth century a. d. , when Indian lineages like the Gautamas came to run the Tang astronomical office, that might help explain the one-century disconnect between the revolution in li and the sudden foothold of foreign experts thereupon. 36 It is a compelling thought, but nothing of Zhang's work survives, and it is difficult to imagine anyone more isolated from outside influence than the man who "went into hiding on a sea island for more than thirty years were he devoted himself to the observation via armillary sphere of data on the differences and changes in the sun, moon, and [planets]" 隱於海島 中，積三十許年，專以渾儀測候日月五星差變之數.