dataesr/scientific-paragraphs-categorization huggingface dataset:
3d. Ambient f OA CO 2 at the Rocky Mountain forest site shows a moderate oxidation level (0. 1-0. 15), similar to the SE US-CTR (Fig. 5 ). f OA C 5 H 6 O in the Rocky mountain site decreases linearly when f OA CO 2 increases. During the Rocky Mountain study, the intense OH aging of ambient air in a flow reactor shows a continuation of the trend observed for the ambient data, where f OA C 5 H 6 O decreases as f OA CO 2 increases. A linear regression to the combined ambient and OFR data sets (f OA C 5 H 6 O = -0. 013 × f OA CO 2 + 0. 0054) will be used below to estimate background f OA C 5 H 6 O in areas with strong monoterpene and low isoprene emissions. f C 5 H 6 O in ambient SOA from other studies catalogued in the HR-AMS spectral database are also shown in Fig. 5. Most urban oxygenated OA (OOA) are within f OA-Bkg-UB C 5 H 6 O (average 1.
Higher background values of f OA C 5 H 6 O (3. 1 ± 0. 6 ‰ in average) were found in area strongly impacted by monoterpene emissions. f MT-SOA CO 2 is 5. 5 ± 2. 0 ‰, which are substantially lower than for IEPOX-SOA (22 ± 7 ‰), and thus they leave some room to separate both contributions. A f A simplified method to estimate IEPOX-SOA based on measured ambient C 5 H 6 O + , CO + 2 , and OA in AMS is proposed. Good correlations (R > 0. 96) between estimated IEPOX-SOA and IEPOX-SOA PMF are obtained for SOAS, confirming the potential usefulness of this estimation method. Given the observed variability in IEPOX-SOA composition, the method is expected to be within a factor of ∼ 2 of the true concentration if no additional information about the local IEPOX-SOA is available for a given study. When only unit mass-resolution data are available as in ACSM data, all methods may perform less well because of increased interferences from other ions at m/z 82.
The uncertainty of calculated f 82 can be as high as 30 % in the lower fresh OA plumes by considering the uncertainties from quantile average and linear regression. There are also some pure chemical species that exhibit high f 82 values, as shown in Fig. A1c. These species include docosanol, eicosanol, and oleic acid. However, none of these pure chemical species alone contributes substantially to ambient aerosol. The probability density distributions of f OA 82 in studies strongly influenced by isoprene emissions are shown in Fig. A2a. The peaks (∼ 8. 7 ± 2. 5 ‰) are similar in SE US, pristine, polluted Amazon forest, Borneo forest to highresolution f OA C 5 H 6 O (∼ 6. 5 ± 2. 2 ‰), indicating C 5 H 6 O + is the dominant ion at UMR m/z 82 in these studies. Compared to the studies with strong urban and biomass-burning emissions, clear enhancements of f OA 82 in studies strongly influenced by isoprene emissions are still observed, but with less contrast than for in high-resolution data sets (Figs.
(A1) to SOAS-CTR data set; see Fig. A4. The estimated IEPOX-SOA in SOAS-CTR from both background corrections (urban + biomass burning vs. monoterpene) both correlates well with IEPOX-SOA PMF with R = 0. 97 and R = 0. 98, respectively. The regression slopes between estimated fresh IEPOX-SOA vs. IEPOX-SOA PMF are 1. 11 and 0. 94, which are within 15 % of 1 : 1 line. The deviation of estimated IEPOX-SOA from UMR by subtracting the background of MT-SOA influences is similar to that from HR in the SOAS data set, indicating the UMR-based IEPOX-SOA estimation may perform as well as HR in areas with high IEPOX-SOA fractions. For areas with small IEPOX-SOA fractions, more uncertainties may exist in UMR calculation; e. g. , there are wider variations of f OA-Bkg 82 from urban and biomass-burning emissions with oxidation level, whereas a smaller and less variable f OA-Bkg C 5 H 6 O is found in HR.
G. Isaacman-VanWertz  is grateful for an  NSF  Fellowship ( DGE-1106400 ). U. C. Berkeley  was supported by  NSF   AGS-1250569. We acknowledge the logistical support from the  LBA Central Office at INPA  ( Instituto Nacional de Pesquisas da Amazonia ). P. Artaxo acknowledges support from  FAPESP  grants  2013/05014-0  and  2014/05238-8  and  CNPq  support from grants  457843/2013-6  and  307160/2014-9. We acknowledges this work was funded by the  US Environmental Protection Agency (EPA)  through grant number  835404. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the  US EPA. Further, the  US EPA  does not endorse the purchase of any commercial products or services mentioned in the publication. The  US EPA  through its  Office of Research and Development  collaborated in the research described here.
It has been subjected to agency review and approved for publication, but may not necessarily reflect social agency policy. The authors would also like to thank the  Electric Power Research Institute (EPRI)  for their support. M. Riva  and  J. D. Surratt  wish to thank the  Camille and Henry Dreyfus Postdoctoral Fellowship Program in Environmental Chemistry  for their financial support. We thank  J. Crounse  and  P. Wennberg  from  Caltech  for gas-phase IEPOX data in SOAS- CTR  and  DC3 , under support from  NASA   NNX12AC06G. We thank  Lu Xu  and  Nga Lee Ng  from  Georgia Tech  for providing data from their studies. We acknowledge funding from the  UK Natural Environment Research Council  through the  OP3 and SAMBBA  projects (grant refs. NE/D002117/1  and  NE/J010073/1 ). Edited by:  A.
II. K-MEANS-ALGORITHMS The purpose of the partitioning algorithms is to construct a partition of N objects into a set of K clusters. These algorithms require an initialization step where seeds are chosen. The most common algorithms of this category of clustering are: K-means  [5] , K-medians  [6] , K-modes  [9]  and K-medoids  [10]. Each of these algorithms depends on the number of clusters K (a priori set), the input features, the initial vector of centers (k 1 ,. , k K ), the similarity measure and the criterion used to evaluate the quality of the partition. The choice of one of these algorithms depends on: (i) the nature of the datasets, (ii) the desired result (mean, medoid. ), and (iii) the complexity of the algorithm. In this paper, we are focused on one of the most widely used algorithm: The K-means method.
This confidence is determined by calculating the percentage of correctly classified objects with respect to the total number of objects in the data set. In this algorithm, the number of clusters is an input. Eick et al. [4]  introduced four representative-based algorithms for supervised clustering: SRIDHCR, SPAM, TDS and SCEC. In their experimentation, they used the first one (i. e SRIDHCR)). The greedy algorithm SRIDHCR (or Single Representative Insertion/Deletion Steepest Decent Hill Climbing with Randomized Start) is mainly based on three phases. The first one is the initialization of a set of representatives that is randomly selected from the dataset. The second is the primary cluster creation phase, where instances are assigned to the cluster of their closest representative. The third one is the iteration phase where the algorithm is run r times: In each time 'r', the algorithm tries to improve the quality of clustering, for instance, by adding a non-representative instance or by deleting a representative instance.
It measures the agreement between the generated partition for the K-means and the partition given by the target class. We illustrate the standard K-means using both our proposed approach and the usual unsupervised initialization approaches on a toy dataset (see Figure  2 ). This dataset contains 510 instances, distributed among five classes (C = 5) : 'ear left' (blue), 'ear right' (green), 'head' (red), 'shoulder' (cyan) and 'noise' (purple); Each class contains respectively 290, 100, 100, 10 and 10 instances. Here, the standard K-means is applied in the following conditions: L2 as a norm, mean as a centroid, Mean Squared Error (MSE) as a criterion to select the best replicates and statistical normalization (SN)  4 as a preprocessing step. MSE criterion : Using the standard K-means, the emplacement of the initial center has often an impact on the quality of the generated partition.
Our supervised initialization approach requires at least an initial center per class (K ≥ C). However, for this dataset, the initialization of a center in the minority class (i. e. 'cyan' class) could deteriorate the quality of the final solution in terms of MSE (the initial center couldn't move outside the cyan points). Figure  3  illustrates this point: the generated partitions using the usual initialization approaches (Rand, Sample and K++) have a better performance in terms of MSE than the K++R approach where K = C. Note that, the difference between the MSE of K++ and K++R is only 8. 9% (0. 326 against 0. 355) when the number of replicates is equal to 1000 and 1 respectively. This shows that the K++R is a faster algorithm than the others. ARI criterion: For this example, when the number of clusters is equal to the cardinality of the target class, using an unsupervised initialization approach, the chance to select more than one seed in the same class is high (especially in the majority class).
Likewise, when K > C, the K++R has the same predictive performance 'quality' as the unsupervised approaches. The preliminary experiment conducted on this dataset has shown the following result: (1) the predictive performance of the generated partition using the proposed initialization method is better than the performance using the usual initialization approaches (when K = C = 5), (2) K++R is faster than the others approaches (reach a good performance with a few number of replicates), and (3) unsupervised initialization approaches are better than K++R in terms of MSE. To further evaluate the efficiency of our proposed approach in a supervised context, we propose an extensive comparison with the usual initialization approaches for several benchmark datasets.
A. Protocol 1) Initialization method: To test the validity of our proposal, we compare our supervised initialization approach (K++R) to the three most popular unsupervised approaches (see Section II-B) such as Rand (as a baseline), Sample (which exhibits interesting performance as described in  [14] ) and K++ (which has theoretical foundations and a good performance while keeping a reasonable complexity). 2) Datasets: To evaluate and compare the behavior of different initialization approaches in terms of their capacity to help the traditional clustering in a supervised context, we have performed tests on different datasets of the UCI repository  [30] , chosen for their diversity in terms of number of clusters, number of features (categorical and continuous) and number of instances (see Table  I ).
It seeks to estimate the univariate conditional density (i. e. P (X m i |Class j ), where m ∈ {1,. , d} and j ∈ {1,. , C}). To obtain this estimation a supervised discretization method is used for continuous features  [32]  and a supervised grouping method is used for categorical ones  [33]. To exploit the information given by the first step, a recoding phase is then required. : Let D = {(X i , Y i )} N 1 denote a training dataset of size N , where X i = {X 1 i ,. , X d i } is Each feature from the instance X i is recoded in a qualitative attribute containing I C recoding values. As a result, the initial vector (X i ) containing d features (continuous and categorical) becomes a new vector (XR i ) containing d × C real components: log(P (X m i |Class j )), j ∈ {1,. ,C}, m ∈ {1,. ,d}. To give an idea of the good impact of this preprocessing step, using the previous dataset (Figure  2 ), the obtained performance 'quality' (using ARI criterion) is equal to 0.
1) Evolution of the error reconstruction: The generated partition contains K centers (k 1 ,. ,k K ). Each center is a vector of Z features, where Z is the number of features after the supervised preprocessing process. So, for a given dataset, the value of Z is then equal to d × C = (M n + M c ) × C (see Table I). To evaluate the quality of the generated partition in terms of instances similarity in each cluster, the Mean Squared Error is used (see equation 1). M SE = 1 N 1 Z 1 K N i=1 Z z=1 K t=1 i∈kt (XR z i -k z t ) 2 (1) where XR i is the new vector of the i-th instance after the preprocessing step (using Conditional Info); It contains Z = d × C components. To our knowledge, the K++ method is quite used to initialize seeds for the K-means algorithm. For this reason, we compare it to our proposed method and other unsupervised methods using the MSE criterion.
Among the three unsupervised initialization method, K++ is the best one. However, it is recommended to use it with a number of replicates at least equal to 10 or greater for certain datasets. For the K++R method, its average value of MSE remains unchanged for all datasets whatever the number of replicates (see Table  III ). Indeed, when the number of clusters is equal to the number of classes, the proposed method is a determinist approach (i. e. it follows the Rochio solution (see Section IV)). We compare now the quality of the generated partition in terms of MSE, using in each time K++ and K++R. Table  IV  presents the comparison results (in percentage) for the MSE of K++R and K++R, for R ∈ {1, 10, 100, 1000}. This table shows that when we combine the proposed initialization method with the supervised preprocessing approach (see Section V-A), K++R is competitive to K++ method in terms of MSE when R=1 (i.
e. it exhibits better or similar MSE than K++). When R increases, the K++R approach exhibits MSE not exceeding 5% for 10 datasets. 2) Evolution of the ARI: The above experiments show that the proposed method can reach a good performance in terms of MSE compared to the other popular unsupervised initialization methods. Now, to achieve the goal of the supervised clustering, we have to prove that the traditional K-means with the proposed method could also reach a good predictive performance (using a supervised criterion). Table  V  presents the average predictive performance (using ARI criterion) of the partition of the traditional K-means using supervised and unsupervised initialization method. From this results, we can see that, whatever the number of replicates (R), the proposed method remains the best one for 9 datasets out of 15 and similar to the others but with one replicate (i.
e the traditional K-means with the proposed method is 10 times (or more) faster than using K++). Now, if we choose another supervised criterion to measure the quality of the generated partitions, the obtained conclusion remains the same. Figures  6  and 7  present respectively, for all used datasets, the quality of the generated partitions using Accuracy 7  (ACC) and Balanced Accuracy 8  (BACC)criterion, in the case R=1000 for 'Rand', 'Sample' and 'K++' and where R=1 for 'K++R'.
2013). Thus the larger simulated mid-Pliocene EASM precipitation in coupled models than their atmospheric components is essentially driven by this warmer SST biases between CGCM and AGCM over South China through inducing larger increase in ∂v * ∂y over EASM domain in coupled models than corresponding AGCMs. A data/model comparison on this area should be carried out in near future to investigate whether the SST in couple models or PRISM3 SST are realistic. In brief, multi-model comparisons demonstrate that the relationships to explain the differences between CGCM and AGCM in the case of IPSL-CM5A have been confirmed when using the model database of PlioMIP. This result pinpoints the essential impact to have reliable SST reconstruction to infer the EASM evolution during mid-Pliocene.
MATERIAL AND METHODS The porosity (Ф) and gas permeability (K) data of 115 samples, including those from six drill cores (E6-1/84, E7-1/82, South Kandava 24 and 27, Dobele 91 and 92) described in old exploration reports  (Silantev et al. 1970; Dmitriev et al. 1973; Babuke et al. 1983; Andrushenko et al. 1985) , from the Liepaja-San borehole (GEOBALTICA project,  Shogenova et al. 2009a ) and the ones recently measured in IFP Energies nouvelles  (IFPEN, French  Petroleum Institute, Paris)  (Shogenov et al. 2013a (Shogenov et al. , 2013b)) , were used for the classification of the reservoir quality of sandstones. Gas permeability was calculated as an average value of reported permeability measured in horizontal and vertical directions. A more detailed description of permeability measurements from the Liepaja structure (Liepaja-San borehole) is given in  Shogenova et al.
(2009a). The physical properties of 139 samples from seven boreholes (offshore E6-1/84 and E7-1/82, onshore South Kandava 24 and 27, Dobele 91 and 92, Liepaja-San), measured recently or reported earlier, were used in this study. The K values of 127 samples and Ф values of 128 samples, grain density of 102 samples, bulk density of 129 dry samples, P-wave velocity (V P ) in 60 dry samples and S-wave velocity (V S ) in 10 dry samples comprised the recent and earlier data  (Shogenova et al. 2001b; Shogenov et al. 2013a Shogenov et al. , 2013b)). Twelve rock samples of the reservoir sandstones of the Deimena Formation from four boreholes drilled onshore in the South Kandava structure and offshore in the E6 and E7 structures (Fig. 1 , Table  1 ) and stored in the Latvian Environmental, Geological and Meteorological Centre were used for the CO 2 injection-like alteration experiment conducted at the IFPEN.
The appearance of new pores, which take up 1-5% of intragrain volume, was determined before the alteration experiment (left). Visual analysis confirms laboratory measurement data (Table  5 ). The zoomed image (lower left) of the thin section before alteration shows very thin interconnections between pores within carbonate cement, which makes the propagation of gases and liquids almost impossible (permeability = 0. 001, Table  5 ). Spotted dissolution of cement with the destruction of cement matrix and locally quartz grains was observed after alteration (right). This phenomenon is responsible for drastically increased porosity and permeability after alteration (Table  5 ). Sample Kn27-4 was of 'very low' reservoir quality sandstone. During the alteration experiment its reservoir quality improved up to 'high-2' (class II), 'very appropriate' for CGS (Table  1 ).
Acknowledgements. This contribution is a part of  K. Shogenov 's PhD study and is partly supported by the institutional research funding programmes (projects  SF0320080s07  and  IUT19-22 ) of the  Estonian Ministry of Education and Research  and the  EU   FP7  ' CGS Europe ' project No. 256725. IFP Energies nouvelles (IFPEN) performed the alteration experiment. All the data presented in the study are the property of the authors and available on request from the first author (kazbulat. shogenov@ttu. ee). We are grateful to the  Latvian Environmental, Geological and Meteorological Centre  for providing samples, to  Jean Guelard  ( IFPEN ) for help in permeability measurements and guidance in the petrophysical laboratory, our colleagues  Olle Hints  and  Maargus Voolma  from the  IG TUT  for their kind assistance with SEM adjustment and  Jüri Ivask  ( IG TUT ) for help during article preparation.
Viral Orthologous Clustering An in-house bioinformatic pipeline was written to cluster together homologous EPV and BV proteins and to search for cellular and viral homologs of each EPV/BV clusters. First, we performed a protein secondary structure alignment clustering, using the jackhmmer program of the hmmer3 package  [25] , in an all-against-all alignment (threshold: e-value = 10 -6 ) of all protein members of Entomopoxvirinae sub-family and Baculoviridae family found in the non-redundant protein database of the NCBI, including the newly described Adoxophyes honmai EPV (AHEV), Choristoneura biennis EPV (CBEV), Choristoneura rosaceana EPV (CREV) and Mythimna separata (MySEV) predicted proteins  [14]. Then, with the ClustalOmega program  [26] , protein multiple alignments were conducted on clusters shared by both virus families, prior to hidden Markov chain (HMM) profile building.
By using hmmsearch program of the hmmer3 package, HMM profiles were aligned to the non-redundant protein database and to the insect EST and TSA databases of the NCBI, both of which were initially converted into protein sequences using the metagenomic option of Prodigal program  [27]. Finally, for each sequence found, we searched for taxonomic information and selected six categories: (1) other insect large dsDNA (including nudiviruses, ascoviruses, iridoviruses and hytrosaviruses); (2) non-insect large dsDNA viruses; (3) invertebrates; (4) vertebrates; (5) plants' and (6) bacteria. If needed, we limited the results to the top 20 matches for each category for efficient phylogenetic reconstruction.
Entomopoxviruses and Baculoviruses Share Accessory Genes We developed a pipeline, to first cluster all available EPV and BV proteins by secondary structure alignment and to align them against different NCBI databases. Out of 487 viral protein clusters, we found 33 clusters of homologous genes shared by EPVs and BVs (blastp e-value threshold: 1e-6), including 26 with similarities to cellular proteins and 21 with similarities to other families of large dsDNA viruses (Table  1 ). As expected, we found only one core gene, the DNA polymerase, among these homologous clusters. The remaining genes were from the accessory genomes. Several genes encoded essential cellular enzymes such as: the Cu/Zn superoxide dismutase, the ribonucleotide reductase small subunit, the dUTPase, the ubiquitin and the matrixin metalloproteinase.
Viruses Infecting the Same Hosts Share Homologous Genes Four EPVs in our dataset had overlapping host species with BVs, allowing the investigation of gene convergence in sympatry. The cluster "unknown XecnGV orf138" (xc138) was the only one to show substantially higher sequence similarities (Table  1 , e-value = 0) between an EPV, MySEV and a BV, Xestia c-nigrum granulovirus (XecnGV) (92% over 650 aa), than between XecnGV and its close relative Pseudaletia unipuncta granulovirus (PsunGV) (71% over 650 aa). Singularly, these three viruses are able to infect the host Mythimna separata  [29]  as well as a number of other noctuid moths. Pseudogenized forms of xc138 were detected in the genome of Helicoverpa armigera granulovirus (HearGV), which is a sister species of XecnGV, and in the genome of Heliothis virescens ascovirus 3g (Ascoviridae, HvAV-3g).
However, we could not detect any xc138 cellular homolog either by bioinformatics data mining or by PCR from M. separata, M. unipuncta, M. riparia, Xestia c-nigrum, Trichoplusia ni, Chrysodeixis chalcites or Agrotis segetum genomic DNA. In contrast, we were able to amplify a xc138 fragment in a closely related EPV of MySEV: Heliothis armigera entomopoxvirus (HAEV) (Genbank accession number: KJ683043). The comparison of the DNA polymerase phylogeny, representing the virus species tree, with the xc138 phylogeny highlighted two xc138 HGTs have occurred between EPVs and BVs (Figure  1a ). Reconciliation analyses by bipartition dissimilarity  [30]  proposed that xc138 was transferred once between MySEV and the ancestor of XecnGV and HearGV, and a second time between HAEV and PsunGV (Figure  1a, b ).
Thus EPVs might have captured an ancestral form of the fusolin from other cellular organisms than their natural hosts. The window for such a gene capture is necessarily smaller than for a gene from a natural host, reducing the opportunity for recurrent gene transfer into different virus families. Consequently, the opportunity to acquire this gene from a cellular organism might have happened only once thus in only one virus family. Fusolin confers a significant adaptive advantage in per oral infectivity  [36] , which is the natural route of infection for both EPVs and BVs. Transmission of an ancestral fusolin gene from EPVs to BVs could have occurred afterwards, but still early in the evolution of both groups, allowing the BV lineage to adapt more efficiently to lepidopteran hosts. Even if gene transfers between viruses are possible, we posit that they are relatively rare events in comparison with HGT from hosts, which are the majority in our dataset.