dataesr/scientific-paragraphs-categorization huggingface dataset:
3 T and the saturation magnetization (departure from the saturation magnetization by 53-100% along the series of compounds). Our experimental results clearly show that the intensity of the TM K-edge XMCD signal does not depend on these deviations (such deviations are too big to be included in the experimental error). If this were the case, the experimental intensity of the XMCD signal would not be proportional to the spin of the absorbing atom with the same proportional coefficient (1 × P exp or (1/3) × P exp ) for all compounds and all edges, which is nevertheless experimentally observed. This means that at least for our molecular materials, the intensities of the TM K-edge XMCD signal and SQUID magnetometry data do not give access to the same component of the magnetic moment. Regarding the macroscopic magnetization measured by SQUID magnetometry at 1.
We started a pilot program to extend the observations of the molecular complexity in the earliest phases of star-forming regions towards a sample of three prestellar core candidates selected from the catalogue in  Montillaud et al. (2015). These nearby cores are located in diverse Galactic environments and are presented in Sect. 2. The survey, performed at the IRAM 30 m, is presented in Sect. 3 together with the data reduction method. The results are presented in Sect. 4, along with the method, including line identification. This led to the determination of the molecular complexity in all three cores, as well as the local thermodynamic equilibrium (LTE) versus non-LTE analysis we used to determine the column densities for each species. Section 5 finally presents the evolutionary stages of the cores, a comparison with the prototypical cores observed previously, and references in the literature.
The data were reduced using the IRAM CLASS/GILDAS package 1. The telescope and receiver parameters (main-beam efficiency, half -power beam width, and forward efficiency) were adopted from the IRAM website 2  : at 86 GHz, η mb = 81%, η ff = 95%, θ = 28. 6 , and at 115 GHz, η mb = 78%, η ff = 94%, θ = 21. 4. The frequency-switching technique was used to optimise the telescope time, but unfortunately, it results in poor baselines over the sub-bands. In order to obtain flat baselines, we first cropped the spectrum into spectra with a smaller frequency range (∼5 MHz) adapted to avoid molecular transitions located at the edges. Then, we made masks for the identified lines where the intensities are brighter than four times the rms and fitted the baseline with polynomials of order between 3 and 5.
Line identification The line identification was performed using the CASSIS 3  software, which connects to the JPL and CDMS databases. This is a standalone software, written in Java, and is freely delivered to the community to help with visualising, analysing, and modelling observations from ground-or space-based observatories. It has been developed at IRAP since 2005 and is part of the OVGSO 4  data centre, which aims at promoting the Virtual Observatory technology. CASSIS displays any spectra (ASCII, FITS or GILDAS/CLASS format or the result from the query to any SSAP VO or EPN-TAP VO service from IVOA registries) and identifies atomic and molecular species through its link to the databases, such as CDMS  (Müller et al. 2005)  or JPL  (Pickett et al. 1998) , via a SQLite database, or via a direct access to VAMDC 5  for any available spectroscopic database.
The cosmic-ray ionisation rate ζ CR of L1544 was obtained by  Bovino et al. (2020) , simulating the chemical evolution with a collapsing core, and comparing their results with the observations of H 2 D +  (Vastel et al. 2006a). 4149 is comparable to L1544 in terms of the cosmic-ray ionisation rate and molecular elling was performed, and they used the NAUTILUS chemical code updated with the most recent chemical network for sulphur to explain their observations and therefore constrain the sulphur elemental abundance to 0. 5% of the cosmic value of 1. 5 × 10 -5. The sulphur elemental abundance in the gas phase is therefore much higher in our three cores, with values in the [0. 2-7] × 10 -6 range, although constrained on HCS + only (see Sect. 5. 2). We also have to keep in mind that the analysis performed for L1544 took the density profile into account to compare with the abundances computed by the chemical model.
Acknowledgements. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 811312  for the Project " Astro-Chemical Origins " (ACO). This work is based on observations carried out under projects numbers  008-18  and  119-18  with the IRAM 30m telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). Charlotte Vastel would like to acknowledge the  IRAM  staff for their help before and during the observations.
Finally, the remaining data were obtained using the "Analyze Results" tool. Patents filed between 2010 and 2021 in Brazil were obtained on the website of the National Institute of Industrial Property (INPI) (Instituto Nacional da Propriedade Industrial 2021) from a basic search for the terms in Portuguese "material de carbono", "materiais de carbono", "carvão ativado", "grafite", "grafeno", "fulereno" and "nanotubos de carbono" in the title field and containing all the words. Data were selected and RCT V. 8 (2022) _____________________________________________________ ISSN 2447-7028 processed through individual access to the files of each patent. Data on the gross domestic product (GDP) by region were obtained from the Brazilian Institute of Geography and Statistics (IBGE) website. All figures were constructed from the data obtained in the search performed.
The δ 15 N data 427 indicate gradual nutrient limitation of phytoplankton growth due to enhance A c c e p t e d M a n u s c r i p t Manuscript submitted to Environmental Research Letters 15 / 28 461 Y. B. designed the study and wrote the manuscript with contribution of M. -A. S. , J. R. , B. J. , J. C. , 462 H. L. and H. J. V. K. and Z. J. contributed the biomarker analyses. L. L. retrieved the 463 environmental data from different database. Q. Z. contributed the biogenic silica and L. S. 464 performed the determination of TOC. All authors contributed to the final version of the 465 manuscript. 466 Declarations of interest: None 467 A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t.
Chemical gradient measurements for CO 2 flux measurements Lab-on-chip sensors were mounted on landers and measured the pH and alkalinity at approximately 2. 6 m south of the plume  [20]. Each sensor had two intakes, at two heights above the seafloor (17 cm, cm), and alternated measurements between these to characterize the time-dependent vertical gradient of each parameter. The current magnitude and direction were each measured at two heights: at 16 cm, with the current meter on the eddy covariance system on the same lander, and at 1. 2 m with a current meter on the on another lander, m away. The total excess dissolved organic carbon (δDIC) contained within the plume was estimated from this data by taking advantage of the fact that the ellipsoidal currents caused the entire plume to sweep over the sensors once per tidal cycle.
By summing the δDIC(t) over a tidal cycle we estimated the total δDIC of the plume during that cycle. To achieve this, pH and alkalinity data from the sensors were converted to DIC using the CO2SYS software  [21, 22]  (using the time-averaged alkalinity value of TA = 2311 μmol/kg and with nutrient concentrations, temperature T = 7. 74 • C, and salinity S = 35. 25 as measured in the area). The two points of δDIC(t) at the lander were used to parameterize a full vertical δDIC(z,t) distribution based on a model of the bubble dissolution at the source. The vertical current profile u(z,t) was estimated with the current measurements and a log-law velocity profile. The total mass flow rate Q of the CO 2 into the water column at each point in time was estimated by Q(t) = ∫ openwater seafloor δDIC(z, t) * u(z, t) * w(t)dz where w(t) the physical east-west width of the plume that each measurement represents, z water depth.
Acoustics To determine if it was possible to image gas in the sediments above the release site we used a high resolution seismic reflection data collected using a chirp profiler (14-21 kHz) integrated on a GAVIA Autonomous Underwater Vehicle (AUV) system. The AUV-chirp data was collected at either 2 or 7. 5 m elevations above the seabed, with a line separation of 2-5 m. The data has a vertical resolution of 2 cm and a horizontal resolution at a depth of 3 m (i. e. the release point) of cm-100 cm. To demonstrate the use of acoustic system for the flux of the gas from the sediments an array of 5 calibrated hydrophones was deployed 3. 3 m (Fig. 1 ) away from the locus of injection to record the acoustic signature of gaseous CO2 bubbles emitted from the seabed into the water column and calculate CO 2 fluxes using inversion methods  [25].
There is inflation of reflectors due to the presence of gas, and the generation of vertically aligned reflectivity which are interpreted as fluid flow conduits to the seabed (chimneys). The chirp profiler data was collected from an autonomous underwater vehicle at an elevation of 2 m. The profile position is shown in Fig. 1. system (co-located 2. 6 m from the centre of the bubble streams) detected significantly increased vertical fluxes in hydrogen ions, produced by CO 2 (g) dissolution near the seafloor (Fig. 4 ). The eddy covariance technique was highly sensitive and specific: even at the lowest release rate (6 kg day -1 ), the peak signal was 20x that of natural background CO 2 production  [31]. Sediment temperature sensors were also effective for leak detection. Dissolution of CO 2 gas in sediments reduces pore water pH, triggering the dissolution of carbonate and silicate minerals.
Acknowledgements Funding was provided by the  European Unions Horizon 2020 research and innovation programme  under the grant agreement number  654462  ( STEMM-CCS ). We are grateful to the  Captains  and crews of the  RRS James Cook  and  RV Poseidon  for enabling the scientific measurements at sea during the  JC180  and  POS518 ,  POS527 ,  POS534  and  MSM63  and  MSM68  cruises. Mark Wells  and the team at  Cellular Robotics  are thanked for the development of the seabed drilling system. We thank the support team at the  National Marine Facilities at the NOC in Southampton  for their unstinting help in the planning of the complex cruise programme.
Data and materials availability All data from the shipboard operations are available in Pangaea or the BODC data repositories. Methane and CO 2 data, POS527: https://doi. org/10. 1594/PANGA EA. 910413. ADCP data, POS534: https://doi. org/10. 1594/PANGAEA. 908919. Lander ADCP data and biogeochemical data POS518 and JC180: https://doi. org/10. 1594/PANGAEA. 908935, https://doi. org/10. 1594/PANGAEA. 909624. Physical oceanography data POS534: https://doi. org/10. 1594/PA NGAEA. 910583. Cruise report and cruise track. JC180: https://www. bodc. ac. uk/resources/inventories/cruise_inve ntory/report/17236/.
ovale and P. malariae. Our project is funded by the Medical Research Council in the United Kingdom, which funds our research collaboration and contributed to improved research infrastructure through the building of a permanent insectary containing clean laboratory space and reliable power supply next to the clinic in Faladie. Furthermore, we also received generous support from 10X Genomics towards the installation of a Chromium 10X controller in Bamako-the first in West Africa-enabling in-country single cell studies. These long term investments will facilitate high quality research that we and other scientists, both local and foreign, undertake in Mali. Our study is generating single-cell transcriptional atlases of the three main malaria species from infected individuals. Outputs from our endeavour will be rapidly added to the Malaria Cell Atlas website, which already serves as a valuable data resource for the malaria research community but currently focuses on cultured lab strains.
Our current study involves shipping processed cDNA samples to the Sanger Institute for sequencing, adding costs associated with shipment as well as sample transportation risk. We envision a prospect where local sequencing capacity will be bolstered to support the processing of samples from blood draw to library preparation to sequencing to quality control and data analysis. There is a sequencing facility at MRTC in Bamako, with access to an Illumina MiSeq instrument, a SeqStudio instrument, and the 10X Chromium controller. A server with 140 terabyte storage capacity is available, dedicated to bioinformatics analyses. Local sequencing capacity is still in its infancy in Mali, and massive investments are needed to expand these capabilities. This is challenged by the requirement of expertise in using these sophisticated instruments, which is compounded by the high cost of their maintenance and consumable supply chain issues.
Mid-Infrared reflectance spectroscopy (MIRS, 4000-400 cm -1 ) is being considered to provide accurate estimations of soil inorganic carbon (SIC) contents, based on prediction models when the test dataset is well represented by the calibration set, with similar SIC range and distribution and pedological context. This work addresses the case where the test dataset, here originating from France, is poorly represented by the calibration set, here originating from Tunisia, with different SIC distributions and pedological contexts. It aimed to demonstrate the usefulness of 1) classifying test samples according to SIC level based on the height of the carbonate absorbance peak at 2510 cm -1 , and then 2) selecting a suitable prediction model according to SIC level. Two regression methods were tested: Linear Regression using the height of the carbonate peak at 2510 cm -1 , called Peak-LR model; and Partial Least Squares Regression using the entire MIR spectrum, called Full-PLSR model.
First, our results showed that Full-PLSR was 1) more accurate than Peak-LR on the Tunisian validation set (R 2 val = 0. 99 vs. 0. 86 and RMSE val = 3. 0 vs. 9. 7 g kg -1 , respectively), but 2) less accurate than Peak-LR when applied on the French dataset (R 2 test = 0. 70 vs. 0. 91 and RMSE test = 13. 7 vs. 4. 9 g kg -1 , respectively). Secondly, on the French dataset, predictions on SIC-poor samples tended to be more accurate using Peak-LR, while predictions on SIC-rich samples tended to be more accurate using Full-PLSR. Thirdly, the height of the carbonate absorbance peak at 2510 cm -1 might be used to discriminate SIC-poor and SIC-rich test samples (<5 vs. > 5 g kg -1 ): when this height was > 0, Full-PLSR was applied; otherwise Peak-LR was applied. Coupling Peak-LR and Full-PLSR models depending on the carbonate peak yielded the best predictions on the French dataset (R 2 test = 0.
g. ,  Gogé et al. , 2012; Nocita et al. , 2014; Gomez et al. , 2020) , or spiking, which consist to enrich the calibration set with some representative soil samples from the prediction set (e. g. ,  Guerrero et al. , 2010; Barthès et al. , 2020). A discrimination of soil types before applying a regression model for SOC prediction has been proposed by  Liu et al. (2018). Liu et al. (2018)  discriminated five soil types through partial least squares discriminant analysis (PLS-DA) and then calibrated a PLSR model using (I) the entire dataset or (II) a subset by soil type. In these previous studies, the test dataset was well represented by the calibration set, with similar soil properties range and distribution and the number of calibration samples was always much larger than the one of the test samples (e.
9 g kg -1 , averaged 6. 4 g kg -1 , had a median of 0 g kg -1 and a skewness value close to 3. 1 (Table  1 ). Moreover, SIC distributions of both datasets were different (Fig. 1A  and B ): the SIC values of DB_RMQS followed a non-normal distribution, due to a very large number of null values (Fig. 1A ), whereas the SIC values of DB_Tunisia followed a bimodal distribution with modes at 0 and 55 g kg -1 , respectively (Fig. 1B ).
Mid-infrared spectroscopy The Mid-infrared (MIR) spectra were acquired following the same procedure on both soil datasets, DB_Tunisia and DB_RMQS, using a Fourier transform Nicolet 6700 spectrophotometer (Thermo Fischer Scientific, Madison, WI, US). The spectrophotometer is equipped with a silicon carbide source, a Michelson interferometer as a dispersive element, and a deuterated triglycine sulfate detector. The soil samples were air-dried, sieved to 2-mm, then 0. 2-mm ground aliquots were ovendried at 40 • C for twelve hours and then placed in a 17-well plate. The soil samples surface was flattened with the flat section of a glass cylinder, and each sample was then scanned using an auto-sampler (soil surface area scanned: ca. 10 mm 2 ). Each spectrum resulted from 32 coadded scans, and the body of the plate (next to the wells) was used as a reference standard and scanned once per plate (i.
Methods The Tunisian dataset (96 samples) was used to calibrate and validate SIC prediction models, and the French dataset (2178 samples) was used to test them. The large SIC range of the Tunisian dataset represented reasonable conditions for calibrating prediction models. The larger size and diversity of the French dataset provided great opportunity to test strategies for optimizing SIC predictions when SIC distribution differs between calibration and test sets. All procedures were performed using R software (R Development Core Team, 2012), and both the ade4  (Dray and Dufour, 2007)  and pls packages  (Mevik and Wehrens, 2007)  were used. Fig. 2  illustrates the process flow.
Dataset preparation for regression models The DB_Tunisia dataset was divided into a calibration set (3/4 of the dataset) and a validation set (1/4 of the dataset; Fig. 2a ) according to the following procedure. The samples were ranked according to ascending observed SIC. The sample with the lowest SIC was put in the calibration set, the next sample in the validation set, and then the next three samples in the calibration set. The procedure was continued by alternately placing the next sample in the validation set and the following three samples in the calibration set. Following this process, distributions of Tunisian calibration and validation datasets (DB_Calib_Tunisia and DB_Valid_Tunisia, respectively) were similar. A principal component analysis (PCA) was applied to the spectra of DB_Calib_Tunisia to identify and remove spectral outliers, which are defined as samples spectrally different from the rest of the samples (e.
, 1991; Reeves and Smith, 2009) , whatever the dataset (red spectra, Fig. 3 ). Conversely, soil samples with low SIC contents were characterized by an absence of peak centered at 2510 cm -1 (blue spectra, Fig. 3 ). So the higher was SIC content, the higher was this peak (Fig. 3 ). Furthermore, the coefficient of determination between observed SIC contents and Peak 2510 values was around 0. 91 for the 2718 samples of DB_RMQS and 0. 84 for the 96 soil samples of DB_Tunisia.
SIC prediction models tested on BD_RMQS database The performance of Full-PLSR model applied to DB_RMQS provided moderate accuracy, with an R 2 test of 0. 71, RMSE test of 13. 7 g kg -1 and RPD test of 1. 2 (Fig. 5C , Table  2 ). These SIC predictions were strongly biased (bias test = -10. 6 g kg -1 , Fig. 5C , Table  2 ). As expected, the RPIQ test was very low (0. 1, Table  2 ), due to non-normal SIC distribution in DB_RMQS (Fig. 1A ). When considering SIC sub-ranges, RMSE test obtained with Full-PLSR model varied strongly, from 4. 4 g kg -1 (for samples with observed SIC values between 45 and 50 g kg -1 , Table  3 ) to 14. 8 g kg -1 (for samples with observed SIC values under 5 g kg -1 , Table  3 ). The performance of Peak-LR model applied to DB_RMQS provided good accuracy with an R 2 test of 0.
Peak-LR and Full-PLSR performances on the Tunisian validation set Before being applied to the French RMQS database, the Full-PLSR and Peak-LR models were calibrated and validated on the Tunisian set. Such PLSR regression models calibrated from a region A and then validated on samples from the same region A, where soil and climate conditions could be considered similar, have been slightly developed and analyzed in the literature. The results obtained from the Full-PLSR model are in accordance with the literature (Table  2 , Fig. 5A  and B ). Barthès et al. (2016)  obtained similar performances for SIC prediction (R 2 of 0. 98 and RPD of 7. 8 in six-group cross-validation) using the same Tunisian MIRS database. McCarty et al. (2002)  obtained similar performances (R 2 val = 0. 98) using a Central United State MIRS database characterised by SIC range from 0 to 65.
Carbonate absorbance peak as a driver to discriminate SIC-poor and SIC-rich samples The SIC contents and Peak 2510 values were highly correlated for both DB_RMQS and DB_Tunisia dataset (R 2 of 0. 91 and 0. 84, respectively), as suggested by  Legodi et al. (2001) , who also reported high correlation (0. 993) between MIR carbonate peak centred at 2510 cm -1 and SIC contents (description of their carbonate peak calculation not found in  Legodi et al. , 2001). Based on this observation, the use of this MIR carbonate peak allowed to separate the SIC-poor and SIC-rich test samples, as 98. 9 % of samples with Peak 2510 ≤ 0 had an observed SIC content lower than 5 g kg -1 and 89. 9 % of samples with Peak 2510 > 0 had an observed SIC content higher than 5 g kg -1. So the Peak 2510 value could be used as a simple proxy of SIC absence/presence in soil samples, like the acid test consisting in placing a drop of dilute (5% to 10%) hydrochloric acid on a soil sample and watching for bubbles of carbon dioxide gas to be released  (Soil Survey Staff, 1993).
As the absence of bubbles of carbon dioxide evidences low SIC content, absence of a carbonate peak at 2510 cm -1 (which corresponds to Peak 2510 ≤ 0) indicated a SIC content lower than 5 g kg -1. Considering the development of large soil laboratory VNIR-SWIR spectral libraries (at national,  Knadel et al. , 2012; continental, Toth et al. , 2013 , Stevens et al. , 2013 and even global scale, Viscarra Rossel et al. , 2016) , similar condition of dissimilarity between calibration and test datasets might be met and a pre-analysis of test soil VNIR-SWIR spectra might be considered to separate SIC-poor and SIC-rich samples, using the carbonate absorption band centred at 2341 nm  (Gaffey, 1986; Gomez et al. , 2008). Finally, future works could study several parameters to optimize discrimination between SIC-rich and SIC-poor samples.
Conclusion This work highlighted that when the test set was poorly represented by the calibration set, a simple linear regression based on the height of a carbonate peak provided better SIC predictions than PLSR using the full spectrum. However, when the test set was well represented by the calibration set, PLSR provided better SIC predictions than that simple linear regression. This work also highlighted the interest of preanalyzing the test set to separate it in two classes (a-priori SIC-poor vs. SIC-rich test samples) based on the height of the carbonate absorbance peak at 2510 cm -1. Then the most suitable prediction model might be selected for each test sample, depending on the class and so on how it was represented by the calibration dataset: PLSR using the full spectrum was selected for test samples with peak height > 0, well represented by the calibration set; and simple linear regression based on carbonate peak height was selected for test samples with peak height < 0, poorly represented by the calibration set.