dataesr/scientific-paragraphs-categorization huggingface dataset:
Pumping wells In terms of current use of the wells for groundwater abstraction (Fig. 6 ), groundwater uses were grouped in three categories: drinking water supply, irrigation use, and industrial uses (such as Caracol industrial park, Larue distillery, ice factories, water purifiers, etc. ). Drinking water is supplied through a few deep boreholes and a large number of semi-deep boreholes (hand pumps). For the deep boreholes supplying the municipalities of Cap Haitien and Ouanaminthe, the abstracted volumes were collected from DINEPA (DIrection Nationale de l'Eau Potable et de l'Assainissement). For hand-pump wells, the estimation of the quantities required one to cross check three main sources of information to arrive at a reliable average estimation of pumped water volumes. These three sources are: the current distribution of population by municipality, the average water consumption per capita, and the pumped water volumes extracted from the wells identified under the 'Haiti Outreach' database (Haiti Outreach 2018).
For the average water consumption per capita, a distinction was made between rural areas (20 L/day per inhabitant) and urban areas (70 L/day per inhabitant); these estimations were Each well listed as functional in the Haiti Outreach database was assigned an average annual withdrawal volume-an example of the calculation is provided for the municipality of Fort-Liberté in Table  1. The same approach to estimate the withdrawals from irrigation wells proved to be difficult due to the lack of regular monitoring and reliable data. The decision was made to ignore their influence for the time being, with the exception of two working wells listed in a BRGM study performed in 2010  (Gutierrez et al. 2012 ). These withdrawals for irrigation are unknown but represent small volumes (family farms, very shallow wells).
For the estimation of water extraction for industrial uses, the survey carried out by the local team identified only eight abstraction points, related to the production of sachet drinking water. Once again, the information proved difficult to collect/ access and cross check. The estimation for the Caracol industrial park is notably underestimated. In total, 2109 abstraction wells were identified thanks to the Haiti Outreach database and introduced in the modelled area. These wells represent an average annual withdrawal estimated at 9. 5 Mm 3 /year, including 9. 3 Mm 3 /year dedicated to drinking water supply.
Hydrographic network The five main rivers crossing the alluvial plain-Haut du Cap, Grande Rivière du Nord, Trou du Nord, Marion and Jassawere modelled explicitly with the software. For each tributary, the river data are defined by their section within the cell. The hydrological priorities are defined by a method of branching tree that arranges the tributaries' connectivity and forms the modelled hydrographic network. A river section is described in the model by its geometry (length, width, and elevation of the riverbed), by its physical properties (thickness and permeability of the bed and the banks), by its source terms (pumping or injections) and by its state (height of water in the river). The geometric characteristics are taken from the field surveys carried out by DAI water technicians.
A recharge chronicle at monthly time steps  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016)  is thus assigned to each of the five recharging zones of the model. (Fig. 9 ). This seasonal recharge fluctuation was verified with some records for the same period collected at the AVANSE farms. The meteoric recharge introduced in the model shows a large deficit during the drought period observed between 2014 and 2016, with even zero recharge in some places. Concerning the inflows coming into the model boundaries, the lack of climatic data on the Massif du Nord for the period 2000-2016 was overcome by adopting an even more schematic approach. A monthly distribution, identical for each year, was defined from the calculation of the effective monthly average precipitation based on old rainfall and PET data (MARNDR), representative of the Massif du Nord.
Theoretical irrigation scenario (scenario 2) Eleven areas targeted for potential future development of banana cultivation were studied. The water requirements were estimated from FAO guides (CROPWAT 8. 0 software). In Haiti, the irrigation period lasts on average from June to October (Table  2 ). In accordance with AVANSE agronomists, the assumption was made that irrigation would be satisfied by setting up boreholes, such that one borehole structure would satisfy the irrigation requirements for a plot of 10 ha. In the identified areas for banana crop, an additional total of 270 boreholes were thus implemented in the modelled area to meet the water needs of the 11 zones identified by AVANSE (Fig. 10 ). Based on these assumptions, the unit production rate of an irrigation borehole varies between 2.
For the latter, the water inflow from Massif Finally, from an operational point of view, the two main operating constraints highlighted by the simulation of such a pumping scenario are as follows: 1. Along the coast, localised operating difficulties could be expected due to a significant risk of seawater intrusion. Indeed, under the effect of agricultural pumping, upconing phenomena and/or reversal of groundwater flow direction cannot be excluded. In particular, the areas of Cap Haitien and Caracol that have simulated piezometric levels close to 0 msl, would be immediately impacted by seawater intrusion when the water table drops below sea level. This conclusion is based on the simulation of drawdown by the model. To develop this point, it might be interesting in the future to introduce into the model variable-density groundwater flow (possibility provided by the MARTHE software).
Acknowledgements The authors are grateful to DAI for supporting this study, as well as for the modelling and for providing the necessary technicians and overall logistics to undertake the necessary monitoring, checks and surveys. The authors also thank the students of Limonade and Port-au-Prince universities and their professors who participated in the field operations as well as the development of the model. Open access availability of this article was made possible through support from  HydroLOGICA.
This approach to safe negation and to coroutining made its way into many NL consultable systems, the best known being perhaps Chat 80  (Warren and Pereira, 1982) , and more importantly, into later Prologs, as we discuss later. Deductive Databases: Dahl then ushered the deductive database field by developing the first relational database system written in Prolog  (Dahl, 1977 (Dahl, , 1982)). Other higher-order extensions to Prolog included in this system or in the one by  Dahl and Sambuc (1976) , such as list/3 (now called setof/3), have become standard in Prolog. Metamorphosis Grammars: Colmerauer's language processing formalism for Prolog, Metamorphosis Grammars (MGs)  (Colmerauer, 1975)  constituted at the time a linguist's dream, since it elegantly circumvented the single-head restriction of Prolog's Horn clauses, thus achieving the expressive power of transformational (type-0) formal grammars †.
This allowed for fairly direct while also executable renditions of the linguistic constraints then in vogue: a single rule could capture a complete parsing state through unification with its left-hand (multi-head) side, in order to enforce linguistic constraints through specifying, in its right-hand side, how to re-write it. The first applications of MGs were compilation  (Colmerauer, 1975) ; French consultation of automatic configuration systems  (Dahl and Sambuc, 1976) , where a full first-order logic interlingua was evaluated through coroutining; and Spanish consultation of database systems  (Dahl, 1977 (Dahl, , 1979)) , where a set-oriented, three valued logic interlingua  (Colmerauer, 1979; Dahl, 1979)  was evaluated, allowing amongst other things for the detection of failed presuppositions  (Dahl, 1977 (Dahl, , 1982)).
More specialized Prolog-based grammars started to emerge. Their uses to accommodate linguistic theories, in particular Chomskyan, were studied as early as 1984  (Dahl, 1986a) , leading to the new research area of "logic grammars"  (Abramson and Dahl, 1989). Further Theoretical Underpinnings: In 1978, Keith Clark * published a paper that showed NaF to be correct with respect to the logic program's completion  (Clark, 1978). This became the standard in Prolog semantics. Simultaneously, Ray Reiter provided a logical formalization of NaF's related "Closed World Assumption"  (Reiter, 1978) , which assumes that everything that is not known to be true is false (whereas NaF assumes that everything that cannot be proven to be true is false). This then led to substantial research on non-monotonic reasoning in logic programming, and to inspiring foundational work on deductive databases by Reiter himself, as well as Herve Gallaire * , Jack Minker * and Jean-Marie Nicolas  (1984).
MU-Prolog (1982) In 1982, another implementation named MU-Prolog  (Naish, 1982 (Naish, , 1986) )  was developed by Lee Naish in Melbourne. It was initially a simple interpreter to understand the workings of Prolog, as the author could not find a Prolog system for his hardware. The system offered efficient coroutining facilities and a delay mechanism similar to those discussed in Section 2. 4. 1 to automatically delay calls to negation and if-then-else constructs, as well as meta-logical (e. g. functor/3) and arithmetic predicates. Its rendition of the delay predicate, here called wait, allows for declarations to be provided manually but also generated automatically. MU-Prolog was one of the first shipping database connections, module systems, and dynamic loading of shared C libraries, as well as sound negation (through delay/2, as in Section 2.
BIM Prolog was funded by the Belgian Ministry of Science Policy and was based on the WAM. One of the earliest resources available is an internal report  (Janssens, 1984). BIM-Prolog developed into a system with the first WAM-based compiler to native code (as opposed to, e. g. , threaded code by Quintus), with interfaces to several database systems (Ingres, Unify, etc. ), a graphical debugger in the style of dbxtool, a bi-directional interface to C, decompilation even of static code, and multi-argument indexing of clauses-which overcame the common practice of indexing Prolog clauses via their head's first argument alone. Its first release was on SUN machines, and later it was ported to Intel processors. BIM was involved in the later ISO standardization effort for Prolog. BIM went out of business in 1996.
IBM  Prolog (1985)  Several Prolog systems that ran on specific IBM hardware remained unnamed and were referred to as IBM Prolog. Here, we focus on Prolog systems distributed by IBM. In 1985, IBM announced a tool named VM Programming in Logic or VM/Prolog  (Symonds, 1986) , which was its Prolog implementation for the 370, focusing on AI research and development. Its development started in 1983 by Marc Gillet at IBM Paris according to  Van Roy (1994). In 1990, a 16-bit Prolog system for OS/2 was announced, including a database and dialog manager. It was able to call programs written in other IBM languages, such as macro assembler, C/2 and REXX scripts. While its syntax was based on its predecessor, it also provided support for the Edinburgh syntax considering the ongoing ISO standard development.
As the 68k faded away, Damas developed a macro language that could be translated into VAX-11, MIPS, Sparc and HP-RISC. Unfortunately, porting the emulator to the x86 was impossible, so a new one was designed in C, making it also easier for some excellent students to contribute. Rocha implemented the first parallel tabling engine  (Rocha et al. , 2005)  and Lopes the first Extended Andorra Model emulator  (Lopes et al. , 2012). This work was well-received by the community, but proved difficult to use in scaling up real applications. The problem seemed to be that many YAP applications used Prolog as a declarative database manager. In order to support them, the team developed JITI  (Costa et al. , 2007) , a just-in-time multi-argument indexer that uses any instantiated arguments to choose matching clauses, hoping to avoid shallow backtracking through thousands or millions of facts.
As C was used as the intermediate language, compiled Prolog programs would map to considerably larger C programs which were very slow to compile using GCC, with little benefit as the code had very explicit and low-level control flow (e. g. labels and gotos. ) This situation led to the replacement of C by a much simpler specialized mini-assembly language  (Diaz and Codognet, 2000)  as the intermediate language for compiling Prolog. This system came to be known as GNU Prolog. ECL i PS e  (1993)  The earliest resource about the ECL i PS e logic programming system  (Schimpf and Shen, 2012)  is the technical paper by  Wallace and Veron (1993). Originally, it was an integration of ECRC's SEPIA, an extensible Prolog system; Mega-Log, an integration of Prolog and a database; and (parts of the) CHIP systems.
There is also a long term goal of integrating databases into Prolog by having a driver that allows YAP to use a database as a Prolog predicate. In the future, YAP's strength lies in the ability to write and maintain large applications. Its team has worked on three key points toward this goal: interfacing with other languages  (Angelopoulos et al. , 2013) , interfaces to enable collaboration between Prolog dialects, and tools for declaring and inferring program properties, such as types  (Costa, 1999).
Extensions Tabling As discussed in Section 2. 8, tabling can be used to improve the efficiency of Prolog programs by reusing results of predicate calls that have already been made, at the cost of additional memory. It improves the termination properties of Prolog programs by delaying self-recursive calls. Tabling was first implemented in XSB and currently a good number of other Prolog implementations support it (e. g. , B-Prolog, Ciao, SWI, YAP). XSB and recent SWI-Prolog versions improve support for negation using stratified negation and well-founded semantics. Both systems also provide incremental tabling which automatically updates tables that depend on the dynamic database, when this is modified. Some systems (YAP, SWI-Prolog) support shared tabling which allows a thread to reuse answers that are already computed by another thread.
Influences on other Languages in the LP paradigm Datalog is a subset of Prolog, which does not allow compound terms as arguments of predicates. This topic has been worked on since the late 70s, although the term was coined later by David Maier and David S. Warren in the 80s. Datalog can be viewed as an extension of relational databases, allowing recursion within predicates. Datalog plays an important role in the research field of deductive databases  (Ramakrishnan and Ullman, 1993). Datalog has found new applications in many areas  (Huang et al. , 2011) , such as information extraction  (Shen et al. , 2007) , program analysis  (Whaley et al. , 2005; Alpuente et al. , 2010) , security  (Bonatti, 2010) , and natural language processing  (Dahl et al. , 1995). λ Prolog was developed in the late 80s, by  Dale Miller and Gopalan Nadathur (1988).
In data science, several data sources need to be cleaned and combined before applying statistical analysis and machine learning. This preprocessing step is often the most labor intensive phase of a data science project. Prolog, particularly when supporting tabling, is a suitable tool for data preprocessing. It can transparently access data from different sources without importing all data as required by relational database management systems. Subsequently, a view on the data can be established from small composable predicates that can be debugged separately and interactively. These ideas have been explored in SWISH datalab  (Bogaard et al. , 2016)  which provides a web frontend for cooperative development of both Prolog data preprocessing steps and subsequent statistical analysis using R, and used in applications in Biology using large data  (Angelopoulos and Wielemaker, 2019).
Important contributions have been made in this field through the optimization of protocols for WSOM extraction and concentration, and also through the use of mass spectrometry analysis, elemental formula assignment, and compositional analysis  [44, 45]. Nevertheless, few studies have focused on the variability in the measurement system itself, the acceptable signal-to-noise (S/N) ratio in the data treatment, and the comparison of experimental replicates, especially concerning atmospheric matrices. It is particularly important to establish and verify these parameters to improve and facilitate sample comparison within datasets and across experiments  [46]. The main goal of this study was to determine the differences in terms of formula assignment for analytical (i. e. , measurement replicates) and experimental replicates (i.
In contrast to other works, we wanted to assess this comparison from the point of view of users, who usually have limited access to the mass spectrum characteristics and, most of the time, must deal with a list of m/z ratios and intensities. Therefore, in this work, we compared the assignments obtained for three analytical and experimental replicates of the same cloud water sample. The experimental replicates, obtained by SPE, were also compared to the results obtained after freeze drying pre-concentration of the same cloud sample to evaluate whether the presence of salts interferes with the analysis. The goal of this study was to establish a methodology that can be used for the treatment of aqueous atmospheric samples based on a comparison of three different software programs to enhance the possibility of data comparison within samples.
3. For more clarity, the schema of the experiment is depicted in Figure  1. Each sample was analyzed in triplicate by three consecutive injections in the ESI(-) FT-ICR MS, and the analytical replicates were named SPE1-1, SPE1-2, SPE1-3; SPE2-1, SPE2-2, etc. , as reported in Figure  1. After internal recalibration, the signal was extracted using two S/N ratios of 5 and 7, as described in Section 2. 4. At this step, we had 42 peak-lists that were assigned using the three software programs described in Section 2. 5. We ended the experiment with 126 lists of molecular formulas (MFs). The blank samples were treated as individual samples, and the MFs assigned to BSPE and BLYO were excluded from the samples as the last step of the data treatment, as reported in Section 3. 5 of the Results and Discussion section.
Preliminary Treatment with DataAnalysis Each spectrum was preliminarily treated immediately after acquisition with DataAnalysis. Spectra were internally recalibrated using the recalibrant list reported in Table  S2 , which contains compounds that were selected after identifying their isotopic fine structures in mass spectra obtained from real recalibrated cloud samples. The linear recalibration was chosen, and the average standard deviation obtained after this step was below The blank samples were treated as individual samples, and the MFs assigned to BSPE and BLYO were excluded from the samples as the last step of the data treatment, as reported in Section 3. 5 of the Results and Discussion section.
The weighted percentages of common MFs were 90 ± 9% for Composer, 90 ± 10% for MFAssignR, and 87 ± 4% for DataAnalysis (LYO2 was not considered). This confirms that the intensity of the mass signal should be considered when comparing MF datasets. Molecules 2022, 27, x FOR PEER REVIEW 7 of 22 LYO2 (standard deviation of 15. 1% and 17. 4% at S/N 5 and S/N 7, respectively). This could be linked to the presence of salts in the freeze-dried extracts, which made the spectrum noisier. Moreover, the presence of salts can lead to interference in the internal recalibration and in the assignment  [60]. For this reason, the present work is mostly focused on the SPE results. We also checked the number of MFs in the blank samples. In our previous works, which involved assignment with Composer, the peak-list of the blank was excluded from the peak-list of the sample.
We decided to consider the third replicate, because the mass signal was generally more stable. Figures  3  and 4  depict, in the bottom line, the percentage of common MFs for each sample, considering only the number of MFs (percentage in number, Figure  3 ) or the number of MFs and their intensities (weighted percentage, Figure  4 ). As expected, the number of common MFs at S/N 5 was always lower than at S/N 7. To avoid interference from the instrument background signal, it is probably more appropriate to use S/N 7 for data treatment. Moreover, there were generally more common MFs in Composer and MFAssignR than those assigned by DataAnalysis. We found that the common MFs represented 62 ± 18% of the total for Composer, 58 ± 19% for MFAssignR, and 49 ± 12% for DataAnalysis (LYO2 was not considered).
It is very difficult to explain this result because the internal recalibration was the same for all peak-lists. In response to this finding, we also calculated the weighted percentages of common MFs, taking into account the intensity of the signal. These calculations confirmed that the peaks with higher intensities were more likely to be assigned to the same MFs in the three replicates than the peaks with lower intensities. In Figure  4 , the trends are the same as those observed for Figure  3 : lower percentages for S/N 5 than for S/N 7 and comparable values for Composer and MFAssignR that are higher than those found for DataAnalysis. The weighted percentages of common MFs were 90 ± 9% for Composer, 90 ± 10% for MFAssignR, and 87 ± 4% for DataAnalysis (LYO2 was not considered). This confirms that the intensity of the mass signal should be considered when comparing MF datasets.
Experimental Replicates-SPE Figure  3  shows that only 56% of the MFs were present in the three SPE extracts, considering the results from Composer software, and this percentage decreased for MFAs-signR (48%) and DataAnalysis (34%). Assuming similar ionization efficiencies, the use of intensity-weighted MFs may improve the comparison between samples. Looking at the weighted percentages, the values increased to 92%, 86%, and 80% for Composer, MFAs-signR, and DataAnalysis, respectively. To better understand the source of this difference, some parameters, such as the average intensity, the MF error, the numbers of carbon, hydrogen, oxygen, nitrogen, and sulfur, and the DBE, were compared. Figures  S1-S3  depict the differences in the DBE and number of atoms for each fraction following data treatment with Composer, MFAssignR, and DataAnalysis, respectively.
051 ± 0. 004 ppm on the calibration slope, lower than 0. 1 ppm, which is considered the threshold value for satisfactory internal recalibration. This means that some of the recalibrants were probably impurities contained in the solid phase that were eluted with the sample during the extraction process. This hypothesis was strengthened because the number of recalibrants found in BLYO was generally lower and at a low intensity, leading to non-optimal recalibration. The standard deviation associated with the recalibration was 0. 34 ± 0. 26. However, this result could have also been due to the large presence of double peaks detected in LYO. The exclusion of the MFs of BSPE in the SPE data treatment list "Common" led to the elimination of 222 (7. 0%), 244 (8. 8%), and 230 (15. 7%) MFs for Composer, MFAssignR, and DataAnalysis, respectively.