dataesr/scientific-paragraphs-categorization huggingface dataset:
Provenance analysis The reliability of geochemical sourcing in archaeology depends on the body of reference data available in order to discriminate regions, sub-regions and specific geological features among a well-defined set of possible provenance locations. The reference data used for inferring geological provenance of the Teti'aroa samples builds on a combination of the online GEOROC database (http://georoc. mpch-mainz. gwdg. de/georoc/) and data deriving from geological survey and mapping conducted in French Polynesia by the Bureau de Recherches Géologiques et Minières (BRGM, France).
Major element data Loss on ignition (LOI) ranges from negative values to 4. 5 wt%. They are lower than 1. 5 wt% for 40% of our sample set, between 1. 5 and 3 wt% for 50% of it and between 3 and 4. 5 wt% for the remaining 10% of our samples (Table  S4  in the Supporting Information). Therefore, the Teti'aroa archaeological samples can be considered fresh to moderately altered volcanic rocks, with elemental compositions consistent with those of geological samples collected in the volcanic islands of East Polynesia. The studied sample set displays a wide range of silica contents, from c. 40-58 wt% (Table  S4  in the Supporting Information). A total alkali versus silica plot (Figure  2 ) indicates that all the samples but one (andesite RIM-01-10a-a) plot in the alkaline rock field, that is above the alkaline-subalkaline boundary proposed by  Macdonald and Katsura (1964)  for Hawaiian lavas.
2010; Legendre et al. 2005b) , while a single Figure  4. Plots of selected highly incompatible element ratios against Th contents (ppm) for Teti'aroa basanites (Table  S4  in the Supporting Information) and Society, Austral-Cook and Marquesas basanites from the GeoRoc database (http://georoc. mpch-mainz. gwdg. de/georoc/). These ratios are thought to be directly inherited from their mantle sources, since Polynesian basanites show little evidence for differentiation or crustal contamination processes. C 2019 The Authors. Archaeology in Oceania published by John Wiley & Sons Australia, Ltd on behalf of Oceania Publications tephriphonolitic protrusion has been mapped on Tahuata and a single phonotephritic one in Hiva Oa  (Maury et al. 2012 ). These two rock types have not been identified in other Polynesian islands, and therefore we consider Tahiti as the most likely source for the Teti'aroa materials, especially for the phonotephrites that have not been identified elsewhere in the area (with the exception of the two isolated occurrences in Rarotonga and Hiva Oa noted above).
RIM-01-10a-a, another flake, was also found on platform RIM-01. Its geological origin west of the Andesite Line implies very long-distance connections beyond tropical East Polynesia. Unfortunately, it is not possible to locate the island source with the elemental data reported here. Isotopic data will provide better constraints to identify the source of this artefact, which potentially derives from Tonga, Fiji, or the North Island of New Zealand. While the possibility of European-induced importations cannot be excluded for such surface archaeological contexts, late contacts between the Tahiti and Tonga have already been suggested based on previous geochemical sourcing of an adze found in the foundation trench of the Lapaha tomb J09 on Tongatapu, which was probably built between 1550 and 1700 AD  (Clark 2014; Clark et al.
However, air cannot be added as a function of time into a single reactor, i. e. , the proportion of air to magmatic gases in the simulation is held constant according to the initial composition. Therefore, a chain-of-reactors approach was developed using the Chemkin Pro software to simulate the chemical evolution of a near-source plume that simultaneously cools (decreasing T) and entrains air (increasing VA:VM) as it dilutes, Figure  1. Thirty PFRs were placed in a chain. A continuous decreasing trend in plume temperature was imposed across the chain by defining the start and end temperatures for each PFR according to our chosen boundary conditions (details in the next section). The starting composition for each reactor was defined by taking the output from the preceding one and diluting it with air (78% N 2 , 21% O 2 , 1% Ar) according to time-varying VA:VM boundary conditions.
ACKNOWLEDGMENTS We are grateful to the reviewers for their comments on the original manuscript. TR acknowledges support from the  ANR Projet de Recherche Collaborative VOLC-HAL-CLIM  ( Volcanic Halogens: from Deep Earth to Atmospheric Impacts ),  ANR-18-CE01-0018 , and  Labex Orléans Labex VOLTAIRE  ( VOLatils-Terre Atmosphère Interactions -Ressources et Environnement ,  ANR-10-LABX-100-0 ). GD and CO acknowledge support from the  Orléans Labex Caprysses  (convention  ANR-11-LABX-0006-01 ) and the  NERC Centre for Observation and Modelling of Earthquakes, Volcanoes, and Tectonics (COMET) , respectively.
We used an error budget approach to a global scale on a 10 d basis in order to calculate the error variance-covariance matrix. We considered all the major sources of uncertainty in The SL_cci/ESA covers the period from January 1993 to December 2016, while all other products cover the full 25-year period (January 1993 to December 2017). Seasonal (annual and semi-annual) signals have been removed and a 6-month smoothing has been applied. An averaged solution has been computed from the six groups. GMSL time series have the same average on the 1993-2015 period (common period) and the averaged solution starts at zero in 1993. The averaged solution without TOPEX-A correction has also been represented. A GIA correction of -0. 3 mm yr -1 has been subtracted from each data set. A correction of +0. 10 mm yr -1 due to the deformations of the ocean bottom in response to modern melt of land ice  (Frederikse et al.
GMSL data series The six main groups that provide satellite-altimetry-based GMSL estimates (AVISO/CNES, SL_cci/ESA, University of Colorado, CSIRO, NASA/GSFC, NOAA) use 1 Hz altimetry measurements from the T/P, Jason-1, Jason-2 and Jason-3 missions from 1993 to 2018  for SL_cci/ESA). Each group processes the 1 Hz data with geophysical corrections to correct the altimetry measurements for various aliasing, biases and drifts caused by different atmospheric conditions, sea states, ocean tides and others  (Ablain et al. , 2009). They spatially average the data over each 10 d orbital cycle to provide GMSL time series on a 10 d basis. The differences among the GMSL estimates from several groups arise from data editing, from differences in the geophysical corrections and from differences in the used method to spatially average individual measurements during the orbital cycles  (Masters et al.
Acknowledgements. We would like to thank all contributors of the  Sea Level CCI (SL_cci)  project  (Climate Change Initiative  programme) and of the  SALP (Service d'Altimétrie et de Localisation Précise ), with special recognition to  Thierry Guinle ,  SALP  project manager at the  CNES. Financial support. This research has been supported by the  ESA  in the framework of the  Sea Level CCI (SL_cci)  project  (Climate Change Initiative  programme) supported by the  CNES  in the framework of the  SALP (Service d'Altimétrie et de Localisation Précise ). Giorgio Spada is funded by a  FFABR (Finanziamento delle Attività Base di Ricerca)  grant of the  MIUR (Ministero dell'Istruzione, dell'Università e della Ricerca ) and by a  DiSPeA  ( Dipartimento di Scienze Pure e Applicate of the Urbino 65 University ) grant.
A new search-match procedure has been developed and tested which, in contrast to previously existing methods, does not use a set of lines identified from a diffraction pattern, but an optimized Rietveld fitting on the raw data. Modern computers with multicore processors allow the routine to be fast enough to perform the entire search in a reasonable time using quite large databases of crystal structures. The search-match is done using the crystal structures for all phases and the instrumental geometry, and as such can be applied to every kind of diffraction experiment, including X-rays, thermal/timeof-flight neutrons and electrons. The methodology can also be applied to nanocrystalline samples for which peak identification may be a problem. A web interface has been developed to permit easy testing and evaluation of the procedure.
The majority of search-match improvements have resulted from the algorithms and software being able to provide increasingly reliable and automated results, limiting human intervention. In parallel, another major effort has been dedicated to increasing the number of 'cards' from Hanawalt's days, using experimental data from different laboratories and collected by ICDD into the Powder Diffraction File (PDF) databases. In the past ten years, large improvements in both  ISSN 1600-5767  the number and the reliability of cards have been obtained by including calculated 'cards' or patterns. Crystal structures from different databases, notably the Inorganic Crystal Structure Database (ICSD;  Hellenbrandt, 2004)  and the Crystallography Open Database (COD;  Graz ˇulis et al. , 2009 Graz ˇulis et al.
, , 2012)) , have been added to search-match procedures. As these databases provide crystal structures for identified phases, one can proceed with phase quantification using the Rietveld method  (Rietveld, 1967 (Rietveld, , 1969)) , which does not require any standard or knowledge of reference intensity ratio factors. In 2002, Le  Meins et al. (2003)  organized a search-match round robin, and the overall result was satisfactory. It proved that 'a good search-match software, in expert hands, combined with an up-to-date PDF can perform efficiently in solid state phase identification'. But it also showed that only one participant was able to succeed in identifying all compounds, and 'in expert hands' can still be interpreted as being far from an automatic procedure for beginners. Furthermore, the investigation of nanostructured compounds, or in general not perfectly crystallized samples, was not a concern in this round robin.
In that project, the software/ procedure FPSM (full-profile search-match) has been developed. Since then, the method and software have been tested and improved, and the full theoretical basis, details of the implementation and some test results of the search-match are presented in the following. Structural and profile parameters are not fully refined in the Rietveld quantification in order to conduct the procedure automatically. When more accurate quantification is required, we advise the use of a full Rietveld refinement employing the identified phases or application of another of the available quantification methods, in particular if unidentified phases are present  (Scarlett & Madsen, 2006; Toraya, 2016 Toraya, , 2018)). We will not discuss the quantification here, only the search-match.
Automatic Rietveld refinement The entire search and profile fitting algorithm is based on the Rietveld method  (Rietveld, 1969). In particular we use a special implementation, as in the MAUD software  (Lutterotti, 2010) , that differs from the classical Rietveld implementations in two main points: (1) The peak profile function is composed of two different contributions: the instrument and sample broadening. The two contributions are directly convolved into the resulting peak broadening, and in the analysis we do not refine the instrument broadening contribution, only the crystallite sizes and r. m. s. microstrains. (2) The refinement strategy, i. e. how the parameters are refined in successive order, is automatically decided by the algorithm and does not require human intervention or decision.
, 1988; Lutterotti & Scardi, 1990). The instrumental broadening part can be determined in advance using a standard sample  (Scardi et al. , 1994) , and to simplify its input it was made compatible with that in the MAUD software (in MAUD we added a menu function to export a CIF containing the data and the instrument characteristics and broadening ready to be loaded and used in the web interface of the FPSM program). If the experimental details are not supplied alongside the data with a suitable CIF, the web interface can be used to specify some general categories for instrumental broadening to represent usual cases and any additional information needed regarding the experimental conditions. An imperfect definition of the instrumental contribution does not prevent the search-match from working correctly (unless a low-resolution instrument broadening is used for a high-resolution instrument and well crystallized sample), but it will give wrong results for the crystallite size and microstrain values and is likely to make the search more difficult.
Search and match algorithm structure We describe in this section the main algorithm used for what we call the full-profile search-match (Fig. 1 ). We start from a crystal structure database restricted to match the elemental composition conditions. In principle the method may work with all the possible phases without any composition restriction, but we have to consider the computing time, and limiting the number of phases using some restrictions on the composition or type of sample (e. g. mineral, inorganic. ) means that a search can be performed in a few seconds or minutes. Let us call S 1 the restricted crystal structures subset, composed of N potential phases, and S 2 the subset containing the identified phases, initially empty. Each phase of S 1 is then evaluated against the experimental pattern by performing an automatic Rietveld refinement as described at the end of the previous section (three-step procedure).
We keep the last possibility as a precaution to stop the algorithm in case of a completely failed identification, giving rise to several phase additions without a real fit improvement. In our still brief experience with the algorithm, the latter happens when (i) severe errors are present in the pattern (including wrong data formats etc. ); (ii) the pattern does not correspond to the declared experimental conditions; or (iii) neither the phase nor a closely related structure is present in the database. By default, after each cycle, we eliminate from S 1 not only the phases moved to S 2 but also the ones resulting in a volume fraction below a threshold f S1 , to speed up the next Rietveld ranking by having fewer structures in S 1. Figure  1  Schematic layout of the FPSM algorithm.
Implementation The adoption of the FPSM procedure as a routine tool for quick automatic finding of more likely structures depends heavily on the speed of execution of the code. For this reason we tested in the implementation different possibilities of how to perform the calculation and how to store and parse the crystal structure database. For the implementation and testing we used both a portable computer with an i7 quad-core 2. 8 GHz processor and a workstation with 2 Â 2. 93 GHz sixcore Xeon processors, resulting in 158 gigaflops. Both systems were equipped with a solid state drive (SSD). The programming language used for the main program was C++, and we employed the database management system MySQL to store/ retrieve the crystal structures. All the crystallographic computations (space-group interpretation, symmetry operations, reflection list and structure factor calculation) were carried out using the Computational Crystallography Toolbox (cctbx; Grosse-Kunstleve & Adams, 2002; Grosse-  Kunstleve et al.
, 2002). The latter proved very efficient in all the calculations and we used only the C++ implementation without the Python bindings. We added the scattering factors for electron diffraction to the cctbx library as tabulated by  Peng et al. (1996) , to enable a simple kinematical calculation of structure factors. We tested two ways of preparing intensities of individual reflections: (1) Preliminarily calculate the intensities and store them in the database. (2) Calculate the intensities when the structures are loaded from the database. The full computation took more time for the former than the latter. It was not shortened even if a quick SSD was used or the database was further optimized in the former. Thus the latter was adopted in the present computation. Moreover, the latter reduces the size of the database for parsing and gives flexibility in changing the radiation type.
For the nonlinear least-squares algorithm we use a Marquardt algorithm  (Marquardt, 1963) , whose implementation was derived from the MAUD software  (Lutterotti, 2010). Since numerical models were partly used in the formulation, all derivatives were computed numerically. We discovered that finding minor phases with this method required a good reproduction of the background, as just the differences between experimental and calculated data points were used. The derivative differences method (DDM;  Solovyov, 2004) , on the other hand, does not rely on a good fit of the background as it minimizes first and second derivatives instead. So in FPSM, we implemented, in addition to the classical Rietveld method, the refinement indices and weighting schemes of DDM as reported in the aforementioned paper and manual (https://sites.
google. com/site/ddmsuite/). In the search and match one can choose the Rietveld scheme, the DDM, or both Rietveld and DDM with a weight between the two. In this first implementation and testing, all the structures imported into our database were extracted from the COD  (Graz ˇulis et al. , 2009 (Graz ˇulis et al. , , 2012) )  in CIF format  (Hall et al. , 1991). In principle, we can use any other source providing crystallographic structures in CIF format, but for testing and our first database we used the COD because it contains the American Mineralogist Crystal Structure Database  (Downs & Hall-Wallace, 2003)  and the zeolites database  (Baerlocher et al. , 2007)  and it is the largest one comprising inorganics, organics and organometallics all together for which CIFs are freely available.
A dedicated interface has been written to perform the following tasks: (1) Input all the CIFs from a local repository (at present, we use a clone of the COD, and we plan to implement later a function for updating the database when structures are added to the COD). (2) Save the database in MySQL format and carry out all the maintenance or editing required. (3) Check each structure for consistency: cell content with respect to chemical formula, space group and symmetry operations. At the present state of implementation we do not make any check for bond distances. (4) Classify the phases as inorganic, organic or, when no clear distinction can be made from the raw chemical formula, unknown (organometallic mostly). In the latest version of the database imported from the COD, 379 097 structures were loaded, 148 were removed because of retraction  (Harrison et al.
However, we could successfully identify, validate and use more than 99. 2% of the structures from the COD. The core of FPSM is devoted to the full-profile searchmatch only, and the implementation was based on the layout shown in Fig. 1. We chose to keep it independent of any graphical user interface for ease of compilation and to allow it to run on multiple platforms. This core part can be compiled and used as a library by any graphical interface and as such was included in the previously described program interface to build the database and test the algorithm. But to permit more widespread use, we have built an interface accessible through a web page. The web interface is shown in Fig. 2. It has a minimal input to keep it simple and is focused mainly on usability. It has three sections for the input.
In the first section the user loads the pattern to be analysed as a simple text format with two columns: 2 (or d spacing in the case of TOF measurements) and intensity. After loading the pattern the user may restrict the database to some subsets (e. g. mineral only or organic only) and specify a possible composition, and if a list of chemical elements is entered the program will exclude all the phases having at least one atom not in the list. This may be helpful to restrict the number of phases to search from and it is strongly advised to use it. In case of doubt many additional atoms may be included to lower the restrictions. Then three thresholds can be set by the web interface: the density threshold, the minimum amount of volume fraction ( f S1 ) and the maximum number of phases. It is possible to specify if the sample contains mainly highly crystallized phases ('Crystallization: high'), normal crystallization or nanocrystallized phases.
This option will change the initial value used for the crystallites and tune the FoM differently to favour the type of phases selected. A final option is to refine only a volume expansion/contraction instead or to refine all cell parameters when the box is unchecked. In the second section the user may specify the instrument geometry and details used in the measurement. Three different radiations are available: X-ray, neutron and electron. For X-rays, the user can select one of the conventional tubes Web interface to test the algorithm using the COD database of crystal structures, as available at http://fpsm. radiographema. com. The user uploads a pattern in a double-column format, selects the instrument type and experimental conditions, restricts the search using composition and/or COD subsets, and finally runs the analysis.
For nanocrystallized samples, as the broadening is much greater than the instrument broadening (unless TEM is used), an error on the instrument broadening choice will not cause a large error in the sizestrain parameters at the end of the analysis. In the latest version of the program MAUD (available as a free download at http://maud. radiographema. com), we added the possibility to export a specific datafile in CIF format containing all the instrumental information needed by FPSM. When such a file is loaded as a pattern via the web interface, FPSM will ignore the web input from the 'Experiment details' section and the instrumental characteristics and broadening from the CIF will be used instead. In the third section the algorithm options are available: the user can choose either only the Rietveld or a mixed DDM-Rietveld refinement at each step.
When the input is finished in the three sections, pressing the 'Search and quantify' button will start the computation by submitting the job to a remote server. The server will select from the database the structures to be used and launch the FPSM algorithm. Another web page will appear (as will be shown later in Fig. 3  and Table  1 ), presenting a table giving the phases found, their percentage, and their crystallite sizes and microstrains, together with a graph illustrating the fitting result for visual evaluation.
For this sample the analysis correctly identified all three phases both using no composition restriction and with restriction. The only difference between the two was the time taken to complete the analysis. On the workstation the computation time was 565 s without compositional restrictions but using the inorganic and minerals subsets of the COD, with a total of 174 064 phases. When restricting the composition to only phases containing the following atoms -Al, Ca, F, Zn, O, Mg, Na, Si, Cl -the computation time dropped to 19 s. If we specify only the atoms really present in the sample (Al, Ca, Zn, O, F) the total time falls below 10 s, most of which is consumed by loading the database and filtering out the structures to use for the search. We could estimate from this Results obtained after less than 10 s using CPD datafile 1h, with the composition shown in Fig.