dataesr/scientific-paragraphs-categorization huggingface dataset:
The entire object-based methodology is implemented within eCognition Developer 9 ® software, from the generation of meaningful objects from the RS data (i. e. , image segmentation), to their classification into hydromorphologically meaningful classes. Because of the strong similarities (both spectrally and topographically) of some of the classes (as explained in Section 2. 2), the riverscape units classification problem was tackled in two phases. In the first step (Step 1 of Figure  5 ), a Machine Learning (ML) object-based classification was developed for mapping the main classes, i. e. , Water Channel, Unvegetated Sediment bars, Sparsely-Vegetated units and FloodPlain units (Level 1 of Table  1 ). Here, the main purpose was to build a supervised classifier able to automatically discern the main land-cover classes found in the active part of the channel (i.
e. , WC, US and SV from Table  1 ) from the FloodPlain unit class (FP from Table  1 ), in order to automatically define the boundaries of the total active channel. For this reason, we propose a multilevel hierarchical segmentation approach based on the two sources of RS data available in order to generate objects that are as closely related to real-world objects. The first level of the hierarchical segmentation (Level 1 of Figure  5 ) was produced within eCognition using the multi-resolution algorithm  [46]  with the Slope layer alone (layer 6 of Figure  2 ). Thereby, different terrain features of similar slope, emerging from ongoing morphological processes of the river, might be distinguishable. A finer sub-level segmentation (Level 2 of Figure  5 ) was then produced with the multi-resolution algorithm, using the four spectral layers available, equally-weighted: Green, Red and Near infrared spectral bands plus NDVI (respectively layers 1,2,3 and 4 of Figure  2 ).
Multilevel, Machine Learning Classification of Riverscape Units and in-Stream Mesohabitats A multilevel, supervised classification was performed using Random Forest (RF)  [47]  and Support Vector Machine (SVM) algorithms. In the field of RS, both classifiers proved to be among the most powerful ML algorithms, especially when classifying high-dimensional pixel-based datasets. In literature, during the recent years they started to be popular also for object-based approaches  [48] [49] [50]. Moving from pixel-based techniques towards object-based, the feature space.
Multilevel, Machine Learning Classification of Riverscape Units and in-Stream Mesohabitats A multilevel, supervised classification was performed using Random Forest (RF)  [47]  and Support Vector Machine (SVM) algorithms. In the field of RS, both classifiers proved to be among the most powerful ML algorithms, especially when classifying high-dimensional pixel-based datasets. In literature, during the recent years they started to be popular also for object-based approaches  [48] [49] [50]. Moving from pixel-based techniques towards object-based, the feature space data to be classified significantly increases, augmenting the classification complexity and causing problems to standard classifiers traditionally used in object-oriented image analysis, e. g. , Nearest Neighbor classifiers. For this reason, RF and SVM are particularly promising in the context of this study.
Object features were calculated for both Levels 2 and 3 segmentations (Figure  5 ) and grouped in different categories. Different combinations of features were tested with the aim of assessing which produce the highest classification accuracies for either riverscape units or mesohabitat mapping. Table  2  illustrates the different groups of input feature sets calculated. From the VHR imagery, ten "VHR features" (1) were extracted by eCognition software: mean and standard deviation of the four spectral layers (1 to 4 of Figure  2 ), plus Brightness and Max differences  [51]. From the LiDAR-derived products, we grouped the mean and standard deviation of the DTM and Slope (respectively, layers 5 and 6 of Figure  2 ) as the "LiDAR features" group  (2). The mean and standard deviation of the DDTM layer (layer 7 of Figure  2 ) comprised the "DDTM features" group (3).
Fifteen "Geometric features" (4) and twelve "Texture features" (5) were calculated by the eCognition software, the latter based on Haralick  [52]  and using both GLCM and GLDV approaches on the four spectral layers, among which: homogeneity, contrast, mean, etc. For a more detailed explanation of the texture and geometric features calculation, the reader is referred to  [51, 52]. At the Level 2 segmentation (Figure  5 ), sample objects were collected based on visual interpretation of VHR imagery, for the Level 1 land-cover classes proposed in Table  1 : Water Channel, Unvegetated Sediment bars, Sparsely-Vegetated units and FloodPlain units, resulting in two sets of randomly and spatially-distributed training and validation objects. Table  3  lists the number of samples collected. It is clear that FP class covers most of the analyzed area, and therefore the number of samples collected for this class is much higher than the number of other classes.
The training samples collected for the FP class cover 19% of the entire analyzed area, while the validation samples of this class cover 15% of the total area. Considering the whole ground-truthing dataset, the training samples represent about 28% of the whole area and the validation samples about 23% (Table  3 ). Validation objects were used for the accuracy assessment, based on Kappa values and per-class producer and user accuracy comparisons. A similar ML approach was also adopted for mapping in-stream mesohabitats, performed at the segmentation Level 3 (see Section 3. 1, Figure  5 ). Training and validation samples were manually collected on VHR imagery, but in this case for the classes: Pools, Riffles, Runs and Standing Waters (Table  1  and Figure  3 ). A few areas affected by sun glint on water channels surfaces were not included in the training and validation datasets.
36% of the SV samples were not classified as SV but labeled as other classes: 19. 11% of the pixels as FP, 1. 58% as US and 0. 67% as WC (Table  5 ). This misclassification occurs predominantly for the vegetation patches located within the TAC boundaries, underlying some of the limits of the classifier in discerning between sparse and dense vegetation that can be found in the FP class. This type of error, however, would have little effect on hydromorphological characterization. 2 ) with SVM and RF. The step 2 post-classification phase (Step 2 of Figure  5 ) developed to classify the Level 2 riverscape units described in Table  1 , was also meant to remove the main CE and OE and to reduce the typical "salt and pepper" effect of small objects. In order to reduce the CE and OE errors, several rule-sets were developed following an expert-based approach, under eCognition software.
Several attempts were tested in order to obtain the optimal expert-based solution. For example, the CE of the US class was improved by imposing two threshold conditions: distance to WC ≥ 200 pxl and Mean DDTM value ≥ 250. US objects satisfying these conditions where labeled as FP class. Thanks to the   2 ) with SVM and RF. The step 2 post-classification phase (Step 2 of Figure  5 ) developed to classify the Level 2 riverscape units described in Table  1 , was also meant to remove the main CE and OE and to reduce the typical "salt and pepper" effect of small objects. In order to reduce the CE and OE errors, several rule-sets were developed following an expert-based approach, under eCognition software. Several attempts were tested in order to obtain the optimal expert-based solution. For example, the CE of the US class was improved by imposing two threshold conditions: distance to WC ě 200 pxl and Mean DDTM value ě 250.
Conclusions The multilevel GEOBIA framework presented in this paper proves to be a new powerful tool for semi-automated classification of essential geomorphic features, in particular for the characterization of the natural fluvial corridor, the delineation of the active river channel, and for in-stream mesohabitat mapping. GEOBIA allows for the integration of different sources of RS data, such as VHR imagery and LiDAR data, in a flexible but robust environment. Thanks to its hierarchical, multilevel capability, the potentials of VHR imagery and LiDAR data are exploited at each level in different ways by combining different object features. Compared to previous studies that solely applied spectral information for a similar classification target  [8, 22, 54] , here for the first time spectral and topographic information have been hierarchically integrated for the characterization of the riverscape units providing a number of benefits.
First, the method performances are higher than all previous attempts reported in literature (best performances result in K values of 0. 91 for riverscape units and 0. 83 for in-stream mesohabitats), although validation datasets are subject to the operator subjectivity and therefore present difficult comparisons. Second, the method proposed allows to automatically delineate the active river channel boundary from other floodplain units, at the same time mapping all the vegetation units within the riverscape environment, such as Riparian Densely-Vegetated units or Sparsely-Vegetated units of Islands. These classes have a hydromorphological meaning of key importance, and, in cases where sequential acquisitions of the same kind might be available, the proposed methodology would be a more objective monitoring tool for tracking changes in fluvial processes.
The Machine Learning classification results show that topographical information is a relevant data source that enhances the identification accuracy of the main riverscape units (Water Channel, Unvegetated Sediment bars, Sparsely-Vegetated units, FloodPlain units). In particular, the Detrended Digital Terrain Model (DDTM), which reports the elevation of the objects with respect to the river network. The best performance of mapping riverscape units was produced when VHR and DDTM features were combined, for both SVM and RF. The DDTM is a powerful descriptor of AC-Floodplain connectivity and enables the classifiers to discern between different riverscape elements with similar spectral signatures: for example a bar of bare sediments within the AC and a bare arable crop field in the floodplain, or a patch of sparse vegetation in the AC and a sparsely-vegetated area in the floodplain.
However, an important limitation when classifying in-stream mesohabitats is the effect of sun glint. Some areas affected by this phenomenon were neglected in this study. However, for future large-scale applications, this aspect needs to be addressed more systematically. We believe that the semi-automated GEOBIA framework proposed in this paper provides notable potential to enrich the availability of hydromorphological data, opening novel perspectives in the river science, which has been characterized so far by a chronic lack of suitable river geomorphological data over large scales and through time  [55]. The application of the proposed method to the entire Piedmont Region, where the same dataset is already available, is now a logical and feasible possibility. Similar datasets are nowadays starting to be available for most European Member States  [33]  and most Member States have already planned similar RS acquisition campaigns in the future.
In this context, and considering the remarkable method's flexibility to the use of different RS input features, there will be ample opportunities to broadly apply the proposed technique. If classification of riverscape units and in-stream mesohabitats will be applied in the future at regional scales around Europe and repeated through time, it will yield an unprecedented dataset of continuous riverscape features that would allow monitoring and characterizing the hydromorphological status of river systems coherently through time in a semi-automated way. This would become a valuable source of data for fluvial geomorphologists: it would allow, for instance, for the design of survey campaigns and restoration targets based on quantitative, objective and repeatable indicators, an urgent and challenging requirement of the WFD, which has not yet been addressed by European Member States.
Sackin, 1972). Of course many diversification mechanisms can lead to the same phylogenetic tree balance  (Jones, 2011)  and so such indices cannot be used on their own to characterize the way the reconstructed tree was generated. However, they may be used to rule out some scenarii. For example, several papers  (Mooers and Heard, 1997; Aldous, 2001; Blum and François, 2006)  point at the fact that the reconstructed trees of the TreeBase database are on average much more unbalanced than expected under the most well-known model of speciation, the Yule model  (Yule, 1924). In this model, every species branches into two species at the same rate (which may vary in time but remains identical for all species) and there is no extinction. The Yule model is the best known example of an evolutionarily interpretable model of speciation due to the following three features: • it is based on an incremental evolutionary construction whereby the tree grows by splitting one of the current leaf nodes which represent the set of extant lineages, • it can be defined jointly on the product space of tree topologies and branch lengths, • the distribution it induces on coarser resolutions of the tree space can be obtained.
We provide several examples in the case α = β of  Blum and François (2006) , in particular to discuss the balance of the trees obtained as a function of β. For completeness, we also propose a continuous-time process of leaf splitting and freezing such that the shape of the tree obtained after N events (regardless of branch lengths) has the same distribution as that obtained through the generating, organizing and deleting processes after N steps. Finally, in the Supplementary Material we give SageMath/Python code to produce these trees at several resolutions as well as a demonstration of the code for the case of the Yule process with four leaves. In the Supplementary Material, we also discuss a reversibility result describing how to choose a pair of sibling leaves (a cherry) in an unranked planar tree with n + 1 leaves created through the generating and organizing processes with n steps, in such a way that the tree with n leaves obtained by removing this cherry has the same law as a tree we would have obtained from the GO processes with only n -1 steps.
But the event that i j = k depends only on the generating sequence ( Gi ) 1≤i≤k-1 , since it depends only on the current length of all active leaves. Consequently, i j and B i j are independent random variables (recall that all components of ( Gi ) are independent of each other). A simple argument then shows that the law of (B i 1 , B i 2 ,. , B in ) is the same as the law of (B 1 , B 2 ,. , B n ), which in turn guarantees that the interval labels of the leaves are also equal in law. Lemma 2 is proved. Algorithm. The code developed for this work is given in the Supplementary Material. It is also publicly shared at https://cloud. sagemath. com/projects/2c5f7f68-e689-4c70-a4b4-5b5d4dc4f93f/files/2015-10-27-082849. sagews. def prob_RPT(T,a,b): '''probability of ranked planar tree T under beta-splitting model a,b>-1, where (a+1,b+1) are the parameters of the beta distribution''' # non-cherry splits ncspS=filter(lambda x: x!=(0,0), splitsSequence(T)) return prod(map(lambda x:beta(x[0]+a+1,x[1]+b+1)/beta(a+1,b+1),ncspS)) def prob_PT(T,a,b): '''probability of planar tree T under beta-splitting model a,b>-1, where (a+1,b+1) are the parameters of the beta distribution''' # non-cherry splits ncspS=filter(lambda x: x!=(0,0), splitsSequence(T)) return prod(map(lambda x: binomial (x[0]+x[1],x[1])* beta(x[0]+a+1,x[1]+b+1)/beta(a+1,b+1),ncspS)) def prob_RT(T,a,b): '''probability of ranked (nonplar) tree T under beta-splitting model a,b>-1, where (a+1,b+1) are the parameters of the beta distribution''' assert(a==b) spS=splitsSequence(T) numSplits=len(spS) # non-cherry splits ncspS=filter(lambda x: x!=(0,0), spS) numCherries=numSplits-len(ncspS) probRPT = prod(map(lambda x:beta(x[0]+a+1,x[1]+b+1)/beta(a+1,b+1), ncspS)) return 2^(numSplits-numCherries)*probRPT sps gives the list of the m = 10000 ranked planar trees sampled by MakePartitionAndTree.
Acknowledgments. R. S. thanks  Mike Steel  for posing the problem and R. S. and A. V. thank  Hélène Morlon  for generous introductions to models of diversification. They also thank the referees for their careful reading of the manuscript. R. S. was partly supported by a Sabbatical Grant from  College of Engineering (University of Canterbury, NZ), External Consulting for Wynyard Group (Christchurch, NZ ), and a Visiting Scholarship at  Department of Mathematics (Cornell University, USA ). R. S. and A. V. were supported in part by the  chaire Modélisation Mathématique et Biodiversité of Veolia Environnement-École Polytechnique-Museum National  d'Histoire Naturelle-Fondation X.
Acknowledgments We thank  C. Le Corre  and  L. Garnier  for help in insect rearing. This work was supported by a PhD grant to A. A. , and research grant from  Region Pays de la Loire  to S. A. and C. G. , as well as a grant from the  National French Funding Agency ANR  ( ANR-12 ADAP-0012-01 ) to S. A. , C. G. and H. T. -L. A. M. was supported by a grant from the  Agronomy Institute Chott Meriem, Tunisia.
This work was supported by a PhD grant to A. A. , and a research grant from  Region Pays de la Loire  to S. A. and C. G. , as well as a grant from the  National French Funding Agency ANR  ( ANR-12 ADAP-0012-01 ) to S. A. , C. G. and H. T. -L. A. M. was supported by a grant from the  Agronomy Institute Chott Meriem, Tunisia.
Statistical analyses. The following analyses were performed using the lme4 package of R software  80. 1) Statistical analyses of worker bee-foraging performance were performed in two phases: first, in by modeling foraging performance over time with a non-linear logistic function with two parameters (α = instantaneous growth rate of foraging, β = maximum quantity of cumulated forage); and second, by applying a generalized linear mixed model to the parameters α and β with a Gaussian distribution, setting identity as the link function and the year of the experiment as a random effect. 2) In the same way, a generalized linear mixed model was applied to analyze the effects of Fipronil on drone and fertility parameters. 3) The difference in queen weight between the control and treated subsets was estimated by t-test after confirming normality.
Acknowledgements Special thanks to  D. G. Biron  for useful advice in the preparation of this manuscript. Thanks to  Yannick Poquet  and  Fanny Mondet  for technical help and assistance. This study was supported in part by the  European Community program  for French beekeeping coordinated by the  French Ministry of Agriculture  (FranceAgriMer agreement  11-42R ),  Région PACA, the department "Santé des Plantes & Environnement" of the French National Institute for Agricultural Research (INRA) , and  Apiculture Remuaux Sud-Est company.
Study site and subjects Data were collected over 3 months from September to November 2009, in a nest-box breeding colony of wild zebra finches at Fowlers Gap Arid Zone research station, Western New South Wales, Australia (31°05′S, 142°42′E) (further details of study site are in  Griffith et al. 2008). Experiments were approved by the Animal Ethics Committee of Macquarie University, New South Wales  (Animal Research Approvals N° 2009/032 and 2007/038). Throughout the entire breeding season, birds were provided with commercial finch seed (e. g. , canary seed and white millet) in temporary feeders, placed throughout the study site of a 1 km radius around a dam (see  Mariette et al. 2011  for maps and details). Nest boxes were checked daily once parents initiated laying. After hatching, nestlings were individually marked by using specific combination of feathercuts on their head, so that we could identify individuals throughout the nestling period.
Analysis of begging calls A total of 13 682 begging calls were extracted from the original recordings of 29 nests on 89 nestlings using a custom-written program (PYTHON programming language, open-source libraries). Begging calls are produced in bouts, which consist of a rapid succession of short syllables. We analyzed the information that could be extracted from single calls and did not analyze the structure of the whole bout, but we controlled in statistical analyses for the potential effect of being produced early versus late in the bout on call structure (see below). Calls were analyzed using the Seewave package  (Sueur et al. 2006 ) implemented in R software  (R Core Team 2012, version 3. 1. 3. ). Each call was first bandpass filtered (0. 1-10 kHz) according to the zebra finch audiogram  (Hashino and Okanoya 1989).
2011). All parental visits to nests and feeders were monitored during the experiment from the day before the first dose until 3 days after the second dose (8 days in total). The data from the decoders was processed and integrated to calculate the number of visits per day (feeders and Overview and timeline of experimental procedure. First day of experiment was used as a control for basal parental behavior, as the nests were left undisturbed. Oral administration of either exogenous corticosterone dissolved in peanut (CORT) or oil only (OIL) was done when nestlings were, respectively, between 7 and 9 days old, and between 10 and 12 days old (« day 2 » and « day 5 » on the graph). Morphological measurements were taken on the day oral administration to nestlings, then 2 and 4 days after the treatment.
Parental visits were monitored at their nest and at the feeders placed in the area during the whole experiment. nests), the duration of the visits in seconds and the duration between visits in minutes (only for nest visits). The feeding visits to the nest on the day before the first dose were used as a basal level and data from days after treatment were normalized by dividing by the mean of the values of the parameter obtained on baseline day for the same individual. This enabled us to assess the progression of parental visits over time. Feeding data for a total of 6 nests were not available (due to a malfunction of the decoder, or because the parents were not tagged), thus data are presented for a total of 26 nests (n = 12 nests for the CORT group, n = 14 nests for the OIL group): 8 nests with both parents equipped with PIT-tags (n = 6 in CORT group, n = 2 in OIL group) and 18 nests with only 1 parent equipped with a PIT-tag (n = 6 with only female with n = 3 in CORT group, n = 3 in OIL group; n = 12 with only male with n = 3 in CORT group and n = 9 in OIL group).
Nestling growth measurements We measured nestling tarsus length with a digital calliper (±0. 1 mm) and mass with an electronic balance (±0. 01 g) at 3 points during the experiment: the day that the first dose was given, then 2 and 4 days after the first dose. Nestling body condition was assessed using the residuals of the linear regression between the tarsus length and body mass (following Schulte-Hostedde et al. 2005), using the final set of morphological measures taken (4 days after the first dose, when the nestlings were about between 11 and 13 days old), for 30 nests (data were missing due to technical problems for 2 nests at this time point). The direct gains in mass and in tarsus length were also assessed for each nestling of 25 nests (n = 36 nestlings in 12 nests for the CORT group, n = 40 nestlings in 13 nests for the OIL group, complete data were missing for 7 nests because of technical issues or inclement weather on the day of measurement) by subtracting for each parameter the value just before the first dose was administered, from the value after the second oral administration.