dataesr/scientific-paragraphs-categorization huggingface dataset:
Technical Validation The checklist was built and formatted directly from the Guinea Collections Database, avoiding transcription errors. Where three or fewer collections exist for a taxon, the specimens have been examined by taxonomic experts to validate the identifications. Our Guinea Names Backbone Database was constructed based on a download of the African Plant Database 15/01/2019. Data from POWO for taxa in the TDWG Guinea area were downloaded and matched to the APD data. Where the two sources did not match, the experts' opinions were used to select an accepted name. Further names from recent literature have been incorporated (See under Names Data Acquisition above). In all cases, the opinions of the checklist authors have been followed. All names were automatically checked against our Guinea Names Backbone Database.
This process resolved all names to the accepted name for the checklist. Any synonyms found to be current on specimens were automatically included in the synonyms list. Authors and basionyms were copied from the Guinea Names Backbone Database, again avoiding transcription errors. Where homonyms exist (identical binomials or trinomials intended to refer to different entities), the collection and geographic range were checked to verify the correct author string. The species epithet which is combined with the genus name to make a binomial name for a species infraSpecificEpithet The infraspecific epithet which is combined with a binomial to make a trinomial name at infraspecific rank, most commonly a subspecies or variety scientificName Concatenation of genus with species and, where applicable, infraspecific epithets to make a binomial or trinomial name scientificNameAuthorship The author or authors responsible for publication of the scientific name taxonRank The level in the taxonomic hierarchy where the taxon name fits taxonomicStatus Indication of taxonomic opinion re the name: accepted, synonym or unplaced acceptedNameUsageID Taxon number (taxonID) of the accepted name for a synonym (found in this same file).
parentNameUsageID taxonID of the species record for varieties and subspecies. Table  2. Content of Taxon core data download available from GBIF. Field names (which appear as column headers in the download) and explanatory notes on their content. Collections were automatically assigned to geographic region based on their geographic coordinates available. All collections bearing geolocation data beyond Guinea's borders were checked and either their coordinates were corrected, or the collections were excluded from the checklist.
Usage Notes Like almost any resource documenting a tropical flora, the CVPRG will be out of date almost as soon as it is published. In fact, publication of a checklist has been shown to stimulate publication of further research on the flora documented. In Guinea, new data on the flora can be expected to include: descriptions of species new to science; records of species already known to science but recorded for the first time in Guinea; new perspectives on the endemism of species currently known only from Guinea; assessments of the extinction risk of species occurring in Guinea; reports on cultural and economic uses of Guinea plants. Depending on their reasons for consulting the CVPRG, potential users may also wish to consult additional resources to obtain as complete a picture as possible on one or more of the above aspects for the subset of species of interest to them.
These newly described species will also appear in APD and POWO eventually. Table  4. Content of Taxon Species Distribution data download available from GBIF. Field names (which appear as field headers in the download) and explanatory notes on their content. Species newly recorded as present in Guinea. The majority of the growth in numbers of indigenous species known from Guinea arises from extensions to the known range of species already known from elsewhere in Africa. The specimens evidencing such changes to known ranges may be sought in aggregators such as GBIF or collection lists such as Tropicos. Such range extensions will be reflected in APD and POWO eventually. Corrections to the endemism status reported for Guinea plants. Corrections to the endemism status reported for Guinea plants may be sought in aggregators such as GBIF or collection catalogues such as Tropicos.
A more fully informed view on the endemism status of many species must await completion of digitisation of the African collections at Kew and completion of digitisation of label data from MNHN (P). Assessments of the extinction risk of Guinea plants. Data on the extinction risk of many Guinea vascular plant species can be found on the IUCN Red List of Threatened Species (IUCN. 2022. The IUCN Red List of Threatened Species. Version 2022-1. https://www. iucnredlist. org), to which new assessments are added two or three times per year. Assessment efforts are ongoing, most recently within the framework of the Guinea Tropical Important Plant Areas programme. overview and site-specific data. Data on any or all of the above topics may also be found in the data sheets which support recognition and delineation of Tropical Important Plant Areas in Guinea, available via the Tropical Important Plant Areas Explorer (Royal Botanic Gardens, Kew.
10More comprehensive synonymy data based on 31,357 synonyms derived from World Checklist of Vascular Plants (now available via POWO https://powo. science. kew. org/about-wcvp) was incorporated in the Darwin Core Archive version of the CVPRG downloadable from GBIF10. Summary of sources of collections data recorded in the Guinea Collections Database, from which the Checklist of Vascular Plants of the Republic Guinea was compiled. Herbarium codes follow Index Herbariorum (Thiers, B. M. Index Herbariorum. http://sweetgum. nybg. org/science/ih/)): P = Muséum National d'Histoire Naturelle, Paris; K = Royal Botanic Gardens, Kew; WAG = Naturalis Biodiversity Centre, Leiden; POZG = Adam Mickiewicz University, Poznan; MO = Missouri Botanical Garden; BR = Meise Botanic Garden.
Acknowledgements This work was completed thanks to the continued support by  Université Gamal Abdel Nasser de Conakry for Herbier National de Guinée. Work towards the checklist was partly funded through a grant from the  Global Biodiversity Information Facility, Biodiversity Information  for Development programme (reference number  AF2015-0042-NAC ) 'Towards a Red Data Book for Guinea' in collaboration with the  Darwin Initiative-funded Tropical Important Plant Areas project  ( Darwin  Project  23-002 ) and completed with support from  Fondation Franklinia  ( 2020-24 ). We thank all those involved in fieldwork in Guinea over the past 10 years who helped to gather data, notably  Fatoumata Fofana Madé ,  Almamy Diallo ,  Natalie Konig ,  Oliver Hooper ,  Abdoulaye Baldé ,  Albert Guilavogui & Boubacar Sow ; and  Kew volunteers Margaret Joachim & Rosemary Lomer  who georeferenced herbarium specimens for both projects.
This results in systematic modeling errors that are difficult to assess. In what follows we compare precision of the predictions of the CRPropa and ELMAG with those of the newly developed CRbeam code 1 (fist version of the code has been reported by 1 CRbeam code is available at the link https://github. com/ okolo/mcray/tree/main/src/app/crbeam  Berezinsky & Kalashev (2016) ). The modular structure of the CRbeam and CRPropa allows all relevant processes to be tested independently of each other. Namely, we consider the Breit-Wheeler pair production and inverse Compton scattering on the EBL and CMB. For each interaction, we compare interaction rate and energy distribution of secondary particles inferred from simulations with the theoretical predictions. Also, disabling all interactions makes it possible to compare the propagation of electrons in a magnetic field.
Acknowledgements. Work of A. K. was supported in part by  Russian Science Foundation  grant  20-42-09010. A. K. 's stay in the APC laboratory was provided by the "Vernadsky" scholarship of the French embassy in Russia. The work of D. S. and A. N. has been supported in part by the  French National Research Agency (ANR)  grant  ANR-19-CE31-0020.
Acknowledgements We thank the  Max Planck Society , the  German Federal Ministry of Education and Research (BMBF , grant number  13XP5114 ), the  Deutsche Forschungsgemeinschaft  ( DFG ,  German Research Foundation - SFB 1449-431232613 ; subproject  C2  and  TP701 ), and the  European Research Council (ERC)  under the  Horizon Europe research and innovation programme  (Project  101075357-GLYCOFOLD ) for generous financial support. JHL and  YO  acknowledge  Agence Nationale de la Recherche  (ANR grant number:  ANR-21-CE29-0016-1 ) and  Glyco@Alps  ( ANR-15-IDEX-02 ) for the financial support and the  NanoBio-ICMG  platform ( FR 2607 ) for granting access to the electron microscopy facility. Open Access funding enabled and organized by  Projekt DEAL.
Accepted Article This article is protected by copyright. All rights reserved. manuscript submitted to JGR: Planets to achieve a good mapping of the atmospheric thermal state, and the analysis of these data is the primary goal of this work. This work, together with other three companion manuscripts in this special issue, represent the first effort carried out in the IAA/CSIC within our scientific plan for the exploitation of both NOMAD and ACS datasets with a common analysis and retrieval strategy. This consists of using an state-of-the-art retrieval scheme, described below, to both instruments. Here we report its application to NOMAD solar occultation data, focused on the first year of the TGO science phase, from April 2018 to March 2019, which covers approximately two Mars seasons. These range from solar longitudes Ls 160 • to Ls 360 • , i.
The structure of the present work continues this Introduction with a section 2 devoted to describe some relevant characteristics of the NOMAD/SO channel and the subset of measurements used in this work. Section 3 details the methodology and pipeline we used for these data analysis, pre-processing and inversion. Section 4 presents the 3-D distribution of temperatures and densities obtained, and describe them to gain insight into atmospheric variability. Comparisons with previous works and with global climate model simulations are also presented there. Finally, Section 5 presents a summary of the major results and plans to improve and extend this work to more NOMAD data, and also to ACS solar occultation data. In addition to this work, other two manuscripts studying the thermal structure in solar occultation data from the TGO/NOMAD and ACS instruments have been submitted to this issue.
In the first one,  Trompet et al. (2022)  analyzed the NOMAD/SO channel's diffraction order 148 during Mars Years 35 and 36, excluding MY34 and focusing on mesospheric altitudes. The second presents profiles up to thermospheric altitudes combining two diffraction orders of the ACS/MIR channel, in a study covering two full Martian years which is an extension of  Belyaev et al. (2021). Each one of these works uses different inversion techniques and approximations, obtaining different retrieval errors and vertical resolutions. Our plan is to extend the present work to a larger dataset in the near future in order to perform a proper comparison with those two investigations.
Dataset NOMAD science operations began in April 2018, once the TGO spacecraft reached its final quasi-circular orbit, at about 400 km above the Martian surface, after a long aerobraking phase and a comissioning phase. In this work we selected data during the first year of routine operations in its science phase, from April 2018 until March 2019, which approximately covers the 2nd half of MY34: solar longitudes L s = 160 • -355 •. For the retrievals of temperature and density in this work we selected diffraction order 149 (3349-3375 cm -1 ) which was very frequently used during this first year of observations and contains CO 2 rovibrational lines of intermediate strength (see Figure  1 ). As we show below in section 4, this order permits a good sounding at tangent altitudes up to about 90 km.
Data analysis The NOMAD/SO data used in this work are Level 1A calibrated transmittance from the latest pipeline processing at the Belgian Institute for Space Aeronomy (IASB-BIRA, NOMAD's PI team)  (Thomas et al. , 2021). The inversion of temperature and density presented here is part of a broader effort at IAA/CSIC devoted to the scientific exploitation of NOMAD and ACS measurements with a common data analysis scheme. We followed a sequential retrieval strategy, starting with the inversion of atmospheric opacity from the continuum in the solar occultation data, then the temperature and density, and these are used later in the inversion of trace species (see the two companion papers on H 2 O and CO by  Brines et al. (2022)  and by  Modak et al. (2022) , respectively). At the core of the inversion scheme is a line-by-line radiative transfer Forward Model (KOPRA, see for example  Stiller (2000) ) and an inversion control program (or RCP in short, see von  Clarmann et al.
Data cleaning and pre-processing The pre-processing phase of the data analysis at IAA is schematically shown in Figure  2  and is devoted to correct for remaining imprecisions still present in the calibrated transmittances. These features include spectral shifts and residual spectral bending across the diffaction orders which can be quite significant in some spectra. These problems are common to all diffraction orders but different for each one of them and variable from scan to scan, as they depend on temperature effects on the detector, on TGO orbital conditions which in turn impact the space signals, and on drifts through time in the behavior of the grating's blaze response  (Liuzzi et al. , 2018; Thomas et al. , 2021). These effects are shown for one specific scan in Figure  3. Other effects, like the precise response of the AOTF and the NOMAD/SO line shape (ILS) also require analysis at this pre-processing level, as well as during the inversion of scientific data.
This last retrieval product, internally called "Line-Of-Sight in the altitude", was originally intended to account for possible biases in satellite pointing. In our case this represents deviations from the nominal altitude registration which improve the best fit. For the radiative tranfer calcultions the atmosphere is divided into 1-km wide layers from the surface up to 60 km, 2. 0 km width layers from 60 to 90 km, 2. 5 km-wide layers at 90-130 km and 5 km wide layers above. The output retrieved vectors also share this grid, which is common to all the IAA retrievals using NOMAD data and presented in this special issue. Outside the SO measurement altitude range, we impose a strong regularization constraint, to give a large weight to the a priori information (to its vertical gradient, which will be imposed on the data).
Accepted Article This article is protected by copyright. All rights reserved. manuscript submitted to JGR: Planets 80 km is used, for statibility reasons, which effectively assumes the volume mixing ratio (VMR) in the a priori value is correct. It is well known that the CO 2 VMR is close to one up to the homopause, surely above the altitude range studied here. However, the CO 2 absolute density will be variable and determined from the NOMAD data via the hydrostatic inversion mentioned above, consistently with the thermal structure. At low altitudes we avoid large dust contamination by filtering observations where the aerosol opacity is greater than a given value. For order 149 we use a maximum slant opacity of 2. 0. Also we avoid steep gradients in the aerosol slant opacity, larger than 0.
The vertical resolution, given by the width of the Averaging Kernel rows, also vary significantly with tangent altitude, between 3 km in the lowermost tangent altitude up to 10 km at the top range in this scan. The best-fit spectra at the end of the inversion usually reproduces the NOMAD data very well. Figure  7  shows the best fit obtained for this scan at about 65 km altitude, where the retrieved temperature significantly differs from the a priori. Sometimes high altitude spectra go slightly above Transmittance=1; these defects in the data are accounted for during the inversion by a simultaneous retrieval of the continuum at each altitude, which also includes a small shift in the continuum, as required for matching the data. To illustrate the robustness of the inversion against changes in the a priori, we show in Figure  6.
Global envelope of atmospheric temperatures Figure  8  shows all the temperature profiles retrieved in this work, put together in a single panel, with a similar plot for the LMD GCM profiles (our a priori). The dashed lines in blue and orange represent the envelope of all possible temperatures anywhere and anytime on Mars, taken from  Smith et al. (2017) , who compiled data from instruments Mars Climate Sounder and Thermal Emission Spectrometer, as well as from the Mars Climate Database simulations for extreme conditions. The range of temperatures observed by NOMAD are quite similar to the GCM, although generally colder in the mesosphere. The global difference between 0. 01 and 0. 001 mb pressure levels (about 60-80 km altitude) seems to be over 10 K. In addition, and although possibly difficult to see in this Figure, the NOMAD temperatures seem to be wavier than in the GCM, and in particular we obtained excursions to very low temperatures at altitudes above 0.
Corrections for the reference pressure During the temperature and density retrieval, our inversion processor finds whether a change in the line-of-sight pointing, or in other words, in the tangent altitudes, would produce a better model-data fit than with the nominal pointing. Usually the correction found is small but without this correction the temperature profiles would be incorrectly positioned. The variation, expressed in km at the tangent point, is shown in Figure  11. Some profiles should be moved up and some down, with a globally averaged negative value around 2 km. Figure  11  also shows the absolute value of this correction. The interpretation of this result is related to the assumed pressure reference for the hydrostatic adjustment. Pressure values from the GCM are taken at a given altitude, and -10-.
This agrees with the expected global description of the atmospheric temperatures in equinox conditions (seen in the bottom panels of Figure  12 ), in spite of the difference data-model (NOMAD temperature being generally colder than GCM at mesospheric altitudes). These warm layer is not seen in Ls Box #2 (right panels). Another result which agree with the GCM simulations is the warm layer at the bottom of the observed altitudes in Ls Box #2 (right panels). These layers seem to correspond to the warming at high latitudes of the SH, typical of the Southern Summer solstice. These warm layers are absent in both model and data in the Ls Box #1 (left panels). The latitudinal variations in the NOMAD temperatures seem to agree in these two periods with typical equinox and solstice conditions as simulated by the LMD-GCM.
Local time variations Inspection of the orbit in Figures 1 or 8 reveals a couple of periods with a significant variation in local time in a short Ls period and small variations in latitude as well. These two periods are analyzed here in the search for possible variations with local time. Period "CT Box1" covers latitudes 20 • -50 • in the NH during the bracket Ls 230 • -250 • , and period "CT Box2" covers latitudes 30 • N-60 • N and Ls 290 • -310 •. We grouped all the scans inside these boxes splitting them by their local time only, and are shown in Figure  13. The figure also shows the LMD GCM profiles for the same scans. In CT Box1 there are no significant differences between the morning and evening results, neither in the NOMAD data nor in the model, although the NOMAD data shows a colder layer at 60 km altitude within a more wavy vertical profile.
CT Box2 shows also a more wavy profile in NOMAD than in the model, with a possibly upward propagating wave with large amplitudes, around 30 K in the mesosphere, which is much weaker in the GCM. This oscillation seems to be smaller in the NOMAD evening profiles, although there are not many profiles to examine and one of them also presents a large oscillation. The warm layer at 80 km in the NOMAD morning data could be related to the recent finding of a warm layer at precisely that altitude at nighttime in stellar occultation data from IUVS/Maven  (Nakagawa et al. , 2020). A similar warm layer was reported also by  (Belyaev et al. , 2021)  using ACS solar occultation data in the ACS/MIR channel. The data used by  (Belyaev et al. , 2021).
Latitudinal-Seasonal slices at fixed altitudes Figure  14  shows the structure of the NOMAD temperatures in latitude -solar longitude cross sections at three altitudes, 30, 50 and 80 km, compared with the a priori GCM temperatures. Again we observe a NOMAD thermal field colder than the GCM at mesospheric altitudes and a better agreement in the troposphere. In this figure, and in spite of variability and differences, we observe that the 3-D map of NOMAD temperatures globally agree with the global thermal distribution simulated by the GCM for MY34. We observe a warm troposphere at 30 km at all latitudes during the GDS with a gradual cooling with time, in both data and model (left panels). At 50 km the warm layers are located at mid latitudes in both model and data but NOMAD does not see the warm branch at 60 • N that the model simulated for the rest of MY34.
The altitude range sampled by both instruments is very similar in all the profiles. At the top of the altitude range, at and above 90 km, the ACS/NIR retrieval errors increase a lot and also in our NOMAD/SO data the a priori effect starts to be significant. At the bottom of the profiles, both retrievals are limited by the aerosol absorption along the LOS, in approximately the same manner. Some of the features observed in the thermal profiles indicate that the vertical resolution in both instruments is also similar. The number of coincidences between NOMAD/SO and ACS/NIR increases significantly when the criterion for geographical collocation is relaxed. In particular, with a relaxation in longitude to ∆LON < 30 • we allow for profiles from adjacent orbits and then we found 95 coincidences. All these, which include the previous 6 closest ones, are shown in the Appendix, using a similar format to Figure  15.
It would be interesting to extend the retrievals of both instruments to a couple of Mars years, in search for more significant deviations from the GCM climatological predictions. Figure  17  shows a selection of another dozen profiles but in this case with clear differences between NOMAD/SO and ACS/NIR. As in the case of Figure  16 , the separation between the "coincident" profiles is about 30 • , which as said above, may play a role and partly explain some of the differences. The discrepancies are particularly large at mesospheric altitudes, and in general the NOMAD/SO profiles seem to be colder than the ACS/NIR at those altitudes. Again, it will be useful to explore more differences with ACS/NIR in the future, when both instruments' retrieval schemes are applied to a more extended dataset including a couple of full Martian years.