dataesr/scientific-paragraphs-categorization huggingface dataset:
The SO(4 5 -3 4 ) transition at 206. 176 GHz of our SOLIS data observed at high angular resolution therefore confirms the CALYPSO results already shown by  Santangelo et al. (2015). Unlike SO, OCS shows its brightest emission towards the hot corino of IRAS4A2. Interestingly, another OCS emission peaking at +3 km s -1 is also detected at (+0. 2 , -0. 4 ) with respect to 4A1 for the two targeted transitions and its position is depicted in the SiO map in Fig. 2. The location of this second OCS peak corresponds to the beginning of the south outflow driven by 4A1 detected in SO and SiO. The SO 2 transition shows extended emission but mostly in the blue range, around SiO peak 5 for the 11 2,10 -11 1,11 transition at 225. 301 GHz and around SiO peak 4 for the 13 2,12 -13 1,13 one at 205. 300 GHz.
Comparison with the IRAM-30 m spectra In order to estimate the flux resolved out by the NOEMA interferometer, we compared the spectra obtained with our NOEMA datasets with the single-dish spectra obtained with the IRAM-30 m single-dish telescope as part of the ASAI Large Program  (Lefloch et al. 2018). Briefly, the aim of the ASAI program was to carry out an unbiased spectral survey at high spectral resolution of a sample of young nearby low-mass and intermediate-mass protostars in the 3, 2, and 1. 3 mm spectral windows. To obtain the NOEMA spectra, we estimated the flux of each spectral channel coming from a circular mask of a size equal to the beam of the IRAM-30 m telescope that ranges from ∼30 at 80 GHz to ∼10 at 220 GHz. The single-dish spectra of transitions from sulfuretted molecules, SiO, and CH 3 OH detected in the ASAI data are compared with the NOEMA spectra in Fig.
C. 1. We also show one representative transition is shown in Fig. 5 , the SO 2 transition at 165. 225 GHz. We present the ASAI spectra both for the original 200 kHz spectral resolution obtained with the EMIR receiver connected to the FTS backend and for a smoother spectral resolution of 1. 95 MHz equivalent to the WIDEX correlator of NOEMA. The recovered flux of transitions detected in the ASAI data at 5σ are estimated from the ratio of the flux integrated between -20 and +50 km s -1 and are given at the top of each panel. High-resolution spectra from ASAI can usually be decomposed into several components (see for instance Ospina-Zamudio et al. 2018, for a more detailed description of a similar dataset towards an intermediate-mass protostar): (1) a narrow line component associated with the large scale cold and quiescent envelope, (2) a broad line component associated with the outflowing gas as seen with broad wings, (3) a moderately-broad line component associated with the hot corino.
Transitions with a low upper level energy, such as the OCS(8 -7), SO(3 2 -2 1 ), SO(6 5 -5 4 ), SO(4 5 -3 4 ), SO 2 (8 1,7 -8 0,8 ), or SO 2 (7 1,7 -6 0,6 ) show a bright narrow line component observed with the IRAM-30 m but not detected with NOEMA. This suggests that the cold extended emission is completely resolved out by the NOEMA interferometer with the used configurations of the array. The velocity profiles of the moderately-broad and broad components for the SO, SO 2 , CH 3 OH transitions show a very good agreement between the IRAM-30 m and NOEMA datasets, confirming that the high-velocity gas is associated with compact emission. The fluxes recovered by NOEMA typically range from ∼25-50% for transitions showing a bright narrow line component to ∼100% (±20% of calibration and statistical errors) for species mostly associated with compact gas.
Dissecting the molecular outflows with clustering methods The independent analysis of the moment 0 maps, namely the integrated intensity maps of the molecular emission, and then the kinematics information included in the moment 1 and 2 maps already allowed us to distinguish by eye several components along the two molecular outflows driven by the IRAS4A protobinary system. We now aim to combine the information included in the three types of moment maps in order to quantitatively distinguish the spatial components included in the outflows through the use of a clustering method. Among the variety of clustering methods found in the literature, we chose the relatively simple and commonly used K-means algorithm  (Lloyd 1982)  available within the scikit-learn Python package. Our data are a set of pixels each characterised by two sky coordinates and 3 × N species (integrated intensity, velocity peak, and linewidth for each species) features.
For eight clusters, the OCS peak (in purple) is identified. For nine clusters, the extremely-high-velocity component ("North(EHV)" in grey) is clearly identified, as already shown by the moments 1 and 2 in Fig. 4. We also estimated the effect of noise on the clustering results by adding a random Gaussian noise to the moment 0 maps with standard deviations equal to those listed in Table  1. Adding a random noise to our dataset gives very similar results. All clusters but the North(EHV) are identified, whilst cluster 0 depicting the noise in black is decomposed into two randomly spatially distributed clusters. This simple clustering method therefore allows us to quantitatively dissect the outflows into several components that were already qualitatively suspected by eye. More sophisticated clustering methods have been proven to be efficient to dissect larger and more complex regions where a visual inspection is impossible (see  Bron et al.
2012 (Taquet et al. , 2013 (Taquet et al. , 2014 (Taquet et al. , 2016;; Dulieu et al. 2019)  in order to estimate the pre-shock abundances. In the second stage, the physical-chemical evolution impacted by stationary one-dimensional Continuous (C) magnetised shocks is followed using the 2015 version of the Paris-Durham shock model  (Flower & Pineau des Forêts 2015). The dynamical age of the IRAS4A outflows is likely of a few 10 4 yr, which should be much longer than the equilibrium time of 1000-2000 yr for the shock waves considered in this work, justifying the choice of stationary C-type shocks. The gas phase chemical network used in MOMICE for the first stage is based on the 2013 version of the KIDA database updated for reactions involving sulfuretted species following  Smith et al. (2004) ,  Loison et al.
Acknowledgements. V. T. is grateful to  Sylvie Cabrit  and  Guillaume Pineau des Forêts  for stimulating discussions on the chemistry in shocks. The authors acknowledge the  CALYPSO consortium  for the use of the CALYPSO dataset. This work is based on observations carried out with the  IRAM PdBI/NOEMA Interferometer  under project numbers  V05B  and  V010  (PI:  M. V. Persson ), U003 (PI: V. Taquet), and  L15AA  (PI:  C. Ceccarelli  and  P. Caselli ). IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). V. T. acknowledges the financial support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement n. 664931. This work was supported by (i) the  PRIN-INAF 2016  " The Cradle of Life -GENESIS-SKA (General Conditions in Early Planetary Systems for the rise of life with SKA )", (ii) the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , for the Project "The  Dawn of Organic Chemistry" (DOC) , grant agreement No  741002 , and (iii) the European MARIE  SKŁODOWSKA-CURIE ACTIONS  under the European Union's  Horizon 2020 research and innovation programme , for the Project " Astro-Chemistry Origins" (ACO ), Grant No  811312.
Background and Aims Using internal trophic pressure as a regulating variable to model the complex interaction loops between organogenesis, production of assimilates and partitioning in functional-structural models of plant growth has attracted increasing interest in recent years. However, this approach is hampered by the fact that internal trophic pressure is a non-measurable quantity that can be assessed only through model parametric estimation, for which the methodology is not straightforward, especially when the model is stochastic. • Methods A stochastic GreenLab model of plant growth (called 'GL4') is developed with a feedback effect of internal trophic competition, represented by the ratio of biomass supply to demand (Q/D), on organogenesis. A methodology for its parameter estimation is presented and applied to a dataset of 15 two-year-old Coffea canephora trees.
e. no plasticity of the plant structure in response to internal variables can be simulated: e. g. GreenLab 'GL1'  (Yan et al. , 2004; Guo et al. , 2006; Ma et al. , 2018) , L-Peach  (Allen et al. , 2005)  or the model introduced by  Coussement et al. (2018). This category includes models with deterministic development driven by environmental variables (e. g. Louarn and Faverjon, 2018). Such a development module can be useful as a convenient framework for specific studies at fine scales or of detailed processes (e. g. transport phenomena within the plant structure). Unless taking into account environmental control, the relevance of this deterministic development hypothesis for real applications is limited to a few single-stemmed species (maize:  Yan et al. , 2004; sunflower: Wu et al. , 2012)  or to 'average' plant representations, that is to say simulations that represent only the PART OF A SPECIAL ISSUE ON FUNCTIONAL-STRUCTURAL PLANT GROWTH MODELLING Downloaded from https://academic.
The rationale is that absolute sink strength is generally defined using potential growth rate (e. g. Evers et al. , 2010) , which can be difficult to measure experimentally. The choice of leaf as a reference is purely arbitrary (but applicable to nearly every plant species) and could be easily changed. It is not our purpose to discuss point (1) further here. Our aims are to propose a methodology to tackle points (2) and (3) in the framework of a new stochastic and retroactive version of the GreenLab model, and to investigate the possible relationship between internal trophic state and plant development by applying this new methodology to a dataset on coffee trees. The paper is organized as follows: we first present the model version 'GL4', which accounts for plant development as a stochastic process driven by the ratio of biomass supply to demand.
The procedure proposed for its parameter estimation is then presented. This procedure is the latest achievement of a continuous long-term study that has tackled the problems of model parameterization for the different versions of GreenLab, from the simplest to the most complex. It uses organ-scale samples and exploits the concept of 'organic series'  (Buis and Barthou, 1984) , in line with the work presented by  Kang et al. (2018)  but with adaptations to take into account the retroactions between the plant trophic status and its organogenesis. In the Results, after a case study illustrating some emergent properties of the 'GL4' model, the core of the paper is the application of this new methodology to a dataset collected on 2-year-old coffee trees (Coffea canephora) grown in the Ivory Coast: the estimated parameters then allow us to reconstruct the source-sink dynamics (Q/D) of the plants and to investigate, for the first time for this species, the existence and shape of the potential dependencies between organogenesis probabilities and this variable.
In 'GL4', the probability of branch appearance and the development probability for the branches, denoted a k and b 2,k , can vary with their rank k because they depend on Q/D. We present below the associated equations, adapted to take into account this variation. It can be shown that for N k the number of phytomers on a branch at rank k from the top: E (N k ) = w • k b 1 • b 2,k (7) V (N k ) = w • k b 1 • b 2,k (1 -b 2,k ) + (w • b 2,k ) 2 • k • (1 -b 1 ) b 2 1 (8) The mean and variance in eqns (  7 ) and (  8 ) can be estimated using the experimental dataset consisting of phytomer counts on a collection of stems and their sets of first-order living branches, indexed by the position of the branch from the top (rank k). The system can thus be solved to obtain estimates of b 2,k and w. The probabilities of branch appearance a 2,k (hereafter simply noted a k because there is no higher branching order) are estimated as the ratio of the number of branches observed at rank k (their average age being k/b 1 ) to the total number of positions, 2N T.
2. This potential structure allows us to compute the theoretical demand of the potential average plant: D θ (t) = id π (id) • P phyt (h (id) , age (id)) (9) where the summation is performed over all phytomers of the potential structure arbitrarily indexed by an integer id. The superscript index θ is used here for to indicate variables corresponding to the theoretical average plant that can be computed using the random processes defining the architectural rules. The term π(id) represents the probability of existence of phytomer id of physiological age h(id) and P phyt (h(id), age(id)) is its associated demand. An example of these probability values π(id) obtained for our Roux model illustration is given in Fig. 2. By applying the usual GreenLab equations, the theoretical biomass q p,θ o (i, t)of each organ of age i of the potential structure in the plant of age t can be deduced.
Superscript p indicates that the 'potential' structure is considered, that is to say the plant with the maximum possible number of phytomers (obtained if they all had a probability of existence equal to 1). The final step is computation of the topological organic series: the average organ weight at rank k in the potential structure is expressed as: q θ o (k, t) = t i=k π age (k, i) • q p,θ o (i, t) t i=k π age (k, i) ( 10 ) where t is plant age and π age (k, i) is the probability that the chronological age of a phytomer of rank k from the tip is i, computed from a truncated negative binomial distribution  (k, b). This simulated output can be compared to the vector of averaged measured values stored in the stochastic topological organic series, Ä q o (k, t) ä 1≤k≤t. The model and the methods for parameter estimation were implemented in MATLAB version R2012b and are available on request.
Data acquisition An experimental study was undertaken at the Divo research station at the National Research Centre for Agronomy (CNRA, 5°46′04. 07″N, 5°13′22. 09″W) in central western Ivory Coast. This experiment was conducted under common field conditions. Average relative air humidity is ~80 %, average annual rainfall is between 1100 and 1400 mm, and mean temperature ranges between 28 and 32 °C. The 6-month-old seedlings were planted in July 2015 with a spacing of 1. 50 m and 3 m between the lines. Fifteen 2-year-old coffee trees (C. canephora) were Downloaded from https://academic. oup. com/aob/article/126/4/687/5881214 by guest on 15 September 2020 measured. At this stage, no branch mortality nor flowering was observed. All plants were described phytomer by phytomer (i. e. a segment of the shoot, which includes an internode, the leaf and axillary buds).
The number of phytomers was recorded for each branch, starting from the top of the main stem. The absence or presence of branches on the trunk and their positions were noted. Plant topology was recorded following the Multi-scale Tree Graph (MTG) formalism  (Godin and Caraglio, 1998)  and analysed using Xplo software (Griffon and de Coligny, 2014, http://amapstudio. cirad. fr/). For each phytomer, the length of the underlying internode, the diameter in the middle of the internode and the presence of branches were measured. We recorded the number of leaves, their length and width. The dry weight of each phytomer (stem and leaves were separated) was recorded (dried at 100 °C for 48 h).
Illustration of the whole estimation process on real experimental data and investigations on source-sink ratio influences Results of the crown analysis step. The method of crown analysis, as briefly recalled in the previous section, was applied to the coffee dataset. Figure  5  presents in a schematic form the topology of four different trees out of the 15 measured, illustrating the variability in their development. Some branches are always missing at the bottom and the ramification rate increases with plant age. In this first step, this observed variability is assumed to be simply driven by stochastic events with constant or varying probability rates that have to be estimated. Figure  6A  shows the variability of the number of phytomers of the branches located at rank k below the top of the trunk.
Use of Q/D ratio as a regulating variable This work investigated, for the first time, the possible use of internal trophic competition as a regulator of plant development in the GL4 GreenLab model using the 'crown analysis' method to guide our sampling strategy. Following the in silico study by De  Reffye et al. (2018) , the model was tested on a real dataset from 15 coffee trees. The estimation procedure can be considered as globally satisfactory with good adjustment between observed and simulated data for the organic series in Fig. 7. Although this does not prove the validity of the model, it is an encouraging sign for assuming that the trophic dynamics generated by the model are consistent with real dynamics throughout the plant life. Clearly some remaining discrepancies can be tackled, such as the dramatic effect of first leaf fall on biomass production, but we consider that the global trends are interpretable, which is our goal in this study.
Indeed, De Reffye (1981) reported that the branching probability was 0. 69 on older coffee trees (300 nodes were measured). Regarding phytomer emission probability, no clear correlation was obtained, although a general trend could be observed. However, the amplitude of the variation of this parameter is far more limited than for the branching probability: b h varies between just 0. 87 and 0. 96. So the effects of noise are likely to have much more impact especially with small sample sizes. Despite these limitations, the full model with retroaction generates simulations that correctly reproduce the data on the average organic series. This is encouraging for pursuing further similar studies, especially as the resulting model gains in mechanistic degree. Moreover, this result is in line with other studies using previous versions of GreenLab.
Limitations and perspectives The methodology developed for estimation of the 'GL4' model has the advantage of relying on a relatively light experimental protocol and our first in silico tests revealed correct results for the estimation  (De Reffye et al. , 2018). Further tests are needed, especially by Monte-Carlo simulations with increasing sample sizes, to better quantify the accuracy of the estimation process. Indeed, even though the dataset can theoretically be built using only a few randomly sampled phytomers, in practice the large variability in both organ numbers and weights requires large sample sizes to provide reliable mean values. Such uncertainties should be better quantified by systematic analysis. Clearly the ratio of biomass supply to demand is not the sole variable regulating plasticity of real plants.
Environmental effects, and hormonal or biomechanical constraints also need to be taken into account. For instance,  Pallas et al. (2010)  found that a stochastic development model driven by trophic competition could reproduce more adequately their experimental datasets for grapevine when the additional influence of water stress was taken into account. Water is also a main influential variable for  Coussement et al. (2018) , who argue that plant growth can be sink-limited under water stress conditions. Inclusion of environmental variables could therefore be considered in our 'GL4' GreenLab version, as well as several modelling refinements, to provide added realism. The current simplified version has nevertheless many advantages that we have exploited in this study. In particular, as a 'toy-model', it provides a useful framework to test new methodologies such as the procedure of parameter estimation presented in the current paper.
To allow a unified specification, a pivotal language named HiLLS (for High Level Systems Specification) is proposed  (Aliyu et al. , 2016). It is a visual language which operational semantics for simulation is given in DEVS (while other semantics are given in other languages for other kinds of analysis). HiLLS combines the expressive power of automata theory (through which DEVS concepts are visually captured), object oriented methodology (so that encapsulation, composition and inheritance are supported), and logic (which allows variables, parameters and operations be captured as predicates, so that they can easily be manipulated symbolically).
Inventories are still essential for characterizing natural habitats (e. g. Corine Biotope, or Natura 2000 for European countries), and consequently for the conservation of associated animal species. The identification problem has long hindered the development of citizen science programmes for monitoring a large number of plant taxa. Overcoming the issue requires (i) developing a computational framework for automatic identification, (ii) acquiring initial training data for efficient automated identification and (iii) building platforms and databases collecting large quantities of observations to meet the multiple needs in this respect. This explains recent investment in computer techniques  (Christin, Hervet, & Lecomte, 2019; Wäldchen & Mäder, 2018)  based on artificial intelligence and deep learning technologies, in particular.
The search for high-performance methods to identify species by image, in order to support citizen science actions, is a recent field of research pursued by computer research teams  (Ceccaroni et al. , 2019) , particularly within the framework of the LifeCLEF international scientific forum  (Joly et al. , 2019). The plant identification task of this forum involves dozens of teams around the world, which use datasets made available every year since 2011. Annual overviews of this task are available in the following publications:  Goëau et al. (2011) , (  2012 ), (2013b), (  2014 ), (  2015 ), (  2016 ), (  2017 ), (  2018 ) and (2019b). Developing operational platforms relying on these methods still represents a real challenge due to the large expectations to which they are subject, for their identification performances in particular.
In particular, a major aim was to facilitate the exchange of data between associative structures, such as those of Tela Botanica and Pl@ntNet, and public platforms, in the long-term. In order to create awareness, the Lewa Conservatory produced an eye-catching leaflet made available in lodges and various offices around the nature reserve, to communicate and encourage guests and people living and working within the Lewa Conservatory to take part in the citizen science project. Practical training sessions were organized to train field officers using the app. Meetings with partners of the nature reserve were also organized, such as the Northern Rangelands Trust 8 and the Ngare Ndare Forest Trust 9 (local community conservancy membership organizations), in order to foster exchanges on the limits and potential benefits of using a context-specific version of Pl@ntNet for local stakeholders involved in development and nature preservation.
Communication Communication is a key factor in the success of citizen science initiatives  (Constant & Roberts, 2017). Involving citizens in participatory science approaches requires effective communication, adapted to the objectives of research and conservation programmes, developed within a particular territory. Communication must be targeted, in terms of format, channels of dissemination and information content. Early sharing of the objectives of the citizen science programme is essential to facilitate the involvement of participants. In the case of the Ramières initiative, sharing and analysing data collected with an external scientific advisor from the Conservatoire Botanique National Alpin was the first objective. It required setting up an export mechanism to facilitate this process, without which the data produced by the reserve managers cannot be valued.