dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. The AIRS L2 cloud data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) which have been used for constructing the UT cloud systems have been produced at LMD, using the CIRS retrieval method  (Feofilov and Stubenrauch, 2017). These data will be made available at the French Data Centre AERIS by the end of 2017. An earlier version of AIRS-LMD cloud data (2003)(2004)(2005)(2006)(2007)(2008)(2009), which is very similar, is available at http://www. icare. univ-lille1. fr/archive?dir= AIRS/L2-CLD-AIRS-LMD. v1-02/. AMSR-E/Aqua L2 rain rates (2003)(2004)(2005)(2006)(2007)(2008)(2009) version 2 are distributed by the NASA National Snow and Ice Data Center (NSIDC) at http://nsidc. org/data/ae_rain/ versions/2. The data have been accessed via ftp in early 2015  (Adler et al.
Off-glacier meteorological measurements 175 Air temperature (Ta), precipitation and snowfall were measured at the Pyramid Observatory adjacent to the tongue of Khumbu Glacier at 5035 m (Fig. 1 ) between 1997 and 2011. Precipitation was measured using a Geonor T-200BM sensor that captures all precipitation phases and corrected for snow undercatch following World Meteorological Office recommendations  (Sherpa et al. , 2017). We measured Ta, near-surface debris temperature (Ts), and temperatures within the debris layer (Td) down to the debris-ice interface (Ti) at four sites (KH1-4) on Khumbu Glacier through the 2014 monsoon season. Further Ta, Ts and Td data were collected using the same approach at KH1 through winter 2014/2015, and at KH2 and KH5, through 2015/16 (Table  1 ). All sites were located on gently 185 inclined slopes offset from the crests of topographic highs where debris was less than a metre thick.
210 Data collected from five sites on Khumbu Glacier between May 2014 and September 2016 were compared to those collected at two sites on Ngozumpa Glacier between November 2001 and October 2002  (Nicholson and Benn, 2013)  and November 2014 and April 2016 (S. Thompson, unpublished data)  and at four sites on Imja-Lhotse Shar Glacier between May and October 2014  (Rounce et al. , 2015). These three debris-covered glaciers are located adjacent to each other and have several factors in 215 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-239 Manuscript under review for journal The Cryosphere Discussion started: 30 November 2017 c Author(s) 2017. CC BY 4. 0 License. common; their ablation areas are covered with extensive debris layers, composed of a mixture of clasts of leucogranite, sillimanite-grade gneiss and minor schist  (Benn et al.
, 2012). Mean debris thickness measured at two sites within 7 km of the terminus of Ngozumpa Glacier in 2001 was 1. 25 ± 0. 75 m (n = 218)  (Nicholson and Benn, 2013)  and on part of Imja-Lhotse Shar Glacier in 2013 was 0. 42 ± 0. 29 m (n = 25)  (Rounce and McKinney, 2014). The on-glacier Ta, Ts, Td and Ti timeseries from these three 220 glaciers do not always cover the same periods, with several weeks missing at the start or end of the monsoon season due to the timing of field visits. Any data gaps were excluded from analyses. All temperature data were compared using values for the monsoon season when the debris was unlikely to be snow covered (03 June to 11 October), as well as for summer (1 May to 31 October) and winter (1 November to 30 April).
295 Here, k was computed following the method of  Conway and Rasmussen (2000) : Temperatures were resampled to hourly increments, and apparent thermal diffusivity was taken as the linear fit between the derivative of temperature plotted against the second derivative of temperature with depth. Apparent thermal diffusivity was determined for; (a) the bulk layer using data from all levels of the debris cover simultaneously, and (b) each level individually. Visual inspection of plots allowed assessment of the 300 presence of non-conductive processes (e. g. convective or latent heat exchange) within the debris profile. On this basis, the site with the least evidence of non-conductive processes was selected from each glacier and thermal conductivity was computed from the bulk apparent thermal diffusivity for the summer months of July and August.
Ta were 1. 8-3. 0°C cooler than those measured on-glacier (Fig. 4) due to the naturally ventilated radiation shields used to measure on-glacier Ta and radiative and sensible heating of the 570 surface air from the underlying warm debris, and the relatively lower position of the thermistors (1 m above the glacier surface compared to 2 m at the Pyramid Observatory); r 2 between the on-glacier and off-glacier Ta, excluding the intervals where data were not recorded at the Pyramid, were 0. 79 for KH1, 0. 53 for KH2, 0. 78 for KH3 and 0. 78 for KH4. Ta measurements were made 1 m above the debris surface on Khumbu Glacier, 1. 5 m above the debris surface of Ngozumpa Glacier, and 2 m above the 575 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-239 Manuscript under review for journal The Cryosphere Discussion started: 30 November 2017 c Author(s) 2017.
ablation beneath debris layers with thicknesses equivalent to or greater than those measured here (0. 3 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-239 Manuscript under review for journal The Cryosphere Discussion started: 30 November 2017 c Author(s) 2017. CC BY 4. 0 License. m). However, Td was above 0°C during March-May at KH1 and NG1 suggesting that the ablation season may precede the start of the monsoon season by several months. Our data reliably cover the monsoon season and are less continuous outside of this period. However, δTd/δhd have a similar form between the summer and the monsoon season, although the monsoon season profiles are uniformly 1-615.
However, comparison of our estimates of ablation with observations of glacier surface lowering indicates that subdebris ablation does not represent the majority of mass loss. Instead, over 65% of mass loss likely 800 occurs where debris is thin (<0. 5 m) or patchy and at the exposed faces of ice cliffs, beneath supraglacial ponds and englacially. meteorological dataset contained within this study was collected within the framework of the Ev-K2-CNR Project in collaboration with the Nepal Academy of Science and Technology as foreseen by the Memorandum of Understanding between Nepal and Italy, and thanks to contributions from the Italian National Research Council, the Italian Ministry of Education, University and Research and the Italian 830 Ministry of Foreign Affairs.
Acknowledgements Fieldwork at Khumbu Glacier was supported by  Royal Society   Research Grant   RG120393  to  A. Rowan  and  D. Quincey , and a grant from the  Mount Everest Foundation  to  A. Rowan. L. Nicholson was supported by  Austrian Science Fund  (Project numbers  V309  and  P28521 ). Phil Porter  is thanked for the loan of some of the equipment used. Yves Lejeune  is thanked for processing precipitation data, and.
610 ablation beneath debris layers with thicknesses equivalent to or greater than those measured here (0. 3 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-239 Manuscript under review for journal The Cryosphere Discussion started: 30 November 2017 c Author(s) 2017. CC BY 4. 0 License. m). However, Td was above 0°C during March-May at KH1 and NG1 suggesting that the ablation season may precede the start of the monsoon season by several months. Our data reliably cover the monsoon season and are less continuous outside of this period. However, δTd/δhd have a similar form between the summer and the monsoon season, although the monsoon season profiles are uniformly 1-615.
Numerical modelling of glacier evolution in response to climate change can apply the relationship observed here to improve predictions of regional multiannual glacier mass balance in future decades. However, comparison of our estimates of ablation with observations of glacier surface lowering indicates that subdebris ablation does not represent the majority of mass loss. Instead, over 65% of mass loss likely 800 occurs where debris is thin (<0. 5 m) or patchy and at the exposed faces of ice cliffs, beneath supraglacial ponds and englacially. meteorological dataset contained within this study was collected within the framework of the Ev-K2-CNR Project in collaboration with the Nepal Academy of Science and Technology as foreseen by the Memorandum of Understanding between Nepal and Italy, and thanks to contributions from the Italian National Research Council, the Italian Ministry of Education, University and Research and the Italian 830 Ministry of Foreign Affairs.
To address these questions, we combined a genetic study based on chloroplast (single nucleotide polymorphisms-SNP) and nuclear (simple sequence repeats-SSR) markers with local farmer interviews. Using these datasets, we analyzed (i) the genetic differences between C. amazonica and C. cujete in the Amazon Basin, and gene flow between them; and (ii) the relationship between genetic and morphological diversity, and how people use and perceive this diversity.
| Genetic analysis DNA was extracted from dried leaves (N = 234) using the CTAB 5% protocol  (Doyle & Doyle, 1990 ) with minor modifications. For nuclear SSR analysis, we genotyped all 234 samples, among which 184 were also analyzed for chloroplast SNPs (Table  S1 ). Collected leaves without enough DNA or with low quality were excluded. To develop the nuclear SSRs, a barcoded library of C. cujete genomic DNA was sequenced  (Moreira et al. , 2016)  using an Illumina MiSeqv. 3 (San Diego, California, USA). QDD software 3. 1. 2 (Meglécz et al. , 2009)  was used to identify nuclear SSR motifs and design primers from 113,865 merged reads. The parameters used to select the nSSR primers were as follows: one primer pair for each read to avoid repeated regions of the genome; avoidance of mononucleotide microsatellites; preference for perfect microsatellites with ≥8 repeats; and avoidance of primers that are very close (≤20 bp) to the target SSR.
The network was visualized using the samples with <4% of missing data, according to software requirements in POPART 1. 7  (Leigh & Bryant, 2015). The nuclear and chloroplast comparison defined paternal and maternal introgression, respectively, and determined the final botanical identification (170 C. cujete and 14 C. amazonica). To assess the impact of the uneven number of samples of the two species, we performed a complementary Structure analysis using the same sample size for both species (  14 C. amazonica, 14 C. cujete; Fig. S1 ), with the C. cujete samples chosen at random. Finally, we assessed the potential impact of null alleles on admixture inferences (Fig. S2 ), by coding any missing data as a homozygote recessive allele  (Falush, Stephens, & Pritchard, 2007). Nuclear genetic diversity of C.
ACKNOWLEDGEMENTS This research was supported by the  Conselho Nacional de Desen volvimento Científico e Tecnológico  ( CNPq-473422/2012-3 ), the  Fundação de Apoio à Pesquisa do Estado do Amazonas  ( FAPEAM 062. 03. 137/ 2012 ), the  Agence Nationale de la Recherche  ( ANR-13-BVS7-0017 ), and the  ARCAD  project funded by the  Agropolis Fondation. P. A. M. thanks the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior  for a scholarship ( CAPES-99999. 010075/2014-03 ). We thank the  Instituto de Desenvolvimento Agrário do Amazonas  for logistical support and farmer families for their support, kindness, and consent for this research.
Funding information Conselho Nacional de Desenvolvimento Científico e Tecnológico , Grant/Award Number:  CNPq-473422/2012-3 ;  Fundação de Apoio à Pesquisa do Estado do Amazonas , Grant/Award Number:  FAPEAM 062. 03. 137/2012 ; Agence Nationale de la Recherche , Grant/Award Number:  ANR-13-BVS7-0017 ;  Agropolis Fondation ;  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior , Grant/Award Number:  CAPES-99999. 010075/2014 -.
Physico-chemical and biological data characterising the pelagic system at LTER-MC are reported in ref. [45]. Data collected at LTER-MC are weekly updated on the website http://szn. macisteweb. com. In the laboratory, adult female oncaeids were individually sorted from the live sample under a stereo-microscope Leica M 165 C (Leica Microsystems Srl, Milan, Italy) and identified at the species level. The characters used for species identification are summarised in the dichotomic key reported by Di Capua and Boxshall  [11]  and in Table  1. The total body length of all specimens used in the present study was measured under a stero-microscope from the tip of prosome to the distal end of the caudal rami in dorsal position; further morphometric Table  1. Morphological diagnostic characters used to discriminate the 11 species of Oncaeidae present in the Gulf of Naples (*measured using traditional method).
Phylogenetic analyses Sequences obtained from our specimens and from GenBank (Table  2 ) were imported in Bioedit Sequence Alignment Editor 7. 0. 9. 0  [49]  software and first automatically aligned by Clus-talW then manually refined. Maximum Likelihood and Bayesian inference were carried out on three sequence alignments; ITS1-5. 8S-ITS2, ITS1, and COI. For all the alignments Modeltest  [50]  implemented in the software MEGA7. 0. 18  [51]  was run in order to find the best evolutionary model that fitted the dataset. For ITS phylogenies Tamura-Nei  [52]  model was applied. A discrete Gamma distribution was used to model evolutionary rate differences among sites. For ITS1-5. 8S-ITS2, 19 DNA sequences for a total of 640 positions, including gaps, were considered for the analyses. For ITS1, 23 sequences and 329 positions were analysed.
Sapphirina angusta Co041. 1. 1 GU171328 f SANG1 KT345967 c SANG2 KT345968 c Sapphirina bicuspidata SBC1 KT354291 c SBC2 KT354292 c Sapphirina darwinii sada1 HM045389 c Sapphirina metallina same HM045344 c M3090 KF985240 h SM1 KT429933 c SM3 KU144690 c SM4 KU144691 c SM5 KU200948 c Sapphirina opalina saop HM045409 c saop2 HM045410 c SAOP3 HM045411 c saop4 HM045412 c SO1 KU158879 c SO2 KU158880 c SO3 KU158881 c SO4 KU158882 c SO5 KU158883 c Sapphirina scarlata sasc HM045348 c SSR1 KT351342 c SSR2 KT351343 c (Continued) and invariable sites (GTR+G+I). The analysis involved 100 nucleotide sequences, of which nine from outgroup insect species. The outgroups were chosen by blasting the more divergent ingroup COI sequence in GenBank and retreiving the non-copepod best hits. Codon positions included were 1st+2nd+3rd+Noncoding.
All positions containing gaps and missing data were eliminated, for a total of 483 positions in the final dataset. For all the analyses, initial trees for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach. 10,000 bootstrap replications were performed for each phylogenetic analysis. The resulting trees were analysed and edited in MEGA7. Using the same alignments, Bayesian inferences were carried out. The analyses were forced to jump among the evolutionary models for nucleotide sequence alignment implemented in MrBayes 3. 2  [54]. The following settings were applied to Bayesian phylogenetic analyses for all Anopheles pristinus SP53_101 GU989357 SP55_2 GU989358.
ITS2 secondary structure reconstructions The last 400 bp of all the ITS sequences were scanned for secondary structure prediction for two reasons: 1) to identify the exact margins of this region, as in GenBank the entry descriptions for poecilostomatoid ribosomal sequences fail to define regions structurally; 2) because the ITS2 secondary structures can be used for phylogenetic purposes together with the sequence itself. RNA secondary structure predictions were performed using mfold software  [57]  and visualised in PseudoViewer 3 Web Application (http://pseudoviewer. inha. ac. kr/). ITS2 alignment and phylogeny. Based on secondary structure reconstructions, the ITS2 region of all the sequences available were manually aligned in BioEdit and then ML (Hasegawa-Kishino-Yano (HKY  [58] ) +G model, 10,000 bootstrap replications) and Bayesian inference were carried out.
Copepod metabarcoding failed to identify Oncaea spp. although microscopic sample counting had revealed their presence  [63]. In this paper, we produced sequences from COI, regularly used for species identification  [23, [27] [28] [29] [30] [31] [32]  and barcoding  [33] [34] [35] [36] [37] [38] [39] [40] , and ITS rDNA, widely applied for phylogenetic reconstructions in copepods  [19] [20] [21] [22] [23] [24] [25]. Although the number of available COI sequences for marine planktonic copepods is relatively high, obtaining COI fragments from oncaeids  [10, 64]  using the universal primers  [46]  commonly employed for copepods proved to be difficult. Additionally, the majority of the COI sequences we retreived from GenBank lack description, discussion and have not been incorporated in phylogenetic reconstructions (Table  2 ).
8S-ITS2. The positions of O. curta in the ITS1 and ITS2 phylogenies are not consistent: in ITS1 it clustered in a clade together with O. mediterranea and Oncaea sp. (MVZ-2013, GenBank KF153701), while in ITS2 phylogeny O. curta clustered with O. venusta. We produced the first O. curta ITS sequence and therefore we could not compare it with previous phylogenies. In all our ribosomal-based phylogenetic analyses, Triconia hawii clustered in basal position to the Oncaea s. s. species, corroborating the sisterhood of these two taxonomic groups. In the case of Triconia as well, our T. hawii ITS sequence is the first produced and future work will elucidate the genetic relationships among species belonging to Triconia and Oncaea. Elvers and colleagues  [41]  identified four morphotypes in O. venusta based on the body size in different geographic areas  [41].
mediterranea and O. venusta. Between O. curta and O. venusta, however, the secondary structures are much more similar compared to those predicted for M. leukarti and Thermocyclops or Macrocyclops(not shown), possibly revealing a more recent speciation or an incomplete lineage sorting  [71]. We used the ITS2 database  [72]    confirming what reported from the authors  [20]. However, more studies are necessary to disentangle this issue.
Fig). ITS1-based phylogeny was carried out to include as many Oncaea sequences as possible (for some species in GenBank only ITS1 sequences are deposited) for a wider comparison (Fig 2B). For this genomic region, BI (Fig 2B) and ML (S3 Fig) did not show topological congruence for the position of Triconia that in one case showed sisterhood with Oncaea spp. (PP 1. 00, Fig 2B), while in ML this taxon clustered in a basal position to the other cyclopoids (bootstrap support 45, S3 Fig). The Bayesian tree topology recalled that produced using ITS1-5. 8S-ITS2 region, with Triconia as a sister clade to Oncaea s. s. [13]. The different O. venusta sequences clustered together with O. venusta from the GoN and presented very low level of variability. Mean sequence identity among O. venusta from the GoN and the other representatives of this species was 98.
Acknowledgments Ferdinando Tramontano  and  Gianluca Zazo  are acknowledged for sampling at station LTER-MC,  Elvira Mauriello  for techinal assistance and  Lorenzo Longobardi  for producing Fig 5. We thank  Marco Uttieri  for discussions that helped improving the manuscript and  Suzanne Rose  for language check. IDC and AA were partially supported by the  Flagship  project  RITMARE (Ricerca ITaliana per il MARE ,  Italian Research  for the Sea). AA was funded by the European.
For example, it has been highlighted that large and meso scale oceanographic structures such as fronts, eddies and filaments are of significant importance to the foraging ecology of top predators  [19] [20] [21] [22] [23] [24] [25]. At fine scales, both biotic and abiotic parameters are likely to be key determinants in the dynamics of biomass distribution  [26, 27] , but these processes are still poorly understood. To study fine scale patterns in prey distribution, and their mechanistic relationships with predator behaviours, we used biologging data collected by southern elephant seals (SES) and focused our interest on the scale of a dive bottom phase. Indeed, this dive phase represents a fundamental organizational unit of the foraging strategy for many diving predators, including SES, where most of feeding occurs  [28] [29] [30] [31] [32] [33] [34].
However, a detailed understanding of how diving predators use their 3D spatial environment and interact with prey requires the actual reconstruction of their 3D path underwater. This is nowadays achievable using biologging data from large diving predators that are able to carry sophisticated loggers with minimal disturbance  [36, [41] [42] [43] [44] [45]. Such loggers can also provide information regarding the likely occurences of prey encounters  [46] [47] [48]. Three-dimensional path analysis has started to provide new insights into the behaviour of elephant seals  [42, 43]  and other diving predators  [36, [49] [50] [51] [52] [53] , but also into the fine scale patterns of their prey distribution  [45]. In this study, six datasets with acoustic recording, triaxial acceleration and magnetometry, sampled at high frequency, allowed us to reconstruct the three-dimensional underwater path of SES using well established deadreckoning methods  [54].
The fourth degrees provide enough freedom for the model to handle V-shaped and squared (U-shaped) dives. Eventually, the vertical speed threshold was chosen after a blind experiment minimizing the difference between the automatic and the visual delineation of the bottom phases. Drift dives are specific dives where SES are resting and/or digesting  [59]  and, as a result, not expected to react when encountering prey. Since our focus is on spatial patterns related to predator-prey interactions, we identified and removed these dives from our dataset prior to the statistical analysis.
Prey encounter events To identify a prey encounter event we implemented the method described in  [25, 58, 60]  (but see  [60]  for details) on the acceleration data collected by the tags. The dynamic acceleration resulting from rapid movements was extracted from the three axes with an order 3 high-pass digital Butterworth filter with a normalized cut-off frequency of 2. 4 Hz (performed with butter and filtflit functions from the signal package  [61] ). For each axis, a one-second fixed window was used to calculate the standard deviation every second. Signals were then processed using a moving standard deviation with a window size of five seconds. Finally, a two-mean clustering was performed for each signal to distinguish "high state" from "low state". These successive operations are performed using the prey_catch_attempts function from the rbl package.
A prey encounter event (hereafter PEE) is believed to be occurring when the three axes are simultaneously in "high state". A continuous succession of "high state" is considered as a single PEE. A comparison of PEE detection results of this method derived from both the head-mounted and back-mounted accelerometer data is provided in [62, Additional file 2]. We counted PEE to obtain an indication of the number of prey encounters in the bottom phases but the corresponding prey types are not known.
The static acceleration is the gravity based acceleration component. It can be obtained by applying the appropriate low-frequency filter to the acceleration signal. As in  [63] , we used an order 3 low-pass digital Butterworth filter with a normalized cutoff frequency of 0. 20 Hz applied to the three axes. The direction of the gravity vector according to the accelerometer provides a reference to calculate the pitch and roll angles of the SES (we used the pitch and roll functions from the animalTrack package  [64] ). The lowpass filter was applied to the magnetic data as well. The resulting signal, combined with pitch and roll information allows to calculate the heading angle (performed using tilt_compensate function from the animalTrack package). Aside from body posture angles, 3D path reconstruction requires knowledge of the SES swimming speed.