dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, analysing the rules (present but also absent), given their support and confidence, allows to identify inconsistencies in the databases (i. e. typing errors, data entry errors or anomalies in the definition of the attributes), vii) analyse more complex rules by comparison with the elementary ones through three logical operations, denoted here as extension (of hypothesis or conclusion part of rules), permutation (of attributes between hypothesis and conclusion part of rules) and junction (of the hypothesis or conclusion part of rules), and then using the same steps described above, viii) represent an overall structure of the extracted rules (indicating the relationship between the identified rules), thereby facilitating understanding and a visual exploration of the mined rule set by users, ix) formalise a "metarule" to generalize a rule-set and provide a new abstraction level grouping the rules.
2 ), specifying the logic relation between the elementary rules (upper part in Fig. 2 ) and more complex rules. For instance, R9 is an extension of R4 (new items have been added to the hypothesis part of R9), R9 is a permutation of R10 (some items of the rules R9 and R10 have been permuted), R10 is a junction between the rules R5 and R6 (combining the hypothesis parts of R5 and R6) and R10 is also a permutation of R8. Finally, the domain expert may make queries on this structure in order to filter the different results, which may help to effectively guide human decision making related to processes, or simply suggest how to better structure the database. In the proposed approach, the role played by the domain expert and the quality of the input data are decisive; both affect the quality of the extracted knowledge.
A B S T R A C T Lakes influence the structure of the atmospheric boundary layer and, consequently, the local weather and local climate. Their influence should be taken into account in the numerical weather prediction (NWP) and climate models through parameterisation. For parameterisation, data on lake characteristics external to the model are also needed. The most important parameter is the lake depth. Global database of lake depth GLDB (Global Lake Database) is developed to parameterise lakes in NWP and climate modelling. The main purpose of the study is to upgrade GLDB by use of indirect estimates of the mean depth for lakes in boreal zone, depending on their geological origin. For this, Tectonic Plates Map, geological, geomorphologic maps and the map of Quaternary deposits were used. Data from maps were processed by an innovative algorithm, resulting in 141 geological regions where lakes were considered to be of kindred origin.
Therefore, lake data should be provided in a gridded form. Lake fields should be global and contain information about all existing lakes. Lake depth is the most important parameter used by all lake models. We may use the bathymetry when it is available, or for middle and small size lakes we may use the mean lake depth, which is recognised to be a reasonable approximation in atmospheric applications, when we are mainly interested in surface lake processes on large scales. Moreover, for atmospheric modelling (in contrast with hydrological), an accuracy and even a reliability of depth data are not critical, but a global coverage is essential. In the absence of direct measurements, indirect estimates, though less precise, can be used. For the parameterisation of lakes in NWP and climate modelling, Global Lake Database (GLDB)  (Kourzeneva, 2009; Kourzeneva et al.
We started from information about the deepest layers of the Earth crust from Tectonic Plates Map, then we applied data about middle layers from the geological map, then about upper layers from the map of Quaternary deposits and finally about the top layer from the geomorphologic map. We considered first the boreal zone, making the first step forward to global estimates. Even nowadays, this climate zone remains the most explored, as it was in the mid-80s, when the first study generalising morphological characteristics of lakes appeared  (Kitaev, 1984). Northern boundary of the boreal climate zone was considered to be the North Pole. Southern boundary was drawn in the Northern hemisphere at 308 latitude for the North America continent, 508 latitude for the Europe continent (westward from the 408 longitude) and 408 latitude for the Asia continent (eastward from the 408 longitude).
Data sources 2. 1. GLDBv. 1 and the mapping method GLDB  (Kourzeneva, 2009; Kourzeneva et al. , 2012a)  was developed especially for the purpose of parameterisation of lakes in NWP and climate modelling. It contains mapped information on the lake depth. This information is presented on the fine grid with the resolution of 30 arc sec (approximately 1 km). Basically, it contains the mean lake depth data, with bathymetry for several large lakes. Information in gridded form gives an opportunity to use it for global atmospheric models. GLDB applies the following data sources: (i) data on the mean depth for individual lakes from different regional databases (ca. 13000 lakes), (ii) the global map Á ecosystem dataset ECOCLIMAP2  (Champeaux et al. , 2004) , and (iii) bathymetry data for 36 large lakes, from ETOPO1  (Amante and Eakins, 2009)  and digitised navigation and topographic maps.
The dataset for individual lakes contains the following information for each lake: the name, the location country name, geographical coordinates, the mean depth, the maximum depth, and the surface area. At the moment, the dataset for individual lakes consists of over 13000 freshwater lakes (and in addition 220 saline lakes and endorheic basins). Natural and artificial lakes both are presented and not distinguished. In this study, we updated this list by fixing errors and adding ca. 500 lakes. To map the data for individual lakes, the automatic method described in  Kourzeneva et al. (2012a)  is used. The method is probabilistic: it is assumed that all data have random errors. Data on freshwater lakes are processed and data on saline lakes are skipped. When there is a lake on the map, but its depth is unknown (no data in the dataset for individual lakes), a 'default' depth of 10 m is used.
Input data were: (i) digitised contours from Tectonic Plates Map and geological maps in 'bottom-up' order, and (ii) coordinates of individual lakes and/or gridded map of lakes from GLDBv. 1. To specify if the lake point with the certain geographical coordinates belongs to the contour in question, we used a polygon test with a crossing number algorithm  (Hormann and Agathos, 2001). If the point belongs to several contours, it means that these contours intersect. Coordinates of lake points may be known either from the dataset of individual lakes or from the pixel coordinates of the gridded lake map. For better statistical analysis, we processed data twice: both for the list of individual lakes and for the gridded lake map. For every lake from the list of individual lakes with known mean depth and coordinates with the polygon test, we determined: (i) the tectonic plate and (ii) the craton which the lake in question belongs to, (iii) presence/absence of the permafrost, (iv) Quaternary deposits type and (v) type of rocks on the territory where the lake in question is located.
Different combinations of information from (i) to (v) formed an individual code. These codes corresponded to intersections of contours, which we assume to be the regions with similar geological conditions of lake origin. A logical scheme to process data from the list of individual lakes is presented in Fig. 1. For pixels of the gridded lake map with known lake depth, we applied a similar algorithm. To process data automatically, we developed software with the Fortran90 programming language. Altogether, we allocated 141 regions. The list of allocated regions is presented in the Appendix. Polygons corresponding to different tectonic plates, cratons, permafrost, Quaternary deposits and rock type, were overlapped. Contours of the allocated regions were obtained by tools of geographic information systems and used further for visual analysis.
There could be some lakes in a region which had been formed differently from the majority of neighbouring ones. We did our best to exclude them from our analysis. For that, we performed our analysis in several steps. First, we analysed full statistics collected from the list of individual lakes and from the pixels of the gridded lake map. If there were enough data in GLDBv. 1 and maximums of histograms for individual lakes and for lake pixels agreed well, the expert decision about the typical lake depth could be arrived at quite easily. Very different maximums of histograms indicated situations of non-typical large lakes located in the region. To cope with these situations, we also collected filtered statistics, with excluded large lakes, and built the appropriate histograms. For filtering, we used a criterion of the lake surface area to be more than 200 km 2.
This threshold value was based on practical reasons. First, the mean depth of most lakes like this is known, while measurements for smaller lakes are rather rare. Second, large lakes are often of more complex origin. They may occupy a particular geological structure that is not typical for the region. Usually they are older and have passed through the numerous cycles in their evolution. It makes them non-representative for the given statistical subset of lakes. For each of the 141 regions, four histograms were built and then analysed: for individual lakes and for lake pixels, with full data and with filtered data, making up 564 histograms in total. Statistics was collected using specially developed Fortran90 software. The expert decision about typical lake depth was reached individually for each region on the basis of the obtained statistics.
Using the large dataset of in situ measurements of morphological parameters of lakes in  Doganovsky (2012)  and  Doganovsky (2006) , the relation between the water volume (W) and the surface area (F) was established: y ¼ ax m Á e xÀ1 ; (1) where y 0log 10 (W'1), x0log 10 (F'1), a is a parameter, dependent on the typical lake size on the territory, and m is a parameter, dependent on geological characteristics of the territory. Values of a and m for each region are given in Table  1. Therefore, from a given lake area, we can estimate the lake volume and the mean lake depth h by inverting eq. (  1 ): h ¼ 1000 Á exp a eÁ ln10 ð Þ mÀ1 Á ln F þ 1 ð Þ ð Þ m Á F þ 1 ð Þ 1 ln10 = h i À 1 n o F ; (2 ) where h is given in m, and F is in km 2. We considered this method to have high reliability and gave it the highest priority on the appropriate territory.
New product and discussion Indirect estimates of the mean lake depth from their geological origin where appended to GLDBv. 1 and the new raster map of the world GLDBv. 2 was produced. Technically, the new map was combined from GLDBv. 1 and three auxiliary maps of the world, containing the lake depth estimates from our expert evaluation, improved geomorphologic and geographical methods (see Section 4) accordingly. The additional map from GLDBv. 1 containing coded information about sources of data was used for flagging and then updated. Values for pixels with code 3 (see Section 2) were just copied from the old map to a new one. For them the lake depth was measured and no indirect estimates were needed. The same was done for code 4 (river). For codes 1 and 2, we applied the following procedure: (i) if a geomorphologic method was possible, we specified the mean lake depth from it (this situation was coded by digit 7); if not, then (ii) if estimations from our expert evaluation were possible, we used them (this was coded by digit 5); if not, then (iii) if a geographical method was possible, we specified the mean lake depth from it (digit 6); if not, then (iv) we used the default depth 010 m and codes 1 or 2 accordingly.
Exception from this rule was Region No. 110, for a section of it, the priority was given to the geographical method (see Section 4. 4 for an explanation). As a result, two new global raster maps were produced. The first map contains gridded information about lake depth including indirect estimates (or predictions). The second map contains information about sources of data coded with the following legend: (0) no lake or river (sea or land), depth 00 m (1) no information about this lake in the list of individual lakes, depth 010 m (2) missing lake depth in the list of individual lakes, depth010 m (3) information about lake depth is from the list of individual lakes or bathymetry (4) river, depth 03 m (5) lake depth is estimated by our expert evaluations (6) lake depth is estimated by the geographical method (7) lake depth is estimated by the geomorphologic method.
Figures  6  and 7  give an example of the new product in comparison with the old one, for the territory close to Hudson Bay in North America. In GLDBv. 1, the lake depth for unknown lakes is 10 m (code 1 on Fig. 6A  and 7A ). In GLDBv. 2, the lake depth is smaller in the northwestern part and larger in the south-western part of the region. In the south-western part (along the Gulf of St. Laurence), lakes have mostly tectonic origin, so they are supposed to be deep. This is in accordance with our estimations and modifications of the lake depth here from 10 m in GLDBv. 1 to 22 m in GLDBv. 2. Currently, we have no additional dataset to verify or validate our product. Cross-validation is also impossible, because of data scarcity for many regions. Our estimates are noticeably subjective and basically difficult to verify.
Nevertheless, we evaluated our product qualitatively, as in the example presented above. We suggest that the new version of the GLBD is more realistic as it provides physically-based estimations without contradiction to the seldom real lake depth measurements. In future, when the new data is available, validation will be performed as well as improvements being made. Another way to validate the new product is indirect. We may compare modelling results of the lake surface temperature with remote sensing observations. The main problem with this approach is errors in remote sensing data, which on the global scale may be large and should be carefully filtered out. This validation is also planned.
The snow module was also switched off, because it had not been yet enough validated at that moment. For atmospheric forcing, the global dataset described in  Sheffield et al. (2006)  was used. The product is presented on the grid in geographical coordinates (longitude and latitude) with 18 resolution. Simulations were performed for 12 hypothetic lakes with depths varying from 1 to 50 m in each grid box. Modelling results were averaged in time for 10-d intervals. The grid of a certain NWP model may have different resolution and map projection than the grid of the lake climatology product. Thus, lake information must be projected from one grid to another with the closest neighbour interpolation method. During this step, data about real lake depth on the target NWP model grid are applied, see  Kourzeneva et al.
The domain contains part of North America with Hudson Bay where many modifications in GLDB have been done (see Figures  6  and 7 ). of the lake mean water and surface temperature for the second decade of August calculated using the new lake depth dataset. Figure  8B  and 9B represent the difference, if the lake depth from GLDBv. 2 and GLDBv. 1 is used. Negative difference means colder temperatures with GLDBv. 2; it mainly corresponds to deeper lakes. The difference both in terms of the mean water and surface temperature may reach 58C. Ice conditions are sensitive to the modifications in GLDB mainly in autumn, since the lake depth controls an onset of the ice season. The difference between two simulations in terms of the ice thickness for the first decade of December reaches 50 sm (Fig. 10 ).
Conclusion Lakes influence local weather and the regional climate through the surface turbulent fluxes and fluxes of radiation. To parameterise them in NWP and climate modelling, external parameters are needed. The main objective of this study was to upgrade the existing GLDBv. 1  (Kourzeneva et al. , 2012a)  by estimations (predictions) of the mean lake depth depending on their geological origin and to produce the new version of the database. For that, we outlined the regions with similar geological and geomorphologic history suggesting similar origin of lakes there, and proposed the typical lake depths for them using expert evaluations on the basis of the statistics from the first version of the database GLDBv. 1 and involving other sources of information. In addition, geomorphologic  (Doganovsky, 2006)  and geographical  (Kitaev, 1984)  methods were used where necessary and possible.
We produced the new version of the database GLDBv. 2, which includes estimates of the typical mean depth for the boreal lakes. Final products include the global gridded map of lake depth with the resolution of 30 arc sec. containing also indirect estimates (predictions) of the mean lake depth, and the updated list of individual lakes now containing ca. 13500 lakes. Apart from continuous maintenance of GLDB by updating the list of individual lakes, the further developments could be proposed: (i) Extend this study and obtain the typical mean depth estimates for lakes outside boreal climate zone. (ii) Add bathymetry for more large lakes. (iii) Increase the map resolution by using different global raster maps Á different ecosystem datasets.
The final survey parameters and scientific goals, as well as the technical properties of the filter set, were described by  Moles et al. (2008). The survey has collected its data for the 20+3 optical-NIR filters in the 3. 5m telescope at the Calar Alto observatory, using the wide-field camera LAICA (Large Area Imager for Calar Alto) in the optical and the OMEGA2000 camera in the NIR. The full characterisation, description, and performance of the ALHAMBRA optical photometric system was presented in  Aparicio-Villegas et al. (2010). A summary of the optical reduction can be found in  Cristóbal-Hornillos et al. (in prep. ) , while of the NIR reduction in  Cristóbal-Hornillos et al. (2009). The ALHAMBRA survey has observed 8 well-separated regions of the northern sky. The wide-field camera LAICA has four chips with a 15 ′ ×15 ′ field-of-view each (0.
Sample selection Throughout present paper we focus our analysis in the galaxies of the ALHAMBRA first data release 2. This catalogue comprises ∼ 500k sources and is complete (5σ, 3 ′′ aperture) for i ≤ 24. 5 galaxies  (Molino et al. 2013). We explored different apparent luminosity sub-samples from i ≤ 23 to i ≤ 20. That ensures excellent photometric redshifts and provides reliable merger fraction measurements (Sect. 4. 3), because the PDFs of i ≤ 23 sources are defined well by a single Gaussian peak  (Molino et al. 2013). In Sect. 4. 7 we also study the cosmic variance in luminosity-and stellar mass-selected samples. The B-band luminosities and the stellar masses of the ALHAMBRA sources were also provided by BPZ2. 0 and are included in the ALHAMBRA catalogue (see  Molino et al. 2013, for further details).
, if the companion galaxies B and C are close to the principal galaxy A, we study the pairs A-B and A-C as independent). In addition, through present paper we do not impose any luminosity or mass difference between the galaxies in the close pair unless noted otherwise. With the previous definitions the merger fraction is f m = N p N 1 , (3) where N 1 is the number of sources in the principal sample and N p the number of close pairs. This definition applies to spectroscopic volume-limited samples, but we rely on photometric redshifts to compute f m in ALHAMBRA. ). We recall the main points of this methodology below and we explore how to apply it optimally over the ALHAMBRA data in Sect. 4. 3. We used the following procedure to define a close pair system in our photometric catalogue (see  López-Sanjuan et al.
Acknowledgements. We dedicate this paper to the memory of our six IAC colleagues and friends who met with a fatal accident in Piedra de los Cochinos, Tenerife, in February 2007, with a special thanks to  Maurizio Panniello , whose teachings of python were so important for this paper. We thank the comments and suggestions of the anonymous referee, that improved the clarity of the manuscript. This work has mainly been funding by the  FITE (Fondo de Inversiones de Teruel)  and the projects  AYA2006-14056  and  CSD2007-00060. We also acknowledge the financial support from the  Spanish  grants  AYA2010-15169 ,  AYA2010-22111-C03-01  and  AYA2010-22111-C03-02 , from the  Junta de Andalucia  through  TIC-114  and the  Excellence  Project  P08-TIC-03531 , and from the  Generalitat Valenciana  through the project  Prometeo/2009/064.
Acknowledgements The authors thank the  National Institute of Agricultural Research (INRA, France) and Academy of Scientific Research & Technology (ASRT, Egypt)  for supporting this study within the REF BHC IMHOTEP 2011 Project No. 25382  YG. Special thanks are due to  Dr. L. Gamet-Payrastre  for providing facilities and following up the animal rearing and dosing in TOXALIM Unit. We also thank  Prof. Dr. Kawkab Abdel-Aziz ,  Pathology Department ,  Faculty of Veterinary Medicine, Cairo University, Egypt  for reading the histopathological slides.
Methods Measles cases in DRC are routinely reported through the integrated disease surveillance (IDS) system, which aims to strengthen surveillance capacity, to better identify disease prevention priorities and to provide epidemiological information for disease control. The IDS functions at several different levels. At the lowest level, information on measles and other reportable diseases is collected on a weekly basis in Health Areas. Information then passes upwards through Health Zones (HZ), Health Districts (HD), and provincial-level ministries of health, which are responsible for transmitting data to the central-level MOH in Kinshasa. An HZ has a variable catchment area, with populations ranging from 26,547 inhabitants in Kowe, Katanga Province, to an estimated 284,187 inhabitants in Ibanda, South Kivu Province.
Following WHO guidelines, in DRC, a suspected measles case is defined as fever >38°C and rash, with at least one of the following: cough, coryza or conjunctivitis. A confirmed measles case is a suspected case with positive IgM antibodies for measles, or a suspected case occurring during an already-confirmed outbreak. A measles death is defined as death from an illness that occurs in a confirmed case of measles within one month of the onset of a rash. All data on cases and deaths were collected on a standard case report form and were routinely transmitted through the IDS system. In the current epidemic, blood specimens of early suspected cases in each HZ (between 5 and 10) were collected and laboratory confirmed by the National Institute of Biomedical Research (INRB) in Kinshasa, where measles and rubella IgM ELISA are performed.
Only a limited number of cases were registered with age and vaccination status as aggregate data is presented on the IDS form. Available information included numbers of suspect and confirmed cases by age group, as well as reported mortality. This was collected from the Epidemiologic Surveillance Division of the MOH in Kinshasa. In addition to surveillance data, we also present information on estimated vaccination coverage from routine monitoring surveys conducted post-vaccination campaign. Between 2010 and 2013, MSF conducted multiple household-based surveys in different areas of DRC, providing local information on vaccination coverage. Results of these surveys and their respective methodologies are reported elsewhere  [12, 13]. As IDS data is not routinely digitized, data entry was performed in Microsoft Excel.