dataesr/scientific-paragraphs-categorization huggingface dataset:
Threat to the validity of the predictive model validation As internal threats, we acquired the real dataset ourselves using our own code, which may be prone to errors. But this data are used in production by the Keolis company, and has been controlled using business rules many times. Also, we used only complete data. No imputation was done. Our code uses a high-level language and state-of-the art libraries for data extraction and machine learning. As construct validity, we choose to use Random forests as ensemble methods model for regression. The choice we made was based on performance amongst a set of models tested on a small sample of data from which random forests performed better (in terms of results and computing time). However, we assumed that the models would behave in an analogous manner with a wider dataset, whereas there is a small risk that this is not the case.
Finally, the scope of the validation of the predictive model section is to assess the quality of compositional prediction for composite objects in complex environment against traditional macro approach, we then considered the machine-learning model selection as a secondary issue, thus we probably could have different results with other models. Yet, choosing better models for compositional prediction is another issue that probably needs a dedicated work. As an external threat, our dataset, time frame and targets selection might have characteristics that could prevent generalization beyond these lines or Rennes Metropolis. On the other side, we gathered 15M tuples over a year from a large city common bus transportation system. The chosen lines are typical and of different kind (urban, inter-district, metropolitan and express).
The compositional prediction approach enables "what if" scenarios such as prediction on paths (lines) with edges (inter-stations) that are unknown to the model. We address such scenarios in our validation by testing our models on lines that are not in the training dataset. Nevertheless, we presented other scenarios that are not in the validation such as step by step scenarios (eg, creating new lines). This type of scenario can be assimilated to a shortest path problem for which we propose a simple fast search algorithm. Still, the greedy search is not optimal and state-ofthe-art algorithms such as the Bellman-Ford algorithm, the Floyd-Warshall algorithm or the Dijkstra algorithm could be used. The validation and the search for optimal algorithm to this application (taking into account execution time, performance) can be the subject of a future work.
Moreover, they add that predictions might be better if one splits the datasets into sub-datasets in which data represents similar conditions, restricting the models prediction scope. Thereby, our micro-learning approach can be considered as a divide and conquer method following this idea. Mendes-Moreira and Barachi  [19]  proposed a prediction model for networks by predicting sub parts of the networks and re-conciliate the aggregated predictions of the sub-parts with the path they are part of. They do this using a method they called Reconciliation For Regression (R4R) by weighting each sub-prediction using a constraint least square algorithm. Their results show that they reach state of the art performance for bus travel time prediction. However, it is unclear on how far from the reality their model perform without MAPE.
They aim at providing a reference for model-driven and data-driven modelling issues. Their work proposes a data-centric and modeldriven approach to integrate heterogeneous models and data into a single framework for the entire life-cyle of socio-technical systems. DataTime can be considered as a practical implementation fitting the MODA conceptual framework. Hartmann et al. [10, 11]  worked on temporal graphs to analyze data in spatio-temporal dimensions, which embed historical analysis and predictions. Their tool, Greycat, includes a scalable graph-oriented data model that allows the building and analysis of multiple parallel worlds (forks of graphs) in a single framework. Their model is meant to be used for fast evolving networks such as smart-grids, cyber-physical systems or IoT systems that yield a lot of data and that can physically evolve quickly.
DataTime makes it possible for domain experts to integrate past, present and future within a traditional information system containing a priori models. Still up to now, using DataTime requires experts to work at the level of the chosen programming language, here Scala, which is seldom known to the IT department of an IPTS operator such as Keolis. We thus plan to make it easier to use the DataTime framework by providing a set of DSL embodying its main abstractions, thus facilitating the integration of new data sources, the edition of the network topology, or make data analysis more fluid. As future work, we envision applying our framework to other application domains that bear structural and temporal similarities with IPTS, such as smart grids, water abduction systems, or supply chains. As long as they fit the directed graph abstraction at the core of DataTime, and as long as global properties of interest could be obtained by composing atomic values associated to edges, we do not foresee any issue in specializing DataTime towards these systems.
The last section contains some comparative tests speed versus accuracy of our implementation with well-known algorithms used for the QTAIM analysis, the Bader Charge near-grid algorithm of the Henkelman's group  13, 25, 26  and the Keith's promega algorithm implemented in the AIMALL package  12, 30  applied to two selected molecular systems. Note that the near-grid algorithm was implemented in the popular Multiwfn. 31 The Yu and Trinkle weighted-grid algorithm  9  was also implemented in the of Henkelman's group for Bader charge analyses. These latter programs are typical examples of well-maintained codes which perform the QCT analysis from Gaussian (or slater) molecular wavefunction data. These data are gathered in a wfn file or a grid cube file format. The wfn format, initially introduced in the Bader's program AIMPAC, is currently supported by numerous quantum chemistry software, such as Gaussian, GAMESS-US, Q-Chem, or NWChem.
| COMPUTATIONAL DETAILS Geometry optimizations have been performed at the hybrid density functional B3LYP level with the Gaussian09 software. 32 The standard all-electron basis set 6-31+G(d,p) was employed for all atoms. To illustrate the applicability of our implementation, we present a set of results using a standard uniform 3d-grid for both electron density and ELF. A grid step varying between 0. 075 and 0. 1 bohr was used. This range is known as a standard quality step and already gives fine grids, tailored to semi-numerical based partitions algorithms. The grid-based corrected algorithms have been implemented within an OpenMP technology parallelization scheme where a shared-memory parallel model is used. The grid-based parallelization scheme and its implementation used in this work is similar to that described by Rodriguez et al.
8  Briefly, it relies on the fact that the assignment for a set of grid points remains independent of the assignment of any other set. Thus, sets of grid points to be automatically assigned to a basin are distributed to several processors using the OpenMP technology. The implementation was carried out in the TopChem2 program package, written in Fortran 90 compiled by the open-source GNU Fortran compiler. 15 TopChem2 was optimized for GNU Fortran compilers and capable of using all available CPU cores, which can significantly reduce the computational time. A MPI implementation, termed TopModMPI, is also currently under development.
It was implemented in several packages owing to its ease of implementation and relatively good performances. 15, 31 However, this algorithm depends quite strongly on the grid spacing and it requires, in principle, a finely sampled grid. This could limit its application to large systems, except with our acceleration process described above that enabled such applications. Note that the Yu and Trinkle algorithm  9  was implemented in the code Bader charge analysis. 25 The method introduced a subgrid accuracy in the computation of QTAIM volumes. They implemented a weighting scheme to handle the fraction of an elementary grid volume to be connected with a neighboring grid point. The Henkelman and Tang algorithms will be respectively termed as on-grid and near-grid throughout the paper.
Air pollution and meteorological data The period examined was from 30/6/2017-30/6/2018, as one year covers the growing season of all species. Concerning Florence, meteorological data were collected by a station located at CNR LaMMA / IBE, 43 • 50 ′ 56" N -11 • 09 ′ 04" E -45 m a. s. l. Air temperature (T) and relative humidity (RH) were measured at 5 m above the ground thanks to a thermohygrometer (Vaisala), and wind speed and direction were obtained at 20 m with an ultrasonic anemometer (Gill windsonic). Global radiation (G) was recorded by a radiometer (Kipp&Zonen) placed 16 m above the ground. Hourly air pollutant concentrations were recorded at the stations Gramsci (NO 2 , PM 10 ) and Settignano (O 3 ), situated in the city of Florence, by the Tuscany Region Environmental Protection Agency (ARPAT).
Regarding Bucharest (Romania) and Tokyo (Japan), air pollutant and meteorological data were available from monitoring stations belonging to the National Agency for Environmental Protection (ANPM) and the Atmospheric Environmental Regional Observation System (AEROS), respectively. In particular, hourly concentrations refer to a station located in the city center of Bucharest (44 • 26 ′ 50" N -26 • 02 ′ 12" E -91 m a. s. l. ) while in Tokyo air pollutants were measured in Shinjuku (NO 2 , PM 10 ) and Nishi-Tokyo (O 3 ). Moreover, as in Japan suspended particulate matter (SPM) is monitored, we used this conversion factor for PM 10 (= SPM × 1. 07,  Koyama and Kishimoto, 2001).
Removal of air pollutants A simplified formula was applied to estimate the flux of air pollutants (F tree , g tree -1 s -1 ) to individual trees  (Omasa et al. , 2002; Nowak et al. , 2006; Bottalico et al. , 2017). F tree = LA × F leaf (1) F leaf = (1-R) × V d × C (2) where F leaf is the flux of air pollutants removed per unit leaf area (g m - s -1 ), LA is leaf area of an individual tree (m 2 ). LA was estimated by multiplying leaf area index (LAI, m 2 m -2 ) by crown area (m 2 )  (Nock et al. , 2008). Species-specific crown area data were obtained from nursery catalogs  (Innocenti & Mangoni piante Catalogo, 2022; Vannucci piante Catalogo, 2022)  and LAI values were obtained from a global database of field-observed LAI in woody plant species  (Iio and Ito, 2014). R is the resuspension rate (R = 0 for O 3 and NO 2 ,  Omasa et al.
CO 2 storage and sequestration The total carbon stored by a single plant species was determined by multiplying dry weight tree biomass (DWB) by a conversion coefficient of 0. 50, and then by 3. 67 to convert carbon into CO 2  (McPherson and Simpson, 1999). DWB of an individual tree or shrub was calculated based on the estimation of plant volume with the equation developed by  Wu (2019) : DWB i = d⋅t⋅ρ i ⋅V i (19) where V i (m 3 ) is the stem volume; ρ i (kg⋅m -3 ) is the species-specific wood density; t (1. 28) is the total biomass conversion factor to include belowground biomass based on the average root-to-shoot ratio; d is a constant to convert fresh weight to dry weight and it is 0. 48 for evergreen species and 0. 56 for deciduous species. To establish the speciesspecific ρ i we used the values included in the global wood density database  (Zanne et al.
, 2009)  referring to European region. To calculate the individual stem volume, we used the following allometric formula proposed by  Burkhart and Tomé (2012) : V i =b 0 + b 1 (DBH) 2 h (20) The coefficients b 0 and b 1 were set to 0. 00626 and 0. 00003666, respectively, as proposed by  Burkhart (1977). DBH is the stem diameter at breast height, while h is the plant height; these species-specific values were found in the Urban Trees Database  (McPherson et al. , 2016). For each species, V i was calculated with DBH and h values measured across a range of plant ages. With this approach, we calculated CO 2 storage according for 61 street tree species present in the Urban Trees Database. For each individual species, annual CO 2 sequestration was obtained as CO 2 storage/ tree age. Finally, a linear regression between tree age and CO 2 sequestration was estimated allowing to determine CO 2 sequestration for 50year-old trees (CO 2 seq 50) for each species as a reference tree age.
Species ranking For each species, a score was assigned to synthesise their ability to remove air pollutants. The ranking was obtained starting from the database and calculating the average removal or abatement value for each pollutant. A score of 0, 1, 2, 3 was assigned when values were ≤ the 25th percentile, between 25th and 50th percentile, between 50th and 75th percentile, ≥ 75th percentile, respectively  (Sicard et al. , 2018). The final species ranking was obtained by summing up the individual scores for each pollutant, so that a score > 10 means elevated removal ability and a score < 3 means low ability. Moreover, the adaptation to the local climate of these species was considered taking into account specific ecological indices (temperature and continentality) suggested by  Ellenberg (1974).
Also  Xie et al. (2022)  stated that crown morphological structures have a greater impact on particulate retention than leaf traits. For CO 2 storage and sequestration, we exploited a new empirical approach that considers species-specific woody density and stem volume across a range of plant ages. Differently to our approach, previous studies (e. g. ,  Nowak and Crane, 2002)  settled the average annual diameter growth at 0. 61 cm for park trees without making a species-specific distinction. Therefore, FlorTree allows more reliable estimates of CO 2 uptake taking into consideration both species and annual growth deriving from a recently developed dataset for urban trees  (McPherson et al. , 2016). The annual species-specific CO 2 sequestration that we found (range 8-111 kg CO 2 tree -1 year -1 ) is similar to  Baraldi et al.
Species selection for urban greening in Florence The final database is the result of a collection of several speciesspecific traits and can be an important tool for urban planners and administrations, as it offers guidelines for future plantations. We exploited the database to choose the most suitable species to plant in Florence urban context showing the best performance for the removal/abatement of O 3 , NO 2 , PM 10 and CO 2. To be noted that the final selection should consider other co-factors such as: pollen allergenicity  (Cariñanos et al. , 2019) , invasiveness  (Dickie et al. , 2014) , drought tolerance and pest and disease resistance  (Sicard et al. , 2018). The final list included 24 high trees (4 conifers and 20 broadleaves) although careful attention to co-factors should be required for some of these species despite their excellent ability to remove air pollutants.
Acknowledgements This work was carried out with the contribution of the LIFE financial instrument of the  European Union  ( LIFE19 ENV/FR/000086 ) in the framework of the  AIRFRESH project  " AIR pollution removal by FoRESts for a better human well-being " and of the  National Recovery and Resilience Plan (NRRP) ,  Mission 4 Component 2 Investment   1. 4 -Call  for tender No. 3138 of 16 December 2021, rectified by Decree n. 3175 of 18 December 2021 of  Italian Ministry of University  and Research funded by the  European Union -NextGenerationEU , Award Number: Project code  CN_00000033 , Concession Decree No. 1034 of 17 June 2022 adopted by the  Italian Ministry of University and Research ,  CUP B83C22002930006 , Project title " National Biodiversity Future Center -NBFC " ( Spoke 5 ).
(Garibay-Rodriguez et al. ; Li and Zarzycki)   that can be applied more efficiently to complex large scale models (e. g. , performance assessment models). Romanchuk et al. focus on the question of self-consistent Surface Complexation Model (SCM) database development by presenting a data fitting workflow that combines new and literature-derived sorption data for three radionuclides in specific oxidation states [U(VI), Eu(III), Np(V)] and the iron (III) oxide mineral goethite fit using the PHREEQC  (Parkhurst and Appelo, 2013)  geochemical solver linked to the MOUSE  (Linge et al. , 2020)  optimization software with automation that relies on the Python programming language. They show that expansion of their approach to a larger set of elements [Cd(II) and Zn(II)] is relatively straightforward and suggests further expansion by adopting Linear Free Energy Relationships (LFER) across an even greater number of sorbing elements.
Again, new and literature data are combined to develop a comprehensive understanding of Sr diffusivity and sorption to hydrated cements. The analysis reveals surprisingly enhanced retardation of Sr and suggests that cellulose organic degradation products impact Sr retention though the exact mechanisms have yet to be revealed. Nevertheless, the results point to the need to expand surface complexation and surrogate modeling approaches beyond simple binary radionuclide sorption models and incorporate more complex ternary organic complexes. Importantly, the increased complexity brings increased importance to the development of large FAIR sorption databases  (Wilkinson et al. , 2016)  that capture the multidimensional sorption conditions. Development of software and workflows that can readily take advantage of the rich data sources available in the literature will greatly benefit the nuclear waste repository research community in the coming years.
An apparent roadblock in SCM database development is the apparent data paucity as it relates to development of well constrained geochemical models of relevant reactions. However,  Zavarin et al. (2022)  determined that the cumulative number of peer reviewed publications referencing the topic of sorption and adsorption has already reached ~1,000,000. Thus, as several authors discuss, this apparent lack of data is, in large part, due to a lack of FAIR data rather than lack of data per se. This lack of FAIR data has severely limited the development of self-consistent SCM databases. A notable exception is that of  Dzombak and Morel (1990)  who developed a mechanistic diffuse layer surface complexation database for the sorption of 22 elements to the mineral hydrous ferric oxide (HFO). A follow-on effort by  Karamalidis and Dzombak (2010)  developed a similar database for gibbsite.
The HFO database was developed by critically evaluating 169 literature references. To this day, this database remains the most widely used database for predicting metal interaction with HFO. However, the database is static (i. e. , cannot be updated) and any reactive transport models that uses this database must include the same choice of thermodynamic data (i. e. , aqueous speciation constants, activity correction models) and surface complexation model (i. e. , two-site diffuse layer surface complexation) employed by the original authors. A more recent data assimilation effort at Lawrence Livermore National Laboratory and collaborators at the Helmholtz Zentrum Dresden Rossendorf, has been developing a data digitization pipeline  (Zavarin et al. , 2022). To date, the manual digitization of data has yielded a LLNL SCIE database that includes 211 references and a total of 22,732 individual digitized data and associated metadata.
Importantly, this approach to data mining is labor intensive and cannot be expanded beyond a set of targeted data (e. g. , reactions specific to the nuclear waste performance assessment needs). Adoption of new approaches to data assimilation are clearly needed. New modeling approaches that take advantage of FAIR database development efforts are also needed. Garibay-Rodriguez et al. describe a computational framework for radionuclide migration assessment in clay rocks that is based on OpenGeoSys6 which is linked to a number of geochemical solvers (PHREEQC, GEMS, etc. ) and can be applied to both traditional surface complexation/ion exchange models as well as surrogate models. For portability, the modeling framework is built around the JupyterLab Python framework that guarantees that all the source code and its dependencies can be executed efficiently and reliably, independent of any computing environment.
Importantly, the surrogate models (aka look-up tables) are explicitly tied to the mechanistic surface complexation/ion exchange models. Test cases suggest that surrogate models can yield significant computational speed-up while maintaining a similar level of precision in model output. Garibay-Rodriguez et al. argue that open source software approaches are key to continuing software development and longterm improvements in nuclear waste performance assessment models. Li and Zarzycki describe a computational pipeline to generate synthetic SCM data and discuss approaches to transform this dataset into AI-learnable input for use in surrogate models. In their approach, available surface complexation model databases are used to produce high density sorption data across a very wide range of conditions.
The generated synthetic datasets can more readily take advantage of AI algorithms that can be used in surrogate model development. The approach is tested for determination of the electrostatic properties of a prototypical oxide/electrolyte interface using the triple-layer surface complexation modeling construct. The authors provide a theoretical framework for developing surrogate models using AI and based on developing synthetic sorption data from traditional surface complexation databases. While these surrogate approaches can yield computational efficiencies that may benefit performance assessment modeling efforts, it should be noted that applicability of surrogate models is often limited by the range of data available for model training. Nuclear Waste Repository reactive transport and performance assessment modeling teams have a number of opportunities to apply modern processes, workflows, and machine learning in predictive tools for informing society of impacts and risks associated with nuclear waste repository scenarios.
It is our aim to avoid learning these features directly from data, as that would require either an excessively large dataset or a highly-specialized learning architecture. We therefore opt for keeping the constitutive model as intact as possible and instead increasing flexibility by allowing some (or all) of its material parameters to evolve in time. The resulting model can be seen in Fig. 1 : a data-driven encoder 1 that learns the evolution of a set of material properties is linked to a physics-based material model decoder that maps strains to stresses. In contrast to other strategies in literature, we keep the architecture as general as possible: a general feature extractor parses 1 We adopt the terms encoder and decoder in order to clearly split the model in two distinct parts which we will treat with different learning approaches.
This leads to a parsimonious model with a single LSTM cell with 5 latent units. At this point it is interesting to draw a parallel between the network and the micromodel whose behavior is being reproduced: the concatenation of the hidden state 𝐡 and cell state 𝐜 of the LSTM cell can be seen as a lower-dimensional surrogate for the set of microscopic internal variables 𝜶 𝜔 of Eq. (3). However, in contrast to the variables in 𝜶, the latent variables 𝐡 and 𝐜 have no physical interpretation and evolve purely according to heuristic memory mechanisms that mimic patterns inferred during training. First, we train the LSTM using only monotonic data. Since only one strain component is being modeled, this initial dataset is composed simply of one strain path in tension and one in compression. The trained model is then used to predict a tension path with one unloading-reloading cycle.