dataesr/scientific-paragraphs-categorization huggingface dataset:
This is due to the overflight taking place over several years and the imaging was conducted by several different camera operators with changing equipment. Photogrammetric reconstruction was performed using Agisoft Photoscan Professional (Version 1. 4. 1. 5925), which uses SfM-MVS to locate and orient each image and produce a dense point cloud of the terrain  (Westoby et al. , 2012). As the images were acquired by handheld cameras they have no geotagging, and as no ground control point could be measured in the field due to hazardous crater access, the resulting point clouds were not referenced. Here, georeferencing was achieved using the ∼1 m resolution point cloud from the Pleiades tri-stereo DEM. We use this dataset to reference the photogrammetric point cloud closest to the Pleiades acquisition date (here 02.
12. 2015) by using point pair-picking registration in CloudCompare. As the SfM photogrammetry point clouds have a much higher resolution compared to the Pleiades point cloud, and to ensure internal consistency within our dataset, all other point clouds were referenced directly against this (02. 12. 2015) high-resolution SfM point cloud using the same manual point-matching method. The georeferencing to the Pleiades dataset could be achieved with an RMS of ±2. 4 m plus an additional error between ±0. 2 m and ±0. 5 m from the subsequent point-matching of the other clouds to the first reference cloud. Using the referenced SfM point clouds, detailed DEMs could be constructed using ESRI ArcMap (Version 10. 5). Here all DEMs were built at 10 cm resolution after performing a statistical outlier subsampling on the point clouds, removing any singular points outside of the standard deviation for the average point distance.
The complete TSX dataset with the seismic timeline is shown in Supplementary Material A, with the relevant data being presented here. TSX and seismic data both indicate the growth of the first lava dome, representing the start of the renewed eruptive activity at Volcán de Colima, occurred in early 2013. The eruptive activity was dominantly constructive with the addition of lava at the summit and the western volcano flanks, and can be observed in the TSX data (Figures  4A, B ). According to helicopter observations, the very first extrusion of lava started on 11 January 2013; however this new dome was destroyed by the ongoing explosive activity. We first identify the lava dome in the TSX image from 19 February 2013 (Figure  4A ). The dome grew westward and by March 2013 resulted in the formation of a lava flow that descended the western flank.
Overview of Structural Data From SfM and Thermal Images The SfM reconstructions performed well on the flight data. The summit area of Volcán de Colima was resolved in all 13 datasets that were considered for the 2013-2016 observation period. We obtained a morphologic time-lapse dataset showing the growth and destruction of the summit lava dome(s) and the growth direction. The exceptionally high resolution of 10 cm allows the identification of small features such as individual explosion pits and lava bombs, and also allows us to identify the structures associated with the direction of dome growth. The complete SfM dataset is shown as shaded reliefs in Supplementary Material C. Our model from the first extrusion in February 2013 shows an elliptical (N-S elongated, aspect ratio AR = 1. 37) lava dome with a diameter of ∼100 m within a ∼160 m wide crater (Figure  5A ).
Conduit Position By integrating photogrammetric data, satellite radar images and numerical modeling methods, we are able to gain insight into the physical processes controlling the lava dome growth at Volcán de Colima, Mexico. Based on deformation studies the location of the feeding conduit could already be inferred in a 2013 dataset  (Salzer et al. , 2014) , which matches very well with the conduit position in our study (Figure  5H ). The July 2015 eruption produced an 80 m deep crater and carved the conduit on the crater floor with the onset of new extrusion in 2016, indicating that the location has been constant for the period 2013-2016. Therefore, we could infer the conduit location (center at 103 • 37'0. 75"W and 19 • 30'44. 881"N) and identify approximate extrusion paths and growth directions of the lava dome in the 2013-2015 growth phase (Figure  5H ).
Another limitation of the model is the use of a 2D axisymmetric half-space to create a 3D model from a profile. This is a common simplification used in several similar studies  (Chadwick et al. , 1988; Corbi et al. , 2016; Le Corvec et al. , 2018)  and is due to the difficult implementation of the large datasets and computing power that are required to use a full 3D surface model. While we manage to capture and include all important features including the conduit and vent positions, it is important to note that our model is derived from a change in topography of a 2D environment and is thus not a complete representation of the real morphology (e. g. , variable crater depth or dome heights cannot be included). Here, we picked the rotated profile location (Figures  5A, 6 ) to be the best representation of the summit morphology.
ACKNOWLEDGMENTS This is a contribution to VOLCAPSE, a research project funded by the  European Research Council  under the European Union's  H2020 Programme/ERC consolidator  grant  ERC-CoG 646858. We thank the  DLR  for support; the acquisition of the spotmode TerraSAR-X data was realized through proposal  GEO1505. DV-B is grateful to  CONACYT-PDCAPN  project  2579. We would like to thank  Jose Manuel Álvarez Nieves  for technical assistance during field work. We would also like to acknowledge  Rogelio García Flores  for providing helpful insight and data on Volcán de Colima photogrammetric surveys. Finally we thank  Sébastien Valade  for providing useful scripts for visualization of the results.
, 1998)  to calculate PyC proportion (% SOC) and content (g PyC•kg -1 soil). While this ratio has been criticized in the literature (e. g. ,  Schneider et al. , 2010) , we used it to compare our values with previous reports of PyC quantitative estimates  (Reisser et al. , 2016). We also used the distribution patterns of BPCA markers as a quality indicator of the PyC. High proportion of B5CA and B6CA were indicative of more condensed and aromatic material  (Wiedemeier et al. , 2015) , while B3CA and B4CA were the product of small condensed units of 3 aromatic rings minimum  (Ziolkowski et al. , 2011). We used paired t-test and multivariate models (see  Soucémarianadin et al. (2018b)  for details) to assess the effects of the three factors (depth, soil class and vegetation type) on the quantity and quality of PyC using R software  (R Core Team, 2016).
Acknowledgments 10 This work was supported by the  French Environment & Energy Management Agency (ADEME)  [APR REACCTIF, piCaSo project],  Campus France  [ PRESTIGE-2015-3-0008 ], the  SOCUTE  project funded by the  "Emergences" program from ville de Paris, National Research Program 68 "Soil as a Resource " of the  Swiss National Science Foundation  and the  University of Zurich Research Priority Programme on 'Global Change and Biodiversity'. We acknowledge three anonymous reviewers for their time and effort in commenting on the manuscript.
Adult tree data Fresh needles from adult trees were lyophiliozed for at least 48 h within a few days of their collection and sent for DNA extraction and genotyping to LGC Genomics (Middlesex, United Kingdom). A total of 315 trees were genotyped at 357 single-nucleotide polymorphism (SNP) loci using KASP arrays and the all-inclusive service from LGC Genomics (Middlesex, UK). We used 267 SNPs from  Roschanski et al. (2016)  and additionally selected 110 new putatively neutral SNPs from the transcriptome assembly of  Roschanski et al. (2016) , based on respective The side shoots point downwards 2 The terminal shoot is not upright 3 Needles are yellow across the whole plant values of Tajima's D between 2 and -2 and dN/dS between 0. 9 and 1. 1, and with low LD with the existing SNPs (r 2 < 0. 1 and p-value > 0.
05). Further, we selected 149 SNPs from the control panel of  Mosca et al. (2012)  that had less than 5% missing data in that study. Three out of the four populations studied in  Roschanski et al. (2016)  were included in our set of 16 populations. δ 13 C of 165 trees adult trees, i. e. ten trees per population on average, was measured at the ETH Grassland Sciences Isolab. Approximately 80 mg lyophilized needle material was milled in 2 ml polypropylene tubes equipped with a glass ball (diameter of 5 mm) (4 min at 30 Hz). Milled samples were directly weighted in small tin capsules (approx. 5 mg, XPR2 microbalance from Mettler Toledo). Sub-samples of approximately 5 mg needle powder were combusted in an elemental analyzer (Flash EA by Thermo Finnigan, Bremen, Germany) coupled to an isotope ratio mass spectrometer (Delta XP by Thermo Finnigan, Bremen, Germany) by a Conflo II interface (Thermo Finnigan, Bremen, Germany).
Environmental data We used downscaled historical climatic data to characterize environmental conditions at each sampling site. In order to obtain the closest representation of the climate for the period when the current populations were established, we used data from 1 January 1901 to 31 December 1978. The choice of this period was justified by two facts: (i) no observation-based climate data go back further in time, and (ii) starting from approximately 1980, the temperature time series are overwhelmed by the effect of global warming  (Harris et al. , 2014). We used the delta method for statistical downscaling  (Hay et al. , 2000)  to obtain mean monthly temperature and precipitation on a 1 km grid scale. The reference climatic data set for downscaling was the 0. et al. , 2017). We calculated several bioclimatic indexes to characterize the environment at each site, including the 19 standard bioclimatic variables  (Booth et al.
Statistical analysis Population structure and demography First, we used the Bayesian clustering algorithm implemented in the software Structure v. 2. 3. 4  (Pritchard et al. , 2000)  to find clusters of genetically related individuals across the sampled populations using the SNP data. We used the admixture model with correlated allele frequencies following  Falush et al. (2003). Further, we included sampling location information to improve clustering performance ("locprior model",  Hubisz et al. (2009) ). We estimated the prior population allele frequency parameter (λ) from the data to account for the fact that SNPs often have rare minor alleles. We estimated λ using K=1 to avoid non-identifiability with the other hyperparameters (λ, α, F). λ was consistently around 0. 65 across ten repeated runs (range: 0.
63-0. 66, median: 0. 65). Then, we tested K values from 1 to 19 using ten independent Markov chains for each K, and 500,000 burn-in iterations and 500,000 iterations for estimation of the membership Second, we estimated the demographic distances between populations from variation in the SNP allele frequencies. For this, we used an admixture F-model (AFM), which assumes that populations have diverged from a common ancestral pool due to genetic drift  (Karhunen & Ovaskainen, 2012). This model can disentangle between small population size and isolation, which are the principal mechanisms underlying drift. We estimated these so-called drift dis-tances, a. k. a. the coancestry matrix, between all pairs of populations using Metropolis-Hastings algorithm implemented in the R package RAFM 1   (Karhunen & Ovaskainen, 2012).
We implemented the model using the R package ASreml-R 3. 0, and constructed the inverse kinship matrix using the function asreml. Ainverse. We performed several tests to check the validity of the above full model. We tested if environmental heterogeneity generated by the experimental designed contributed to the trait variance by comparing models with and without block effect using a Wald-test (wald. asreml). We also tested for the significance of the random effects using a likelihood ratio test between models with and without seed weight and population, and also tested significance of including the pedigree itself (referred to "family" effect). Finally, the above model assumes that the 16 populations have a common V A. This assumption may not hold because the populations were selected from an environmentally and perhaps demographically heterogeneous region.
Adaptive divergence and environmental drivers We used the test of adaptive divergence (S-test) developed by  Ovaskainen et al. (2011)  implemented in the R package driftsel 2 to distinguish between the effects of drift and natural selection in shaping the trait values across the 16 populations. driftsel also implements an animal model. However, it not only accounts for the pedigree, but also the demographic distances between the populations estimated from genetic marker data (see above). To achieve this, driftsel coestimates the additive genetic mean and variance of the trait value in an assumed ancestral population and the deviations from the ancestral mean for each population, i. e. the population effects. Covariates can also be incorporated, and here, we used block and seed weight. The S-statistic is used to summarize the evidence for adaptive divergence across all populations.
Sources of trait variation and evolutionary potential Comparison of animal models including different effects revealed that heterogeneity within the experiment captured by block had a significant effect on the variation of most traits (Table  4 ). For three traits, Bud Break Score 1997 and 1998, and Growth Increment 1997, we were able to compare the effect of the greenhouse. We found that variance components were similar when taking all data or just one of the greenhouses, with slightly higher estimates for one greenhouse only (Table  4 , Fig. 2 ). The effect of year on the traits was addressed indirectly using Bud Break Score and Height that were measured across three years of the experiment in greenhouse 1. We found that seedlings most likely acclimated to the experimental conditions: the block effect disappeared for Bud Break Score 1998 and 1999 and for Height 1998.
Acknowledgments: The project, and in particular the development of the genetic data, was supported by a research grant from the  Center for Adaptation  to a changing environment (ACE) at the  ETH Zürich  and by the group of  Alex Widmer  ( ETH Zürich ). KC was supported by an  ACE  fellowship while collecting the data and by  Marie Skłodowska-Curie Individual Fellowship  ( FORGENET 705972 ) while analyzing the data and writing this manuscript. We are grateful to the Milles forest nursery team for installing, managing and measuring the experimental plant material. We thank  Kristian Ullrich  ( Max Planck Institute ), who helped with the SNP selection, and  Dirk Krager  (WSL), who helped with extracting the climatic data from the data bases. The contribution of several field and lab workers was necessary for this project, in particular, we thank  Olivier Charlandie , who helped with the field sampling,  Rene Graf  and  Fabian Deuber  (WSL), who performed most of the sample preparation, and  Annika Ackermann  ( Grassland Sciences Isolab ), who performed the stable isotope measurements.
This is achieved through both numerical indicators and visual analysis that we outline in this work. A reference task that illustrates our approach is the delay prediction performed with airlines data. The objective is to investigate the ability of streaming classification models to refine their delay prediction after flight departure and before its real arrival. Other model evaluation methodologies rely on the first prediction only, while our extension considers intermediary predictions. We claim that our approach yields model performance assessment better matching use cases where streaming classification models are used, i. e. , where the evolution of the models is desirable and predictions provided by these models may evolve in turn. In summary, the main contributions of this work are the following: -We point out the need for a specific evaluation for delayed labelled streams; -We propose a novel evaluation method to be applied when verification latency takes place, and provide an open source implementation of it 1  that can be used by the community; -We discuss the impact of delayed labels using synthetic and real world data streams of flights' delays and others; -We present empirical experiments highlighting our evaluation procedures and the traditional test-then-train procedure; -Finally, we discuss possible extensions to this work as well as an experiment with a regression problem.
Gomes et al. (2017)  present an approach to evaluate the classification performance assuming a problem setting where the labels are not readily available. It was identified as 'delayed labelling setting' and its definition was quite simple: the learner is evaluated based on its capacity to correctly predict an instance, which ground truth will only be available a fixed number of time units latter. In  Gomes et al. (2017) , authors show that useful information can be obtained when comparing the results for both the immediate and the delayed labelling settings, using the same algorithms and datasets. Plasse and Adams (2016) introduce a taxonomy to determine the delay mechanism and magnitude, present real world applications where delayed labels occur (e. g. credit scoring), notation for the delayed setting and how the set of delayed labels can be used to pre-update the classifier.
Experimental analysis In order to analyse the proposed performance indicators, we have used three synthetic and three real streaming datasets. Furthermore, we have selected stream classification methods illustrating both methods developed to address unique requirements of stream mining, and methods successfully adapted to stream processing needs. The first group of stream classification methods featured in the experiments was Hoeffding tree (HT)  (Domingos and Hulten 2000) , Hoeffding Adaptive Tree (HAT)  (Bifet and Gavaldà 2009) , and recently proposed Adaptive Random Forest (ARF)  (Gomes et al. 2017). Other methods included k Nearest Neighbours (kNN) and Naive Bayes (NB). Finally, the baseline models No Change (NC) and Majority Class (MC) were also used. Hence, methods typically used for comparison purposes in stream mining studies and representing different groups of classification methods i.
This generator can be used to simulate incremental concept drifts, by varying the values of its weights as the stream progresses  (Bifet et al. 2011b). The Hyperplane data represents the case of streams missing the notion of time. Therefore, first the time of every instance is set to instance index in this dataset. Next, labels from original instances S a are removed and labelled instances S b are added at t(S b ) = t(S a ) + 100. Hence, for this data stream we set the latency of true label to a fixed value. As a consequence the stream includes pairs S a = {(x k , ?)},. , S b = {(x k , y k )}. LED The LED data set simulates both abrupt and gradual drifts based on the LED generator, early introduced in  Breiman (2017). This generator yields instances with 24 boolean features, 17 of which are irrelevant.
(2011a) , the synthetic data describes hypothetical loan applications. There are ten different functions 2 that map instances into two different classes. It is possible to simulate concept drift by changing the function. For example, the first function assign instances to class A according to age: age < 40 ∨ age ≤ 65; while the seventh function uses the salary, commission and loan attributes to assign instances to class A: (0. 67 × (salar y + commission) -0. 2 × loan -20000) > 0. A perturbation factor is used to add noise to the data. This factor changes the original value of an attribute by adding a deviation value to it, which is defined according to a uniform random distribution. In our experiments with AGRAWAL, we simulate 4 gradual concept drifts. The size of the window of change for each drift was set to 400; the amount of instances per concept was set to 2000; the functions (concepts) vary from 1 to 5; the total amount of instances was set to 10, 000; and the delay was fixed to 1000.
Airlines The airlines dataset comes from Data Expo 09 3 and contains flight departure and arrival records for all commercial flights within the USA. A part of this dataset constrained to flights arriving to Atlanta International Airport (ATL) has been used. Only departure and arrival events for flights not cancelled and not diverted were included in our analysis. The objective is to predict whether a flight will arrive to ATL before time, on time or delayed based on instance data describing the flight and including features such as distance, planned departure and arrival time, origin airport and carrier. We assume that the instance data becomes available at planned departure time. Moreover, delayed label becomes available at true arrival time. Hence, the time between obtaining the instance data t(S i ) and the time of receiving its true label t(S i ) = t(S i ) + Δt i is the time between planned departure and true arrival.
Not surprisingly, the latency individual labels arrive with strongly varies. Approximately two hours being the most frequent value, but some Δt i values exceeding 13 hours. Before true label of an instance arrives evolving predictions for the instance are expected to be made. We expect temporal dependencies to occur in the data. Electricity The Electricity dataset, made available by J. Gama 4 , contains class labels annotating energy price changes (UP or DOWN). Further details on Electricity data can be found inter alia in  Bifet et al. (2013). Based on the raw Electricity data, we develop a stream of instances reflecting frequent practice in power systems of performing 24hour-ahead prediction  (Kuo et al. 2014; Srinivasan et al. 1995). More precisely, we create out of the raw instances including features such as energy prices and demand, a stream of instances each containing these features for 24 time periods of day d.
The objective is to predict the price change (UP or DOWN) during ith period of day d + 2 i. e. at least 24 hours ahead. Hence, we assume that after the midnight starting day d + 1, all instances containing day d data become available. Next, the true label which arrives on the day d + 2 after i × 30 minutes contains actual price change observed on the d + 2 day in the [(i -1) × 30, i × 30] period, i = 1,. , 48. Therefore, the delay that every true label arrives with varies approximately between one and two days. cover types are 30×30-meter cells obtained from the US Resource Information System (RIS), which are represented by 10 numeric and 44 binary features. This dataset does not contain a "natural" delay, therefore we simulated the delay using 1000 instances.
Experimental results The primary objective of this section is to investigate the insights provided by the evaluation measures proposed in this study. All experiments were performed in the Massive Online Analysis (MOA) framework  (Bifet et al. 2011b ). For each dataset, the results were produced by running Algorithm 1 varying the learning algorithm. Table  1  provides an overview of the results and characteristics of the datasets, including the number of labelled instances |Ω(T )| after which binned performance is reported. In all experiments, K = 10 was used to ensure a balance between the computational cost of polling models for refined predictions and additional insight arising from these predictions. Furthermore, B = 50 was applied to divide the periods between receiving individual unlabelled instances and their delayed labels into 50 sub-periods and produce smooth performance trends.