dataesr/scientific-paragraphs-categorization huggingface dataset:
Value 8. 66 × 10 -5 Note. The model had the form Call: lm(formula = metanalysis$Z. transform ~metanalysis$path. type + metanalysis$comparison, weights = metanalysis$fixed. weight). Note that for the lm function, the order of the variables is not relevant. * p < 0. 05. ** p < 0. 01. *** p < 0. 001. TA B L E 1 Results of the linear regression for species richness F I G U R E 3 Box plots of the Fisher's Z transformations of each category in the three categorical variables included in the model of plant richness effects. Note that largest Fisher's Z transformation value (effect size) does not necessarily mean the largest richness value, but rather the largest change in richness between experimental comparisons. Top: Path type. Roads and trails were significant categories in the meta-analysis. Bottom: Comparisons, B-A = before-after, H-L = high-low, N-F = near-far, P-A = present-absent types in the richness dataset, and their insufficiency as indicators of the relevant mechanisms.
Bottom: Comparisons, B-A = before-after, H-L = high-low, N-F = near-far, P-A = present-absent types in the richness dataset, and their insufficiency as indicators of the relevant mechanisms. For example, grasslands, which were significant for plant diversity, are open habitats (meaning, lacking a canopy layer), and many ruderal species require both disturbed soil and full sunlight to establish. Studies that indicated these factors, rather than simply habitat type, would allow for a finer definition of what drives richness responses in different habitats.
Many environmental data are inherently imbalanced, with some majority land use and land cover types dominating over rare ones. In cultivated ecosystems minority classes are often the target as they might indicate a beginning land use change. Most standard classifiers perform best on a balanced distribution of classes, and fail to detect minority classes. We used the synthetic minority oversampling technique (SMOTE) with Random Forest to classify land cover classes in a small agricultural catchment in South Korea using MODIS time series. This area faces a major soil erosion problem and policy measures encourage farmers to replace annual by perennial crops to mitigate this issue. Our major goal was therefore to improve the classification performance on annual and perennial crops. We compared four different classification scenarios on original imbalanced and synthetically oversampled balanced data to quantify the effect of SMOTE on classification performance.
Introduction Detailed information on land use and land cover (LULC) is essential in many areas of environmental sciences. A constantly growing body of literature emphasizes the impact that changes in land use can have on Earth's climate, biodiversity and water cycle  [1] [2] [3]. Among different human land use forms, croplands and pastures are particularly frequent and occupy 40% of the land surface  [1]. Detecting LULC changes in agricultural areas might be challenging because global land cover databases like GlobCover or MODIS Land Cover Type Product (MCD12Q1) have few croprelated classes. To derive more detailed LULC maps, time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) can be used because they allow to track seasonal variation of vegetation development. Several studies have shown that time series could better discriminate between different types of vegetation than single snapshots  [4, 5].
The goal of our study is to improve the classification of the rare classes 'annual dryland crops' and 'perennial crops' by using RF with SMOTE on MODIS time series. We compare four different classification scenarios on original imbalanced and synthetically oversampled data. We quantify the effect of SMOTE on overall classification performance and on different groups of land cover classes. In particular, we show that in the presence of class overlap, synthetically increasing the number of training points using SMOTE does not guarantee a better classification result. To our knowledge, only few papers in the remote sensing literature address this issue on a complex real-world data set with imbalanced classes. Finally, we analyse by which mechanism the alternative scenarios affect model performance, looking at the relationship between class labels and surface reflectance (mutual information) and the difficulty of classification (entropy).
MODIS surface reflectance MODIS has a daily revisiting time and the data can be temporally aggregated to mitigate gaps (e. g. due to cloud contamination). We used the Vegetation Indices product MOD13Q1 (Collection 6) that has a spatial resolution of 250 m and provides four reflectance bands (bands 1, 2, 3 and 7). They contain specific information related to their spectral ranges. The red band 1 (B1) covers 620-670 nm and is sensitive to chlorophyll in vegetation. The near-infrared band 2 (B2) covers 841-876 nm and has been widely used to study vegetation together with B1. Band 3 (B3) is commonly called the blue band due to its sensitivity to water vapour. It covers 459-479 nm and is used to filter the cloud covered data or detect water bodies. Additionally, it serves to differentiate soil from vegetation.
The range of the mid-infrared band 7 (B7) equals 2105-2155 nm and is also used to examine land and cloud properties (LP DAAC, https:// lpdaac. usgs. gov/dataset_discovery/modis, accessed on 2017-11-01). Note that the channels B3 and B7 are originally acquired at 500 m resolution, but delivered at 250 m resolution in MOD13Q1 after downscaling done by NASA  [22]. Every observation in MOD13Q1 is a temporal composite of a 16-day measurement period  [23]. We used the data for the year 2010. Every band contained 23 images (i. e. 23 acquisition dates) and we obtained 92 data points per pixel of 250 m × 250 m. The whole catchment covered 1200 pixels of the MODIS tile H28V5. In the same study area, Seo and colleagues  [24]  used reflectance bands together with vegetation indices to predict fractional LULC cover.
Reference land use and land cover data The reference LULC data set was obtained by ground census of the whole study area in 2010. It contains 67 crop/non-crop LULC classes and is available online at the public repository Pangaea  [20]. The data set consists of projected geospatial polygons (WGS84 / UTM 52N; EPSG:32652) with LULC classes assigned to each polygon and with detailed information on grown crops. Because we were interested in improving the classification of 'annual dryland crops' and 'perennial crops' in general, we gathered different crops belonging to these groups together (S2 Table ). For each of the 1116 MODIS pixels covering the study area, we determined the LULC class using this polygon data set. If a pixel contained multiple polygons (e. g. several agricultural fields with different crops), we assigned the LULC class covering the largest proportion of the pixel.
Resampling and preprocessing of training data The synthetic minority oversampling technique (SMOTE) oversamples a rare class by generating new instances  [8]. For every existing point Q i (in our case MODIS pixel) in a given rare class, it inserts synthetic points along a line that connects this point to one of its k nearest neighbors. Depending on the oversampling rate N, several k nearest neighbors can be chosen randomly and several points can be generated along one connecting line (Fig  2 ). In our case, SMOTE can be understood intuitively as a linear spectral mixing method because it linearly combines two spectral signals (i. e. MODIS time series) to generate a new synthetic time series. The original SMOTE algorithm suffers from several drawbacks  [6, 9, 29]. In particular, the information on the distribution of all other classes is ignored, which can lead to overgeneralization and implausible synthetic points located spectrally close to other classes.
Classification scenarios In order to evaluate how the classification performance changes when the training data is preprocessed, we compared different classification scenarios. In every scenario, we carried out a 6-fold stratified cross validation (SCV). Thus we split the data randomly (each class separately) in 6 folds, used 5 folds to train the classifier and the hold-out fold to test it. Because each fold was used once as a hold-out test fold, we obtained predictions for every pixel. The random splitting was repeated 10 times. Note that we always evaluated the scenarios on the hold-out test set that was not preprocessed (i. e. neither synthetically oversampled, nor undersampled nor Tomek links removed). We used the same splitting in training and test data for all scenarios. These were defined as follows: S1: Original data This is the base-line scenario.
Summary and conclusions Our research was motivated by environmental issues like soil degradation and erosion associated with dryland agriculture on slopes practices in the study area. Shifting from annual to perennial crops is one of the mitigating measures. Therefore, our goal was to improve the classification of the classes 'annual dryland crops' and 'perennial crops' based on freely available remote sensing data in order to improve the monitoring of LULC changes. In this study, we used SMOTE to resample MODIS time series to decrease the imbalance ratio of the original data set. The classification of the original data was particularly challenging due to a small number of training points in the minority classes and the class overlap. SMOTE helped to alleviate the issue of class imbalance by increasing the number of training points.
Acknowledgments This study is part of the  International Research Training Group TERRECO  ( GRK 1565/1 ) funded by the  German Science Foundation (DFG)  and the  National Research Foundation of Korea. This publication was funded by the  German Research Foundation (DFG)  and the  University of Bayreuth  in the funding programme  Open Access Publishing. Bumsuk Seo  acknowledges the support by the  Agenda Program of the Rural Development Administration  ( PJ00997802 ) in South Korea. The MOD13Q1 data is courtesy of  NASA  ( Land Processes Distributed Active Archive Center  at the  USGS/Earth Resources Observation and Science Center  (https://lpdaac. usgs. gov). The reference land use and land cover data can be downloaded at the public repository PANGAEA (https://doi. pangaea. de/10. 1594/PANGAEA.
The MOD13Q1 data is Active Archive Center at the USGS/Earth Resources Observation and Science Center (https://lpdaac. usgs. gov). The reference land use and land cover data can be downloaded at the public repository PANGAEA (doi:10. 5194/essd-6-339-2014). The software code is available at GitHub (https://github. com/drohner/SMOTE). We confirm that all data can be accessed freely.
Abstract. Nowadays, the study of the upper atmosphere is increasing, mostly because of the need to understand the patterns of Earth's atmosphere. Since studies on global warming have become very important for the development of new technologies, understanding all regions of the atmosphere becomes an unavoidable task. In this paper, we aim to analyze the temperature variability and its trend in the mesosphere and lower thermosphere (MLT) region during a period of 12 years  (from 2003 to 2014). For this purpose, three different heights, i. e. , 85, 90 and 95 km, were focused on in order to investigate the upper atmosphere, and a geographic region different to other studies was chosen, in the southern region of Brazil, centered in the city of Santa Maria, RS (29 • 41 02 S; 53 • 48 25 W). In order to reach the objectives of this work, temperature data from the SABER instrument (Sounding of the Atmosphere using Broadband Emission Radiometry), aboard NASA's Thermosphere Ionosphere Mesosphere Energetics Dynamics (TIMED) satellite, were used.
One of the many instruments used to investigate the upper atmospheric region is the SABER (Sound of the Atmosphere using Broadband Emission Radiometry) on board the TIMED (Thermosphere, Ionosphere Mesosphere, Energetics Dynamics) satellite. Several reports of comparisons between SABER temperatures and those measured by groundbased techniques have been published in recent years (e. g. ,  Mertens et al. , 2004; Oberheide et al. , 2006; Mulligan and Lowe, 2008; Remsberg et al. , 2008; Smith et al. , 2010). All of these studies involve relatively short data runs and focused on intercomparisons of several data from Northern Hemisphere regions. The work of  French and Mulligan (2010)  differed from previous studies in the following respects: it used ground-based temperature data from a Southern Hemisphere station -Davis Station, Antarctica (68 • S, 78 • E) -and it included the years 2002-2009, which enabled the behavior of the bias between SABER and the Davis dataset during this extended period to be examined.
Several authors have studied the temperature in the middle and upper atmosphere over the Brazilian territory, utilizing different methods and techniques for temperature measurements, but not using specific models for trend and variability analyses as in our investigation described here. Among the previous utilized techniques are the airglow OH emission measurements  (Takahashi et al. , 1995) , vertical distribution of atmospheric sodium  (Clemesha et al. , 2004) , OH rotational temperature measurements  (Clemesha et al. , 2005)  and Rayleigh lidar  (Batista et al. , 2009) , but these studies were not focused on the upper mesospherelower thermosphere over the southern region of Brazil, which is a region with very well defined seasons and which is very interesting for these studies. In this paper temperature data from TIMED/SABER instrument in the region of 80-100 km altitude from the years 2003 to 2014 were used to inspect the temperature variability and temperature trend over southern Brazil and surrounding areas.
Instrumentation and data In this study, data from the TIMED/SABER satellite were used for the assessment in the upper mesosphere. The Trend-Run model was used to study the temperature trends in this region in the atmosphere  (Toihir et al. , 2014). It is know that the mesopause may vary its position through the years. In addition, the studied geographical area can influence the height of mesopause once it changes around the globe. The geographical area chosen for the present study was the southern Brazil region, Uruguay and portions of Argentina × 15 • grid was chosen. In both cases, the altitude range for which the SABER individual profiles were taken was from 80 to 100 km. The TIMED/SABER satellite soundings, from which the daily average temperature profiles were obtained, are in blue. It can be seen from Fig.
TIMED/SABER satellite The TIMED satellite was launched in December 2001 aboard the Delta II launch vehicle at Vanderberg Air Force Base, California. The TIMED satellite helps supply data for studies of the influences of the Sun and human activity in the MLT region. This region is the passage between space and the terrestrial environment, where solar energy is first deposited  (Russell et al. , 1999). The experiments onboard TIMED are focused on a portion of the atmosphere located at about 60-180 km height. The TIMED payload consists of a set of four instruments, one of which is the SABER instrument used in this work. The SABER instrument is an infrared radiometer designed to measure the heat emitted by the atmosphere over a wide range of altitude and spectra. The main objectives of the SABER instrument are to explore the MLT region to determine the energy balance, the atmospheric structure (such as temperature, density and pressure) and atmospheric variation according to the altitude, geographical locations and time  (Huang et al.
, 2010). Errors in the retrieved temperatures in the 80-100 km region are estimated to be in the range from ±1. 5 to 5 K if the kinetic temperature profile does not have a pronounced vertical structure  (García-Comas et al. , 2008). Different from other satellites, TIMED acts in such a way that SABER collects data 24 h a day, allowing the identification of diurnal variation of ozone concentration and temperature (e. g. , thermal tides). This is important in the MLT region since the diurnal variation of ozone and temperature may be dominant  (Huang et al, 2014). The data obtained by SABER provided individual daily temperature profiles unequally spaced in altitude. In order to solve this problem the data were linear interpolated at each × 15 • grid. The 24 h analysis is shown in black; note that this is the same profile shown in Fig.
Furthermore, the studied geographic region and the period of time (2003-2014) differ from previous works, which have been, in the majority of the cases, carried out in the Northern Hemisphere. The possible influences on the temperature variability are mainly related to the semi-annual and annual cycles, the solar cycle, cycles of galactic cosmic rays and planetary waves in the upper mesosphere, among other phenomena. A complementation of SABER temperature data can be given by the AURA satellite in order to find a way to remove gaps and better organize the daily mean temperature in order to improve future works.
Conclusions Using temperature data from TIMED/SABER satellite soundings, the upper mesosphere in the south of Brazil was studied in terms of temperature variability and its trend. This is important since there are few works on this subject over the Brazilian territory and none for the southern Brazil region. This work, therefore, contributed to the study of the upper atmospheric temperature behavior over southern Brazil, showing no substantial trend in the temperature in the mesosphere over the southern Brazilian region and surrounding areas, centered in Santa Maria, RS, Brazil. In this work, the results found that the temperature trend in the 15 • × 15 • case was 0. 58 K decade -1 at 85 km, -0. 02 K decade -1 at 90 km and 0. 41 K decade -1 at 95 km. However, since the results for temperature trends are slightly positive, it can be considered approximately zero per year and it is possible to in-fer that there is no variability tendency of temperature in the mesosphere.
In addition, because it is a geographically poorly studied region, the lack of data also caused interference in the results of temperature trend analysis of the MLT region. However, despite the reduction in the number of days in the 15 • × 15 • grid compared to the 20 • × 20 • grid, the results and comparisons are satisfactory for the analysis of the trend in the region of study. In future works, other methods of measurement can be used as well as data from the Earth Observing System (EOS) Microwave Limb Sounder (MLS) instrument on board NASA's EOS AURA satellite, and even at other interesting sites (e. g. , on sub-Antarctic islands). With the data obtained from AURA, it will be possible to fill the gaps of days without data of the SABER instrument and thus contribute to the study of the upper atmosphere climatology that has increased in recent years.
Acknowledgements. This work was initially started by  FAPERGS (Rio Grande do Sul Research Foundation Support)  and advanced with student assistance from  PRAE (UFSM Pro-Rector for Student Affairs). Mateus S. Venturini  is grateful to both  FAPERGS  ( 2015/2016 ) and  PRAE/UFSM  ( 2017 ) for the assistance. José V. Bageston  would like to thank the  CNPq  for grant no. 461531/2014-3. We thank the  TIMED/SABER  team for the temperature data availability used in our analyses. We are thankful to  CRS/COCRE/INPE-MCTIC (Southern Regional Space Research Center  of the  National Institute for Space Research)  for the support, in terms of infrastructure, for the development of this work. The topical editor,  Ricardo Arlen Buriti , thanks  Ana Roberta Paulino  and two anonymous referees for help in evaluating this paper.
A 28S rDNA PCR detection assay was previously developed to identify Dipylidium caninum DNA inside single fleas collected from both cats and dogs. Sequence analysis of the 28S rDNA fragment indicated two genetically distinct variations of the target region. The two genotypes, so-called "D. caninum canine genotype" and "D. caninum feline genotype", based on host origin, are further investigated and described in this paper. Restriction fragment length polymorphism (RFLP) analysis and hydrolysis probe-based genotyping assays were developed and validated for genotyping D. caninum DNA. The complete mitochondrial (mt) genome of the "feline genotype" was sequenced and compared to the D. caninum mt genome available in GenBank. The molecular characterization of D. caninum isolates collected from infected fleas, and also proglottids collected from dogs and cats, confirmed the existence of two distinct genotypes.
caninum DNA. Secondly, a sensitive, noninvasive nucleic acid detection assay applied for the detection of D. caninum DNA in faeces. And finally, the complete sequencing of the mt genome from a feline host to compare the "D. caninum feline genotype" and the D. caninum mt genome available in GenBank. The objectives of this study were thus to develop the necessary assays for genotyping D. caninum DNA to discriminate between the two D. caninum genotypes. These comparisons allowed the identification and breeding of the two genotypes, in order to further evaluate their biological differences (see  Beugnet et al. , 2018, part 2, [4] ).
All appropriate controls were included. The hydrolysis probe qPCR assays to discriminate between the two genotypes were based on a qPCR assay with the following setup. Primers DC28S-1F and DC28S-1R  [3]  were added to a final concentration of 900 nM each. Probes D_caninum dog (FAM-GTGTGTGCACAGTC-NFQ-MGB) and D_caninum cat (VIC-CCTGTGTGTA-CAGTCG-NFQ-MGB) were added to a final concentration of 200 nm each. qPCR was performed using a QuantStudio 6 instrument fitted with a 384-well block in a final reaction volume of 10 ml using SsoAdvanced TM Universal Probes Supermix (Bio-Rad) under the following cycling conditions: initial denaturation of 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min. Pre-and postread stages at 25°C for 30 sec were included. Analysis was performed using the QuantStudio 6 Real-time PCR system Software.
Sequencing of the mitochondrial DNA Primer pairs DcMitoUni-1F (5'-GGGCTTGTTT-GAATGGTTTGACAAGATAATTTG-3') and DcMi-toUni-1R (5'-CACTTGCTGCCAAACCATTTTAGTT-AAAAAACTAAG-3') were based on the sequence D. caninum partial mt DNA sequence isolated from spotted hyena (Accession number: KF202097) (as published by East et al. [7] ). The 842 bp PCR product was amplified using total DNA isolated from R166 (obtained from the École Nationale Vétérinaire de Maisons-Alfort) and was sequenced using Sanger methodology. The above mentioned primer pairs were reverse complemented, resulting in primers pair DcMitoUni-INV-1F (5-CTTAGTTT-TTTAACTAAAATGGTTTGGCAGCAAGTG-3') and DcMitoUni-INV-1R (5'-CAAATTATCTTGTCAAAC-CATTCAAACAAGCCC). These primers were used at 400 nM final concentration to amplify the remainder of the mt DNA genome using LongAmp ® Taq DNA Polymerase (NEB) using 50 ng total DNA as template.
Mitochondrial genome sequence analysis Illumina Miseq and Sanger sequencing data were assembled, after quality trimming, using the Geneious assembler (Geneious 8. 0. 5). Contigs were mapped to the only available D. caninum mt DNA genome (AB732959), using Geneious assembler. Annotation and similarity percentages of the D. caninum R166 mt genome was performed using the Geneious Live annotate and predict function, making use of AB732959 as reference as well as submitting the final completed mt genome sequence to MITOS  [2]  for annotation. Geneious Live annotate and predict employs a BLAST-like algorithm that includes translation search where sequences are translated in all six frames and compared to the reference. Results from both automatic annotations were in agreement and were also manually checked.