dataesr/scientific-paragraphs-categorization huggingface dataset:
THE DATA AND THE BASIC MEASUREMENTS 2. 1. The COSMOS We base our study on the COSMOS survey dataset  (Scoville et al. 2007 ), so to capitalize on its high quality compilation of multiwavelength imaging, including Hubble Space Telescope (HST) ACS data  (Koekemoer et al. 2007) , over a wide-area field. For the present analysis we employ the ACS I-band source catalogue of  Leauthaud et al. (2007)  containing 156,748 sources (102,007 of these tested to be reliable galaxies) down to a flux limit of I 814W = 24 mag. The reliability of this catalogue for galaxy photometry and morphological analysis was subsequently improved via extensive visual inspection and cleaning to remove artifacts, cosmic rays, and stars, and to identify deblending errors, leaving a total of 102,007 sources flagged as reliable galaxy detections.
For the purpose of estimating photometric redshifts and stellar masses we match the ACS I-band source catalog against the CFHT i * -band  (McCracken et al. 2010)  and Subaru i + -selected COSMOS Intermediate and Broad Band Photometry Catalog  (Ilbert et al. 2009 (Ilbert et al. , 2010;; hereafter, the 'I09 catalog'). This aperturematched, photometric database, constructed with an updated implementation of the source detection procedure described in  Capak et al. 2007 , offers a large compilation of broad-and narrow-band flux measurements in 3 arcsec apertures across 31 bands from UV-optical (u) through to infrared (8. 0 µm). As described in  Capak et al. (2007)  the use of PSF-matched, aperture magnitudes allows for -The rest-frame NUV-J vs. R-J color-color distributions of quenched (in red) and star-forming (in blue) galaxies at 0.
, exponentiallydeclining SFRs spanning a range of metallicities from 0. 05 to 2 Z , decay timescales from τ ∼ 0. 05 to 9 Gyr, and ages from 0. 01 to 12 Gyr (with a Bayesian prior to bound the latter at less than the age of the Universe at any given redshift). The construction of this library was achieved via the  Bruzual & Charlot (2003)  stellar population synthesis code, adopting a Chabrier IMF  (Chabrier 2003). The impact of dust extinction is handled during template matching by allowing dust reddening  (Calzetti et al. 2000)  with the E(B -V ) value treated as a free parameter of the fit. Synthetic template matches were identified, and a stellar mass successfully derived, for all but 1,088 of the 85,277 COSMOS galaxies with photometric redshifts (a 1. 3% failure rate). Owing to the inherent degeneracies in the choice of stellar population template and dust extinction model, which dominate the error budget in the present analysis (given the thorough characterization of the observational SEDs across our multiwavelength database) we estimate an uncertainty of σ log M ≈ 0.
2009; Oesch et al. 2010) , and the stability (i. e. , predictability) of its systematic biases, which can thus be robustly corrected, as we demonstrate in Section 3. 2 below. Conversely, profile fit-based sizes are themselves prone to increasingly unstable behavior (i. e. , large random errors) in the low surface brightness regime  (Häussler et al. 2007 ) and at faint magnitudes; the results of profile-fits are unreliable for typical galaxies fainter than I 814W ∼ 23 mag in the COSMOS imaging, whereas we want to push our limit one magnitude fainter. To measure the (aperture) sizes of the galaxies in our sample, we used our custom-built software package package ZEST+ (see Section 2. 3). This also measures galaxy half light radii within elliptical apertures, providing an alternative to the circular aperture measurements of SExtractor  (Bertin & Arnouts 1996) , which are intrinsically limited in their ability to properly characterize elongated systems such as inclined disks.
As a showcase we present in this Appendix the correction functions, in a similar way as in Figures  2  and 3 , but for sizes obtained using the public code Galfit  (Peng et al. 2002). The Galfit analysis in the Appendix uses the same set of simulated galaxies which were used to correct the ZEST+ size measurements that we use in our analysis. We note that the trends and strengths of the correction vectors for the Galfit sizes differ from those reported in this section for the ZEST+ sizes: Galfit does better than ZEST+ in the regime of small sizes (i. e. , smaller than the PSF), but is less accurate in recovering sizes of large, low surface brightness galaxies. Our main message here is twofold: first, in order to study galaxy populations spanning a large range of sizes and surface brightness, both aperture-based and the model-fit size measurements require corrections.
8 < z < 1) , (2) highlight in a straightforward manner the rate of growth in the number densities of Q-ETGs in each bin of size and Σ MASS. At the highest stellar masses, i. e. , in the M Galaxy > 10 11 M bin of our analysis, we detect a decrease from z = 1 to z = 0. 2 of about 30%-40% of the smallest and densest Q-ETGs, i. e. , of high-mass Q-ETGs with halflight radii smaller than ∼ 2 kpc. Note that in our data this is only seen at the 1 -σ-level. Nevertheless this result, obtained within a self-consistent dataset, formally agrees with the several works which report a decreasing number density of compact galaxies with increasing age of the Universe (see references above). At these high masses, the disappearance towards lower redshifts of compact galaxies seems to be increasingly larger the smaller the galaxy size, as shown in the inset in the relevant size-function plot of Figure  4 , which zooms on the < 2.
5 kpc scales. The analysis of our low-mass bin at 10 10. 5 < M Galaxy < 10 11 M , i. e. , just below M * , also shows surprises. The number density of the r 1/2 ≤ 2 kpc Q-ETGs remains remarkably stable throughout the z = 1 → 0. 2 redshift range. At these masses, there is essentially no change in the number density of the most compact Q-ETGs since z ∼ 1 down to z ∼ 0. 2, and thus no evidence for any disappearance of compact galaxies due to an increase in their individual sizes across this time span. This results is not affected by our binning in size: it holds down to the smallest ∼ 1kpc sizes, as shown in the inset in the relevant size-function panel of Figure  4 , which plots again a zoom-in of the small-scale size-function (this time in the < 2kpc regime). Note that we ascertain this constancy self-consistently within the COSMOS sample, without recurring to comparisons between different studies or datasets.
e. , their 'mass quenching'; cf. Figure  13  in  Peng et al. 2010). To explore whether mass-quenching of star-forming galaxies is quantitatively consistent, in terms of numbers and sizes, with the evolution of the size-function of Q-ETGs that we have observed, (i) we compute, in our two mass bins, the size (and Σ MASS ) functions for the star-forming population in the COSMOS dataset; and (ii) we adopt the predictions of  Peng et al. (2010)  for sudden environment-independent mass-quenching of star-forming galaxies, which is the relevant quenching mode at the mass scales of our sample. We choose to limit this analysis to the star-forming disk galaxies in our COSMOS sample. We note that, at the stellar masses that we are investigating, disks represent > 80% of the star-forming population; furthermore, we also remark that, above 10 10.
5 arcsec, 0 < e < 0. 95, and Sérsic index 0. 2 < n < 8. As before each model galaxy was constructed at a pixel scale of 0. 03 arcsec and convolved with a representative ACS I 814W PSF  (Rhodes et al. 2007 ), and added with Poisson noise to an empty region of sky from the real COSMOS HST/ACS imaging. SExtractor was then run to generate object masks and to estimate starting values for use in Galfit. In particular, we adopt the Kron magnitude, the mean isophotal ellipticity and position angle, and the Kron half light radius as initial guesses for their model counterparts-and all fits were begun with an initial guess of n = 2. 5 for the Sérsic index. After running Galfit the output parameters of all convergent fits (i. e. , those which did not end in a catastrophic error causing the program to crash) were saved for comparison against our input model (arcsec) q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0.
This well-studied mollusc-hunting cone snail produces potent analgesic compounds, including -conotoxin MrIA  (10, 21)  and O-conotoxin MrVIB along with 40 other identified conotoxins. From the different second-generation sequencing platforms, the 454 pyrosequencing technology was selected as it generates relatively long reads (on average Ͼ 300 bp) that can cover the full length of conopeptide precursors (70 -100 amino acid). This approach allows direct identification of conopeptide precursors, avoiding the errors inherent to the assembly of reads into contigs typically required for other second-generation technologies that generate shorter read lengths  (22). To complement this approach, we performed a detailed proteomic investigation using three high sensitivity mass spectrometers and developed dedicated bioinformatic tools for data integration.
Sequencing was carried out on a Roche GS FLX Titanium sequencer. In addition to our sample, three other samples from a related project were run together on a full plate, using a unique barcode for each sample. After sorting, cleaning and trimming of the reads, sequence assembly (contigs) was carried out using Newbler 2. 3 (Life Science, Frederick, CO). Conopeptide Sequence Analysis-Raw reads and contigs were up-loaded in a proprietary web-based searchable database. The identification of conopeptide sequences was carried out from the raw data using tBlastn and either signal sequences or mature sequences retrieved from the ConoServer  (23). As mentioned previously, such long sequence reads are likely to contain the full nucleic sequences of conopeptide precursors. The identified conopeptide sequences were then aligned using Multalin program  (24).
At this stage, redundant sequences, incomplete precursor sequences and aberrant sequences (i. e. extended N-terminal due to frameshifts or degenerate positions) were removed. Alignments were then edited with Jalview and the sequence clustering tree was constructed from "average distance using % identity" algorithm implemented in the Jalview program  (25). Gene superfamilies, signal peptides, and cleavage sites were predicted using the ConoPrec tool implemented in ConoServer  (26). The cutoff value for assigning a signal peptide to a gene superfamily was set at Ͼ 75% sequence identity, as extrapolated from a recent analysis of all precursors deposited in ConoServer  (13). Venom Sample Preparation-Six adult (Ն 6 cm) specimens of C. marmoreus were collected from the Great Barrier Reef (Queensland, Australia) and held in aquaria for several months.
The problem of identifying a conopeptide from a gene sequence is increased by the presence of differential post-translational processing. ConoMass was implemented in Cono-Server to help in the identification of conotoxins by mass spectrometry  (26). In this two-step process, monoisotopic and average masses resulting from variable PTM processing are computed for each peptide and then matched to masses observed experimentally without relative mass accuracy correction. These bioinformatic tools are implemented in PHP, Python, and Mysql and are available online at the ConoServer website (http://www. conoserver. org) (26). Proteomic Data Analysis-LC-ESI-MS reconstruction was carried out using Analyst LCMS reconstruct BioTools (Framingham, MA, USA). The mass range was set between 1000 -8000 Da. Molecules Ͼ 8000 Da were observed but excluded from further analysis.
The mass tolerance was set to 0. 2 Da and S/N threshold was set to 10. The MS data matching was carried out using the ConoMass tools (see below) followed by critical manual inspection. The precision level was set to 0. 1 Da for automatic matching search. Manual search accuracy was set to 100 ppm. Deconvoluted mass lists from different instruments were cross-calibrated, compared, cleaned and binned using two bioinformatic tools, namely "Compare mass lists" and "Remove duplicated masses," which are available on the ConoServer website. The precision level used for binning and comparing masses was set to 0. 2 Da. The ProteinPilot™ 4. 0 software (AB SCIEX, Framingham, MA, USA) was used for sequence identification by searching the LC-ESI-MS/MS mass lists obtained at a mass tolerance of 0. 05 Da for precursor ions using the reduced and reduced/alkylated samples.
These masses, and related fragmentation masses (0. 1 Da tolerance), were matched against a protein database comprising all ConoServer conopeptides, NCBI cone snail related proteins and all read sequences obtained from this transcriptomic project (2,157,997 entries). Modifications used in the search include the following: amidation, deamidation, hydroxylation of proline and valine  (27) , oxidation of methionine, carboxylation of glutamic acid, cyclization of N-terminal glutamine (pyroglutamate), bromination of tryptophan  (28) , and sulfation of tyrosine  (29). The O-glycosylation PTMs were not included in our search as this modification has not been reported for C. marmoreus conopeptides (glycosylation occurs infrequently and mostly in fish-hunting species) and the typical fragment loss associated with glycosylation was not seen by MS in this venom.
RESULTS Transcriptomic Data Analysis-A single run ( 1 ⁄4-plate equivalent) on the Roche GS FLX Titanium sequencer generated 179,843 reads averaging 317 bp (min 18 bp) in length after removal of low-quality sequences. 114,159 reads were assembled into 839 contigs, and the rest remained as singletons. Although this study focused mainly on conopeptides, many protein and enzyme sequences were also identified among the contigs and will be described elsewhere. As outlined in the experimental procedures section, we searched for conopeptide sequences directly from the sequencing reads, as the average read length of Ͼ 300 bp allowed full conopeptide precursors to be found. Conopeptides were also searched in the contigs, and no additional conopeptide sequences were found. Overall, 105 unique conopeptide sequences were retrieved from the venom duct transcriptome of C.
marmoreus has a milky appearance (Fig. 4B ), in contrast to the translucent venom obtained from "hook-and-line" piscivorous species. The milky appeareance is mainly due to the presence of secretory granules (Fig. 4C ) that appear similar to those found in the venom duct of another molluscivorous cone snail, C. victoriae  (34). The volume of the injected venom seems to vary according to the size of the animal, and generally 10 -20 l were collected per milking, with six different individuals pooled for our proteomic analysis. Mass Spectrometry-We used ESI or MALDI sources in LC-ESI-MS (QSTAR Pulsar), MALDI-MS (4700 TOF-TOF Proteomics Analyzer) and LC-ESI-MS/MS (TripleTOF 5600 System) configurations to uncover the complexity of C. marmoreus injected venom. Using a precision of Ϯ 0. 2 Da for binning the mass list, single 115 min LC-ESI-MS run on the QSTAR instrument revealed 3172 unique masses (from the 6867 raw data mass list) in the milked venom of C.
* This work was supported an  NHMRC   Program Grant  (RJL and PFA), an  Early Career Research grant  from  The University of Queensland  (to S. D. and AHJ), an  NHMRC   Principal research Fellowship (RJL)  and a Grant from the  Australian Research Council (QK). S. D. was the recipient of a UQ postdoctoral fellowship. The AB SCIEX 5600 mass spectrometer was supported by an  ARC   LIEF grant. □ S This article contains supplemental Fig. S1 and S2 and Tables  S1 andS2.
Stream sites The study was conducted in 15 stream sites belonging to two glacier-fed catchments in the Ecological Reserve of Antisana. These sites were selected because detailed long-term climatic and hydrological data were available in this area. For example, work on the geology and water chemistry of these catchments (see  Favier et al. , 2008; Villacis, 2008)  has detected glacial meltwater infiltrations and resurgences, which were of great interest to test the relevance of our method (see Introduction). To assess the wavelet signals of a broad range of glacial contributions, 10 of the 15 stream sites (no. 1-10; Fig. 1 ) were localized along two glacier-fed streams and five on their respective tributaries (no. 11-15; Fig. 1 ). Among the studied tributaries, four stream sites (no. 11-14) were considered non-glacial as they had no glacier cover in their catchment (see Fig.
Local and scale-averaged wavelet power spectrum To determine the glacial influence based on water-level time series, we first transformed water pressure values, obtained from the 15 stream sites, into water-level values by subtracting the atmospheric variations from the water pressure data. Time series were centered on their means and normalized by their standard deviations prior to wavelet transform calculation to allow across-site comparison of our results. We then developed a code inspired by C. Torrence and G. Compo (available at http://paos. colorado. edu/research/wavelets) that we ran in MATLAB, version R2009a (The MathWorks Inc. , Natick, MA, USA). This code allowed producing three types of figures: (1) the averaged normalized water-level time series, which presents the water-level variations throughout the year; (2) the local wavelet power spectrum (normalized by their standard deviations), which gives the magnitude of the variance in the series at a given wavelet scale and location time; and (3) the scale-averaged wavelet power spectrum, which presents the fluctuation in power over 24 h scale over a whole year.
Glacier metrics vs. glacier cover in the catchment As the percentage of glacier cover in the catchment is commonly used to estimate the potential influence of a glacier on a stream (e. g. ,  Hari et al. , 2005; Thayyen and Gergan, 2010; Jacobsen et al. , 2012) , we were interested in assessing how our three wavelet metrics at each stream site would behave as a function of glacier cover in the catchment. To measure the percentage of glacier cover in the catchment, we first created the channel network, and the catch-ment area was calculated with the SAGA GIS software (System for Automated Geoscientific Analyses, version 2. 0. 8). Briefly, SAGA derives a channel network based on gridded digital elevation model (DEM) with the specification of the target cells (gauge station), for which the upslope contributing area is identified.
Acknowledgements. We warmly thank the constructive suggestions provided by  Bettina Schaefli  and three anonymous reviewers; our manuscript is undoubtedly a stronger contribution as a result of their efforts. We thank  Patricio Andino  and  Rodrigo Espinosa  for technical support in the field and  Maëlle Collet  for the glacier outlines of Antisana. Temperature data close to the glacier were provided by the  Service d'Observation GLACIOCLIM  (http://www-lgge. ujf-grenoble. fr/ServiceObs/index. htm). The funding by  Ecofondo  grant no. 034-ECO8-inv1  to O. Dangles, by a  WWF-Novozymes  grant  2008  to  D. Jacobsen , and by a  SENESCYT-EPN  grant no. PIC-08-506  to  M. Villacis  are greatly appreciated. Edited by:  M. Weiler.
Statistical model checking tool-chain Fig. 3  shows the statistical model checking tool-chain. The SoS is modeled in UPDM/SysML and annotated with GCSL contracts. The specification follows two paths: the GCSL contracts are translated to the B-LTL language accepted by PLASMA and enriched with OCL patterns. The structure of the modeled follows a different technological path to be imported into the DESYRE simulator. The model structure is annotated with the referenced component's implementations.
PLASMA: a statistical model checking tool PLASMA  [15] , developed at INRIA [14], adopts a modular architecture to facilitate the extension of its features. Models can currently be specified using the PRISM  [25]  reactive modules syntax, timed The input specification is translated into a common intermediate language based on elements (referred to as simple commands because they have no explicit synchronization and no choice of actions) having the structure (guard, rate, actions), where guard, rate and actions are functions over the current state (constants, variables, clocks) of the system. The intermediate language thus expresses the semantics of a system which advances by discrete events: the guard enables the command, the rate resolves non-determinism between enabled commands (and controls the delay in continuous time systems) and the actions update the state of the system.
Once the model is represented in the intermediate language it is compiled into an executable form (the model program). PLASMA uses its own in-built compiler to create bytecode for execution on its own stack-based virtual machine (VM) that comprises a logic VM (LogicVM in Fig. 4 ) and a simulation VM (SimVM in Fig. 4 ). PLASMAs bytecode instructions constitute a domain-specific, low level, platform-independent language designed for efficient statistical model checking. This language contains standard low level instructions, such as push, pop, add, sub, mul, div, etc. , as well as non-standard instructions to construct efficient model checking algorithms. The VM is implemented in a high level procedural programming language (currently Java, but the code uses no features that cannot easily be adapted to other languages) and is efficient because it is optimized for its domain of application: high level instructions are efficient sub-parts of model-checking algorithms and all instructions are optimized with respect to the hardware level.
DESYRE simulation environment DESYRE  [10, 5, 11]  is a System-Level Simulation Framework developed by ALES S. r. l. [2]. It is a SystemC  [13]  based simulation environment used to support the design and the verification of Large-Scale Real-Time Distributed Embedded Systems. The tool provides capabilities to perform Design Space Exploration and System Verification by simulation. It has been successfully employed in several EU & DARPA Projects such as: SPEEDS  [28] , METAII  [11, 7] , SPRINT  [29] , MBAT  [22]  and in several industrial projects  [10, 5]. DESYRE is a component based simulator where system models are described as the composition of a set of modules. Hierarchical modules are supported. Modules are arranged in libraries. A library represents a group of models that are logically related (e.
g. , the IEEE802. 3 library contains the models of the physical and MAC layer defined by the IEEE standard). A module in DESYRE is fully identified by its interface, which specifies the set of parameters and ports (interaction points) of the module, and by a set of implementations, that represent the behavior of the module at different levels of abstraction. DESYRE modules have an associated descriptor that is necessary to enable the instantiation and the configuration of the module as a component into a system model definition. A DESYRE system model can be specified either with SPIRIT IPXACT  [16]  XML standard files or with SLang (System Language for DESYRE) XMI files. This way the user has the capability to specify its own system model and simulate several configurations, without the need to recompile the entire model.