dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. The meteorological data are accessible from http://www. underground. com at a resolution of 3 h. Mass concentration and chemical properties of PM 2. 5 and PM 1 are available from the authors upon request (caihongxu@. mail. sdu. edu. cn). The back trajectory data from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model are available at http:// ready. arl. noaa. gov/HYSPLIT_traj. php. The raw 18S rRNA gene sequences are available at the NCBI Sequence Read Archive database under accession number SRR5146156.
After rinsing, fluorescence of the foraminifera was immediately observed with an epifluorescence stereomicroscope (Olympus SZX16, LPG-BIAF laboratory) equipped with a fluorescent light source (Olympus U-RFL-T). Foraminifera with less than 3 chambers not fluorescing (terminal chambers) were considered to be alive. The average size of all juveniles was measured before (t 0 ) and after (t 1 ) the incubation using an automatic particle analyzer (LPG-BIAF laboratory) equipped with an automated incident light microscope system; a Leica CLS100X ring light source mounted on a monocular Leica Z16PO microscope. A camera (SIS CC12) recorded images and the size of individuals was determined with the software analySIS FIVE (SIS/Olympus)  [76]. The growth rate (in % size change) was calculated as: Size t1À Size t0 Size t0 Â 100.
Ultra-thin sections observed with TEM were subsequently imaged with a NanoSIMS ion microprobe  [78]. Areas of interest for NanoSIMS imaging were selected based on TEM observations permitting direct correlation of ultrastructural (TEM) and isotopic images. Our observations systematically focused on the antepenultimate chamber of the foraminifera, i. e. the third chamber counting from the aperture. NanoSIMS imaging followed established procedures  [79] [80] [81] , as detailed in S1 Text. Regions of interest (ROIs) were drawn with the software Look@NanoSIMS  [82]  to estimate the percentage of cytoplasmic occupation and to quantify mean 13 C enrichments of different sub-cellular structures of a given foraminifera. 13 C enrichments were reported as 13 C atom fraction in %: x( 13 C) = 13 C/( 13 C+ 12 C)×100.
The 13 C atom fraction of the total organic matter, x( 13 C) TOC , of decalcified foraminifera were determined by continuous flow elemental analysis/isotope ratio mass spectrometry. For each analysis, 30 previously decalcified specimens were used. The total organic carbon (TOC) content was determined from the peak area of the major isotopes and expressed in microgram per individual cell (μg C×ind -1 ). Reproducibility and accuracy were better than ±0. 01% for x( 13 C) TOC (2 SD) and ±0. 02 μg C×ind -1 for TOC content. For each analysis, 30 specimens were used (details in S3 Text). Statistical analysis. Data were analyzed using the R software. Univariate ANOVA tests were performed to compare the effects of the time and experimental conditions (i. e. oxic vs. anoxic). To determine the significance between two time points or two conditions at the same time point, the Tukey post-hoc test was carried out following the ANOVA.
Experiment I was supported by a grant  EC2CO   N-Forlab  from the  French Centre National de la Recherche Scientifique -Institut National des Sciences de l'Univers  (www. insu. cnrs. fr)(EG). Experiment II was funded by a grant no. 200021_149333  from the  Swiss National Science Foundation  (AM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Here, we explore the main oceanic and atmospheric processes that drive complex spatial climate patterns over the Iberia Peninsula across the last 2000 years by integrating new records from the Iberian Margin, Galiza, Minho and Algarve, with published datasets (Porto and Tagus;  Abrantes et al. , 2005 Abrantes et al. , , 2011;; Lebreiro et al. , 2006).
Material and methods This study combines proxy data previously published for sedimentary sequences collected off the Tagus River (PO287-26B and -26G, D13902 and D13882 designated as Tagus;  Abrantes et al. , 2005; Rodrigues et al. , 2009)  and Douro River (PO287-6B and -6G, designated as Porto;  Abrantes et al. , 2011)  with new data from three other sites on the Iberian Margin (Table  1 , Fig. 1 ). Two of the new sites are located in the northern area, off Vigo (GeoB11033-1 referred to as Galiza) and off the Minho River mouth (DIVA09 GC, referred to as Minho in this paper), and one core is from the southern Iberian/Algarve margin (POPEI VC2B, referred to as Algarve). With the exception of the Galiza deep-sea core, all other sedimentary sequences were collected on the inner shelf in areas directly affected by river discharge.
(1997). The analytical error was 0. 5 • C. The concentration of each compound was determined using hexatriacontane as an internal standard. For the calculation of SST, we selected the globally defined calibration of  Muller et al. (1998)  (U k 37 = 0. 033 × SST -0. 044). Both alkenone-based SST and [n-alk] are widely accepted proxies for SST and river input, but to better understand our regional records, the significance of our high-resolution sediment data was assessed by comparison to NOAA daily Optimum Interpolation Sea Surface Temperature (OISST, V2 AVHRR-only) dataset (Fig. S3 ) and the river discharge dataset at Sistema Nacional de Informação de Recursos Hídricos (SNIRH). Sample preparation procedures for pollen analyses followed the methods described in  Naughton et al. (2007). Pollen and spores were counted using a Nikon light microscope at ×550 and ×1250 (oil immersion) magnification.
This technique weights high-resolution records more heavily and prevents interpolation across gaps or hiatuses from affecting the stack  (Lisiecki and Raymo, 2005). Additionally, a test of the sensitivity of the stack to the bin size and the inadequacies of the proxy data (Supplementary Material) reveals that the stack is insensitive to bin sizes (20-50 years) and independent of data deficiencies (Fig. S4 ). To focus on lower-variability periods, a similar stack was built using a 5year bin size and the Porto, Tagus and Algarve cores with a sedimentation rate > 0. 4 cm yr -1 (Fig. S3 , bottom panel). To investigate the existence of periodic signals and potential changes in their amplitude through time, we carried out a continuous time series transformation with a Morlet wavelet analysis on each dataset and stack  (Torrence and Compo, 1998)  after interpolation of the series to regular time steps.
Light gray band marks the RP; the pink band marks the MCA; the blue band marks the LIA. AMO index AMO index (j) (i) (h) (g) (f) (e) (d) (c) F. Abrantes et al. :  The climate of the Common Era off the Iberian Peninsula from the same sediments allows us to accurately evaluate the coupled ocean and land variability without chronological ambiguity. Furthermore, to better assess the regional value of U k 37 -SST and [n-alk], sediment-derived variables were compared to climatological datasets (Sects. S2 and S3 in the Supplement). Results validate the proxies and reveal that in the west coast, SST compares to winter climatological data but in the Algarve, SST mimics spring-fall (Fig. S2 ), providing the opportunity to disentangle winter from spring-fall conditions in the region.
The Supplement related to this article is available online at https://doi. org/10. 5194/cp-13-1901-2017-supplement. Author contributions. FA: PI of the various projects that funded all the data combined in this paper; she had the idea and wrote the paper. TR: responsible for the biomarker analysis in all cores. MR: statistical data analysis. FN: responsible for the pollen interpretation and age models of DIVA and POPEI. ES: data from GeoB11033-1 core. DO: pollen analysis of the DIVA core. SD: pollen analysis for the Tagus site D13882 and POPEI core. AC: age model for core GeoB11033-1. PO processed the satellite-derived and OISST data. TD provided the Algarve core (POPEI). MMH participated in the DIVA cruise. Competing interests. The authors declare that they have no conflict of interest. Edited by: David Thornalley Reviewed by: two anonymous referees.
The authors express their gratitude to the captain, crew and participants of the cruises Discovery 249, Poseidon PALEO I, RV Poseidon 342 -Galiomar and B/O Mytilus DIVA09 for their contribution during the retrieval of the various cores used in this study. Thanks are due in particular to Guilhermo F. Pedraz and the Departamento de Geociencias Marinas y Ordenación del Territorio (Universidade de Vigo) for allowing the recovery of the DIVA core on their DIVA09 cruise. Special acknowledgments are due to the anonymous referees of previous versions of this paper, who greatly contributed to its improvement. Funding was provided by the projects INGMAR (FCT ARIPIPI Program -Support for State Labs Development), HOLSMEER (EVK2-CT-2000-00060), CLIMHOL (PTDC/AAC-CLI/100157/2008), MIÑO-MINHO (0234_NATURA_MM_1_E), POPEI (PDCT/MAR/55618/2004), CALIBERIA (PTDC/MAR/102045/2008, national funds from FCT -Foundation for Science and Technology and COMPETE/FEDER-FCOMP-01-0124-FEDER-010599), CIIMAR (20132017 CIMAR) and CCMAR (Associated Lab PEstC/MAR/LA0015/2013 and UID/Multi/04326/2013).
Funding also came from fellowships to Filipa Naughton (SFRH/BPD/36615/2007), Teresa Rodrigues (SFRH/BPD/66025/2009) and Emília Salgueiro (Ref. SFRH/BPD/26525/2006 & SFRH/BPD/111433/2015). Marta Rufino was funded by contrato ciência 2007 and by a post-doctoral grant of the IPMA, within the EU project SAFI (FP7-SPACE-2013-1, grant agreement no. 607155). Finally, we thank Apolonia Inês, Daniel Ferreira, Cremilde Monteiro for their help and support with the laboratory analysis and Zuzanna Stroynowski for revision of the English.
Acknowledgements. The authors express their gratitude to the captain, crew and participants of the cruises Discovery 249, Poseidon PALEO I, RV Poseidon 342 -Galiomar and B/O Mytilus DIVA09 for their contribution during the retrieval of the various cores used in this study. Thanks are due in particular to Guilhermo F. Pedraz and the Departamento de Geociencias Marinas y Ordenación del Territorio (Universidade de Vigo) for allowing the recovery of the DIVA core on their DIVA09 cruise. Special acknowledgments are due to the anonymous referees of previous versions of this paper, who greatly contributed to its improvement. Funding was provided by the projects INGMAR (FCT ARIPIPI Program -Support for State Labs Development), HOLSMEER (EVK2-CT-2000-00060), CLIMHOL (PTDC/AAC-CLI/100157/2008), MIÑO-MINHO (0234_NATURA_MM_1_E), POPEI (PDCT/MAR/55618/2004), CALIBERIA (PTDC/MAR/102045/2008, national funds from FCT -Foundation for Science and Technology and COMPETE/FEDER-FCOMP-01-0124-FEDER-010599), CIIMAR (20132017 CIMAR) and CCMAR (Associated Lab PEstC/MAR/LA0015/2013 and UID/Multi/04326/2013).
Introduction About 60% of the solar radiation in traversing the Earth's atmosphere is absorbed by water vapor. Water vapor absorption involves two contributions: (i) a succession of vibrational bands located every 1500-2000 cm -1 with intensity decreasing from the far infrared to the visible. These are formed by multitude of narrow rovibrational absorption lines which are tabulated in spectroscopic databases like  HITRAN (Gordon et al. , 2017)  and GEISA  (Jacquinet-Husson et al. , 2016) , (ii) a 5 weak broadband absorption continuum with slow frequency dependence roughly following the vibrational bands. In the regions of low absorption between the vibrational bands -the water vapor transparency windows-the continuum has a major contribution and may dominate the cumulative effect of the weak rovibrational lines.
In this study, the temperature of the high finesse cell 15 was varied between 25°C and 50°C. Water vapor was generated by using a steel tank filled with few cm 3 of deionized water connected to the spectrometer upstream. Series of spectra were recorded in flow regime (typically 0. 13 sccm) during pressure ramps up to 23 mbar, in order to remain below the saturation pressure (about 28 mbar at 296 K). The pressure ramp was adjusted by a manual and a proportional valve placed before and after the cavity, respectively. About 6000 spectra were recorded during the pressure ramp (about 10 min). The gas pressure was measured 20 upstream the cavity by a CPG2500 sensor (from Mensor, 0-1000 mbar with an accuracy better than 0. 5 mbar). A second sensor was directly connected to the cavity. During the data acquisition, we noted significant differences between the pressure values provided by the two sensors, especially for low pressure values indicating the presence of a pressure gradient between the two measurement points.
This is due to the tube fitting which causes pressure losses on the line (Venturi effect), producing pressure artefacts especially when pressure is low or rapidly varying. Therefore, we decided to deduce the actual 25 water vapor pressure (or molecular density) by directly using two strong absorption lines of the main isotope (H 2 16 O) present in the recorded spectra (see below). The calibration of the frequency axis relies on the known FSR value (187. 5 MHz) and on the absolute frequency of water absorption lines as given in the HITRAN2016 database  (Gordon et al. , 2017). Fig. 1  shows typical spectra obtained with the OFCEAS system for a temperature of 50°C and pressure of 5, 15 and 20 mbar. The increase of the baseline level 30 and of the absorption lines is clearly apparent. The baseline is not completely flat because of optical interference pattern which is produced between the output cavity mirror and the photodiode.
1 ) but also for the FFT analysis of the residual). The quality of the spectrum simulation is illustrated by the residuals included in the bottom panel of Fig. 1 , showing an rms of the residuals of 1. 68×10 -8 cm -1 at 20 mbar for a single scan (100 ms). As mentioned above, the pressure value of each spectrum was calculated using the water absorption lines. The integrated absorption coefficient of a given line is simply the product of the molecular density (in molecule. cm -  ) by the line intensity (in cm/molecule) as tabulated in the HITRAN2016 database. In the studied interval, the two strongest lines at 3006. 975 and 15 3007. 1278 cm -1 are due to the main isotopologue (H 2 16 O). Their intensities were recently measured by  Loos et al. (2017)  with an accuracy of 1. 5 % and 2. 4 %, respectively.
As a validation test, we present in Fig. 2  the ratios of the pressure values derived from these two lines during a pressure ramp at 50 °C. The small dispersion of the ratio values along the ramp confirms the stability of the experiment (average value of 1. 056 with a standard deviation of 1. 0 %). Considering all the ramps recorded at different temperatures, we obtain an average ratio of 1. 041 and a standard deviation of 0. 84 %, which is 20 consistent with the intensity error bars reported by  Loos et al. (2017). Note that compared to  Loos et al. (2017) , the ab initio values of the BT2  (Barber et al. , 2006)  and  Schwenke and Partridge (2000)  theoretical databases of water vapor transitions gives ratio of 1. 048 and 1. 061, respectively. As a result, the pressure of each spectrum was obtained as the average value of the two determinations with an associated error bar estimated to 3 %.
Comparison to literature The different experimental determinations of the self-continuum cross-sections at room temperature in the 4 µm window are 20 gathered in Fig. 3  together with some of the last versions of the MT_CKD continuum up to the current V3. 2 version. In addition to the present study at 3007 cm -1 located at the high energy edge of the window, previous OFCEAS studies were performed at 2283 cm -1  (Campargue et al. , 2016)  and 2491 cm -1  (Richard et al. , 2017). Three datasets were obtained by FTS with long-path absorption cells and cover most of the transparency window: (i) CAVIAR consortium dataset obtained with a 512. 7 m pathlength at 293 K  (Ptashnik et al. , 2011a) , (ii) two series of FTS determinations from IAO in Tomsk  (Ptashnik et al. , 2013; 2015) , obtained with a pathlength of 612 m at 289.
5 and 287 K, respectively, (iii) the FTS dataset obtained at NIST by  Baranov and Lafferty (2011)  with a 100 m pathlength between 311 and 363 K. Finally,  Burch and Alt (1984)  reported continuum measurements in the center of the window using a grating spectrograph and an optical configuration aiming to minimize the spectra baseline drifts. 5   (Richard et al. , 2017)  is not yet taken into account). In the high energy edge of the window, the present 5 measurement at 3007 cm -1 validates the V3. 2 values which are unchanged from V3. 0.
5 Hz 15 acquisition rate). The present measurement points of the water continuum near 5000 cm -1 are located on the high energy edge of the 2. 1 µm window and complete our spectral sampling of the window by OFCEAS and CRDS between 4250 and 4725 cm -1. Fig. 4  shows an overview of the water vapor CRDS spectrum near 5002 cm -1 and the location of one of the four spectral points selected for the water vapor absorption continuum retrieval. From the spectrum recorded with an unprecedented 20 sensitivity in the region (α min ~order of 3×10 -10 cm -1 ), water vapor transitions with intensity values below 1×10 -28 cm/molecule are measured. The recorded spectrum was found in very good agreement with simulations performed on the basis of the HITRAN2016 database (see Fig. 4 ). The CRDS cell was filled with water vapor after purification by liquid nitrogen cooling and pumping on the residual vapor phase.
We have collected in Fig. 7 , temperatures around our measurement point present OFCEAS measurements cover the addition to the room temperature FTS studies discussed above, prior measurements at 3000 cm 20 CAVIAR consortium at temperatures from 350 to 472 K using a short 14 are fully consistent with our previous results and on line with a linear extrapolation of the It indicates that the present MT_CKD spectral dependence is validated up to 4950 cm decreased by about a factor of 2 at 5000 cm -1. Similarly to the situation found in the 4. 0 µm, the FTS values of the CAVIAR the two Tomsk datasets are largely overestimated, by about a factor of 5 in the present case. In our three sets of FTS values are reported with very large error bars (up to ±94 % for the CAVIAR upper limit values corresponding to the detectivity threshold of the FTS setups.
As the recorded spectral interval corresponds to a Atmos. Meas. Tech. Discuss. , https://doi. org/10. 5194/amt-2017-430 Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 5 December 2017 c Author(s) 2017. CC BY 4. 0 License. micro window surrounded by lines with intensity up to 4-5 orders of magnitude larger than those observed in Fig. 1, the monomer wing contribution is in fact the dominant contribution to the base line increase. According to the usual definition of the water continuum, lines located in the [ν -25, ν +25 cm -1 ] wavenumber interval have to be considered and simulated using the HITRAN2016 database with a Voigt profile truncated at ±25 cm -1 from the line center. Note that similarly to the continuum, the monomer wing contribution has quadratic pressure dependence.
Acknowledgements This project is supported by the  LabexOSUG@2020  ( ANR10 LABX56 )and the  LEFE-ChAt program  from  CNRS-INSU. The research leading to the results at 3. 33µm has received funding from the  European Community 's  Seventh Framework Program ERC-2011-AdG  under grant agreement n°  291062  ( ERC ICE&LASERS) , as well as  ERC-2015-PoC  under grant agreement n°  713619  ( ERC OCEAN-IDs ). 25 Atmos. Meas. Tech. Discuss. , https://doi. org/10. 5194/amt-2017-430Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 5 December 2017 c Author(s)  2017. CC BY 4. 0 License.
All samples were kept in a cool, dark place at 4˚C and transported to the laboratory. Analyses were performed on the day of sampling and the following day. On each sampling date, conductivity, pH, temperature, dissolved oxygen concentration, and total dissolved solids were measured at the four sampling stations using a 3420 IDS multiparameter probe (WTW, Germany). Dissolved oxygen saturation was calculated using the relationship %S ¼ 100xC=Cs; knowing that Cs ¼ 468:41=ð31:64 þ T CÞ Eq 1 where %S = Dissolved oxygen saturation percent; C = Dissolved oxygen concentration (mg L -1 ); Cs = Standard concentration; and T˚C = Temperature. Meteorological data were obtained from the Hammam Debagh weather station located close to the reservoir. Water transparency was estimated using a Secchi disk (SD = depth of visibility of the Secchi disk; diameter 25 cm), and Z eu (depth to which 1% of the surface irradiance penetrates the water column) was calculated from SD using the relationship Z eu = 2.
Molecular data analyses The sequence dataset provided by Illumina MiSeq sequencing was processed using the gene analysis pipeline of MR-DNA (http://www. mrdnalab. com/). This pipeline uses a classical approach, including the depletion of barcodes and primers in sequences and the removal of <150 bp sequences, sequences containing ambiguous base calls, and homopolymer runs exceeding 6 bp. The sequences were denoised, operational taxonomic units (OTUs) were generated, and chimeras were removed. The OTUs were defined by clustering at 3% divergence (97% similarity). The final OTUs were taxonomically classified using BLASTn against a database derived from RDPII (http://rdp. cme. msu. edu) and NCBI (www. ncbi. nlm. nih. gov). Assignation at the species level was performed when >97% sequence identity was found with a reference sequence.
Statistical analyses Multivariate analysis was performed on the physico-chemical and biological variables after data standardization, using principal component analysis (PCA) with the ADE4 and factoextra R packages. The explanatory variables retained for this analysis were pH, conductivity, dissolved oxygen saturation, air temperature, water temperature, evaporation, turbidity, depth of the euphotic zone, mixing depth, maximum depth of the reservoir, and phosphate, nitrates, nitrites and ammonium concentrations. Microcystis sp. and P. rubescens abundances were included in the analysis as supplemental data. The richness, Chao1 and Shannon-Weaver indices were used to assess the richness and diversity of the communities. These indices were calculated using PAST software and the vegan R package, respectively.
Richness and diversity of the cyanobacterial community based on 16S rRNA gene sequencing The molecular characterization of the cyanobacterial community in the Hamman Debagh reservoir was performed using samples collected from October 2013 to November 2014 to obtain a detailed description of this community. Chroococcales sequences were the most abundant (78% of the sequences), followed by Oscillatoriales (13%), Pleurocapsales (8%), and Nostocales (1%) (Fig  5 ). The total number of reads ranged between 527 and 53,341 (Table  1 ) among the 12 samples. In all, 84 OTUs of cyanobacteria were identified from our molecular data (sequences of the OTUs available in S3 Table ). As shown in Fig  4 , the cyanobacterial community was generally dominated by Microcystis wesenbergii (>80% of the reads) during the entire study period except in January 2014 (dominance of Chroococcidiopsis sp.
Discussion The results from the Illumina MiSeq sequencing of a 16S rRNA fragment show that the cyanobacterial community in the Hammam Debagh reservoir displays high richness, as 89 species were identified in our molecular dataset. In contrast, the diversity in this community was very low due to the high dominance of Microcystis sp. The dominance of this genus has also been found in other Algerian reservoirs used for drinking water  [6, 7, 30]  and in North African freshwater ecosystems  [31] [32] [33] [34]. The molecular results show that 98% of the Microcystis reads were assigned to M. wesenbergii. As this morphospecies is not known to be a microcystin producer, contrasting with other Microcystis morphospecies  [35, 36] , its dominance indicates that the potential toxicity of the Microcystis blooms in the Hammam Debagh reservoir is likely limited, at least when considering only the microcystins.