dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, to evaluate the potential of PHYSAT for the detection of phytoplankton assemblages, we took advantage of the Continuous Plankton Recorder (CPR) survey, collected in both the English Channel and the North Sea. The available CPR dataset contains information on diatom abundance in two large areas of the North Sea for the period 1998-2010. Using this unique dataset, recurrent diatom assemblages were retrieved based on classification of CPR samples. Six diatom assemblages were identified in-situ, each having indicators taxa or species. Once this first step was completed, the in-situ analysis was used to empirically associate the diatom assemblages with specific PHYSAT spectral anomalies. This step was facilitated by the use of previous classifications of regional radiance anomalies in terms of shape and amplitude, coupled with phenological tools.
, 2005 (Alvain et al. , , 2008)). However, the study of  Alvain et al. (2012)  showed that characteristics of radiance anomalies are explained theoretically by phytoplankton characteristics such as cell size, composition, intracellular structure, cells arrangement, and absorption. This study made the suggestion that specific radiance anomalies could be empirically extended beyond dominance cases. Thus, analysis restricted to "dominant" groups based on biomarker pigments may represent an under-exploitation of radiance anomalies database, which could also contain a large diversity of information. Other types of in-situ data about phytoplankton are common, such as the Continuous Plankton Recorder  (CPR thereafter, SAHFOS, 2015). In this context, the possibility to enhance the radiance anomaly labeling capabilities, when in-situ information about specific phytoplankton abundance are available, was evaluated.
For this purpose, the CPR diatom database from the North Sea and the English Channel were used (Figure  1 ). Broadly, diatoms represent a dominant taxon in the North Sea, at least during part of the year, with an abundance contribution from 40 to 90% of total microphytoplankton  (Reid et al. , 1990; Leterme et al. , 2006). Please note that here the term "dominant" has not the same meaning that the one used in PHYSAT (see definition n • 6 in Supplementary Material). Although North Sea waters are not only characterized by diatoms, the fairly detailed information on diatom species and taxa provides a good starting point in this evaluation. However, a new approach was necessary to promote the use of abundance data to empirically associate radiance anomalies with specific phytoplankton assemblages in-situ; and therefore, enable future improvements of PHYSAT.
In this paper, a method was designed to label radiance anomalies (Ra(λ) thereafter) according to in-situ information provided by the CPR survey. The goal was to answer the following question: is there an empirical association between the presence of a diatom assemblage and specific radiance anomaly signals (as supported by the theoretical study of  Alvain et al. , 2012) ? First, tools were developed and applied to extract suitable ecological information (i. e. , diatom assemblages) from the CPR in-situ database (section 2. 4. 2). Second, the Ra(λ) was classified according to their form and amplitude  (Ben Mustapha et al. , 2014) , and to their phenological characteristics (section 2. 2). The objective of this second step was to fully characterize the Ra(λ) ranges in terms of shapes, amplitudes, and phenological behaviors in order to increase the chances of finding an association between specific in-situ diatom assemblages and a specific signal from remote-sensing.
The three main steps are framed with dotted lines. The first step involved the processing of remote-sensing data; the second step involved the processing of CPR data and the third step involved the test of association between the results of steps one and two. -A concurrent in-situ database with information on phytoplankton to empirically give a name to radiance anomalies -Clear sky conditions (as define in  Alvain et al. , 2005)  for matchups between the remote-sensing Ra and the in-situ data Different approaches can be used to analyze Ra(λ) shapes and amplitudes. In the first version of PHYSAT  (Alvain et al. , 2005) , large envelopes of minimum and maximum Ra(λ) values were used. This was in fact, the first time radiance anomalies had been analyzed. In Ben  Mustapha et al. (2014) , an improvement consisted of using a Self-Organizing Map (SOM hereafter) to classify the Ra(λ) more precisely and independently.
In-Situ CPR Data To evaluate the potential of PHYSAT in the detection of phytoplankton assemblages, it is necessary to make use of insitu information about phytoplankton assemblages that may be potentially associated with a specific remote-sensing signal. An ideal in-situ database would be an inventory of all phytoplankton assemblages at a synoptic scale and with a highrepeat frequency over the course of one full calendar year (or longer). Unfortunately, such a database is currently not available. However, CPR data (SAHFOS, 2015) collected in the English Channel and North Sea, are particularly relevant in this first attempt to evaluate the potential of PHYSAT for the detection of diatom assemblages. The Continuous Plankton Recorder survey is an upper layer plankton monitoring program that regularly collects samples in the North Atlantic and the North Sea, since 1946  (Reid et al.
, 2003). This in-situ dataset is unequaled in terms of size, spatio-temporal coverage, and representativeness of microphytoplankton abundance; it is classified into four groups: dinoflagellates, diatoms, silicoflagellates, and prymnesiophyceae. In particular, the CPR dataset (5,977 samples, Figure  1 ) recorded an abundance of 72 diatom species or taxa over the period 1998-2010, representing from 40 to 90% of the total microphytoplankton abundance in Summer (June-August) and Spring (March-June), respectively  (Leterme et al. , 2006). These diatoms are identified at a species or taxonomic level (one taxon could represent more than one species). Although diatoms are not alone in North Sea waters, and are associated with other types of phytoplankton (e. g. , pico-and nano-phytoplankton), dealing with diatoms as an indicator of assemblages is the first step in describing phytoplankton communities.
Characterization of Diatom Assemblages from in-Situ CPR Data: Step 2 The following processing steps correspond to Step 2 in Figure  2. The main objective was to analyze the large CPR database in order to obtain condensed ecological information suitable to be matched with Ra(λ) classes in Step 3. The CPR diatom counts recorded the occurrence of 72 diatom species and taxa. As mentioned above, diatoms were considered in this study as an indicator of the phytoplankton community composition; as such a species or taxa level description was not available from the other phytoplankton groups identified via CPR. The diatom species or taxa formed assemblages 1 ; some occurring in the same location at the same time according to their physical, chemical, and/or biological preferences  (Sverdrup, 1953; Platt and Jassby, 1976).
The rare diatom taxa 17 were removed from this classification step, leading to a database of 11 non-rare diatom taxa. Then, the next step was to determine the number of assemblages that had both a statistical and an ecological meaning as defined in  Legendre and Legendre (2012) , through the computation of the Indicator Value (Equation  2 ). The IndVal is computed for each possible cut-off level in typological samples (i. e. , for each possible diatom assemblage). For example, the first cut-off level delineated two diatom assemblages, and the second one delineated three diatom assemblages-with their respective Indicator Value. A ij = Nindividuals ij / Nindividuals i. B ij = Nsamples ij /Nsamples j. IndVal ij = A ij x B ij (2) Where A ij is a measure of specificity : Nindividuals ij is the mean number of individuals of taxon i across group of samples j; and Nindividuals i.
is the sum of the mean number of individuals of taxon i over all groups of samples. And B ij is a measure of fidelity: Nsamples ij is the number of samples in cluster j where taxon i occurs; and Nsamples. j is the number of samples in cluster j And IndVal ij is the Indicator Value of the taxon i in the sample group j (Equations from  Dufrêne and Legendre, 1997). An ideal number of assemblages is reached when the Indicator Value does not increase significantly for a higher number of assemblages (see details in  Dufrêne and Legendre, 1997; Aho et al. , 2008). Please note that, the Indicator Value method was computed for the whole CPR diatom dataset (from 1998 to 2010) in order to reflect, as close as possible, the in-situ situations.
The threshold helped to retain only labeling that could be observed regardless of the calibration subset. Considering this threshold, the procedure led to the labeling of 11. 7% of the 85 available Ra(λ) classes (total of Ra(λ) classes). Amongst the six in-situ diatom assemblages identified in the CPR dataset, three assemblages were robustly associated with ten Ra(λ) classes. In fact, during the 1200 repetitions of the calibration, assemblages characterized by Thalassionema nitzschoides, Thalassiosira sp. , and Rhizosolenia imbricata, were associated with the one of the Ra(λ) classes more than 840 times (70% threshold). On the contrary, assemblages characterized by two taxa of Chaetoceros and by Pseudonitzschia seriata were not sufficiently associated with Ra(λ) classes in the multiple steps of calibration.
Radiance Anomaly Spectra Associated with Diatom Assemblages Figure  5  displays the spectra of labeled-Ra(λ) classes associated with the three diatom assemblages. These Ra(λ) spectra are characterized by different amplitudes, shapes, and seasonalities. Note that these results were expected for intra-assemblage as well as inter-assemblage spectra variations in terms of shape and amplitude (see further information in section 4). Please also consider that, some Ra(λ) could appear very similar in terms of shape and amplitude, but their seasonal characteristics were different (an example is shown in Figure  S1 ). The assemblage characterized by Thalassionema nitzschoides (  on the available in-situ dataset. According to their shapes, these labeled Ra(λ) could be divided into two subgroups: high and low Ra(λ), at 412 and 443 nm, respectively.
The assemblage characterized by Thalassiosira spp. was associated with one Ra(λ) class (1. 2% of available Ra(λ), Figure  5b  for referent spectrum). This reference spectrum had a straight shape. The Rhizosolenia imbricata assemblage was associated with three Ra(λ) classes (3. 3% of available Ra(λ), Figure  5c  for referent spectra). These spectra were different in shape and amplitude. At this stage, 10 Ra(λ) classes, with specific shapes, amplitudes, and seasonalities, were associated with three diatom assemblages, which were selected after the repetitive calibration validation procedure applied to the available CPR dataset.
These classes were more frequently detected in the southern part of the North-Sea, the English Channel, and along the western coasts of the United Kingdom (UK). In the summer, the frequency of Ra(λ) classes associated with Thalassionema nitzschoides was low, and was located in the northern part of the North Sea and along the southern coasts of the UK. The frequency of the detection of the Ra(λ) class labeled as the Thalassiosira spp. assemblage showed a very low frequency (due to only one labeled Ra(λ), based on available in-situ dataset at this stage). This Ra(λ) class had its highest frequency in the middle of the year (June and July), and was mainly located in the middle of the North Sea (Figure  7B ). The Ra(λ) classes identified as the Rhizosolenia imbricata assemblage exhibited a higher frequency in spring (March and April), which subsequently diminished during the following months, remaining low for the rest of the year.
Spatio-Temporal Distribution of Labeled-Ra(λ) Based on the CPR dataset available for this study, ten Ra(λ) classes (out of 85) were associated with three specific diatom assemblages in North Sea waters, with a high degree of confidence. The three other assemblages could not be robustly associated with Ra(λ) classes, according to our criteria (sections 2. 4. 3 and 2. 4. 4). For the ten Ra(λ) classes associated with diatom assemblages, monthly maps of Ra(λ) frequency show, for the first time, spatial and temporal variability in agreement with previously published literature in the North Sea  (Reid et al. , 1990; Beaugrand et al. , 2001). In addition, the spatial distribution of all labeled-Ra(λ) classes followed known patterns relating to physical and hydrological conditions in the North Sea during the year that drive the phytoplankton distribution  (Reid et al.
Remaining Ra(λ) Database, Limitations, and Perspectives For this first attempt to evaluate PHYSAT in non-dominance cases detection, very stringent steps were chosen, with a high degree selection (such as the 70% threshold on the rate of labeling). Potential future extension of this method, with additional in-situ data (hopefully not just limited to diatoms) will have to find a balance between strict thresholds, dataset availability, and score of validation. If the proposed method allows the identification of 11. 7% of the available Ra(λ) classes at this stage, the remaining anomaly classes are available for future investigation and empirical labeling. Figure  9  illustrates the potential of PHYSAT development by showing spatial distribution of the most frequently detected Ra(λ) classes in the North Sea.
Third, PHYSAT requires a specific reference table (see Equation  1 ) for each ocean-color sensor. For now, it is difficult to evaluate the uncertainties during the transition of the PHYSAT labels from one sensor to another one. Thus, impacts of multi-sensors inter-calibration will have to be carefully addressed before PHYSAT application to standardized remotesensing database. That is one reason why we greatly support the development of in-situ measurements to carry out a new calibration and/or to validate the transposition of labels between ocean-color sensors.
CONCLUSION PHYSAT  (Alvain et al. , 2005 (Alvain et al. , , 2008 ) is an ocean-color algorithm that has allowed us to identify five phytoplankton groups based on ocean-color radiance anomalies analysis in terms of shape and amplitude. However, this empirical algorithm was limited to dominant cases due to the use of biomarker pigment ratios in the empirical calibration step. In this study, we took advantage of both the addition of phenological tools to analyze the radiance anomalies and the diatom species abundance data to evaluate the PHYSAT potential in terms of phytoplankton assemblages detection. The use of abundance data in the PHYSAT calibration step presents a promising avenue for future investigations and development of PFT algorithms. Although this study is based on a limited number of assemblages at this stage, it demonstrates the potential of development of the PHYSAT method beyond the dominance case, toward the detection of phytoplankton assemblages.
ACKNOWLEDGMENTS The authors would like to thank  NASA/GSFC/DAAC  for providing access to daily SeaWiFS products and the  Sir Alistair Hardy Foundation for Ocean Science (SAHFOS)  for providing in-situ observations from  CPR. www. englisheditor. webs. com is thanked for its English proofing. David Antoine ,  Annick Bricaud ,  Lucile Duforêt-Gaurier ,  Hubert Loisel ,  Bertrand Lubac , and  Julia Uitz  are thanked for their valuable comments on an earlier draft of this paper. Experiments presented in this paper were carried out using the CALCULCO computing platform, supported by  SCoSI/ULCO  ( Service COmmun du Système d'Information de l'Université du Littoral Côte d'Opale ).
, 2014; Forni et al. , 2015]. ChemCam acts as a survey tool, having collected more than 422,000 spectra to date. It targets rocks and soils at distances 1. 5-7 m from the rover, analyzing spots of 350-500 μm diameter with multiple laser shots to remove dust from the surface and obtain chemical depth profiles, allowing the study of potential coatings  [Lanza et al. , 2015; Wiens et al. , 2012; Maurice et al. , 2012]. The wealth of data collected by ChemCam aids in directing Curiosity toward intriguing science targets for further analyses with its instrument suite [e. g. ,  Newsom et al. , 2016; Frydenvang et al. , 2016; Gasda et al. , 2016]  and provides a chemical record of samples throughout the entire rover traverse. Chlorides, carbonates, and sulfates represent classes of potential evaporite minerals that have been previously detected on the Martian surface; perchlorates and nitrates are also found in minor amounts [e.
, 2015] , potentially as a result of NaCl in the rock (Ca from the accompanying host rock mixes with the Cl in the plasma to produce the CaCl molecular line that is identified in the LIBS spectrum). At Gale crater, carbonates have not yet been inferred from ChemCam data nor found at abundances >1-2 wt % using CheMin  [Leshin et al. , 2013]. The ChemCam spot size of 350-500 μm allows the instrument to pinpoint and measure the composition of individual salt veins. However, an outstanding question is the ability of ChemCam to detect more dispersed salts that would appear at lower concentrations in the bulk rock, for example, as detrital contributions, cementing agents, or diagenetic precipitates in the sedimentary rocks in Gale crater. The presence of salts in bulk rocks has been inferred using ChemCam based on observed anticorrelations between the potential cation and other major elements [e.
[2011]  developed a database of lines detectable by ChemCam under a Martian atmosphere for 32 elements focusing on controlled samples composed of a single or a small number of elements to ensure proper line identification. They identified 27 lines for Cl, 33 for C, and 28 for S. Ollila et al. [2011]  focused on carbon, identifying 18 C peaks and 21 C 2 Swan bandheads in graphite and all 18 C peaks and 4 of the C 2 Swan bandheads in one or more geologic samples. Lanza et al. [2010]  found that different carbonates could be distinguished from igneous materials and from each other using multivariate techniques on LIBS spectra collected under Martian conditions. Sulfur-bearing rock samples including sulfates and sulfides were used in the study by  Dyar et al. [2011] , which recommended implementing multivariate analysis of the spectral regions containing S lines around 540.
We build upon prior work by (1) measuring LIBS spectra of controlled mixtures of Mars-relevant salts to investigate how effects due to the basaltic matrix-typical geochemistry for Martian materials-influence the spectra and our ability to identify salt-related emission lines and (2) preparing the mixtures at several concentrations of salt relative to basalt to evaluate the potential for quantitative analysis of abundance and to establish detection thresholds. We present the spectral trends in continuum and line emission with salt concentration; identify Cl, S, and C detection thresholds; determine the best lines for use in anion detection and quantification; and then discuss how these results relate to the current literature and their applicability to the ChemCam data set and future Mars LIBS investigations.
Italicized samples were measured in the second batch (see section 2). instrumental neutron activation analysis for Cl, and combustion in a pure oxygen environment followed by gas-phase infrared (IR) spectroscopy for C and S (measured as CO 2 and SO 2 , respectively) performed by Activation Laboratories Ltd. (Actlabs) (Table  S1  in the supporting information). Measured C, S, and Cl values match expected values (Figure  S2 ). Mixed sample powders were pressed into pellets, and LIBS analyses were performed using the ChemCam-analog instrument at Los Alamos National Laboratory  [Clegg et al. , 2017]. The instrument consists of the engineering model mast unit containing the laser, telescope, and RMI, placed inside an enclosure cooled to 14-19°C, connected by a fiber to a body unit outside the enclosure containing the optical demultiplexer, spectrometers, and data processing unit.
This setup generates a maximum ChemCam laser energy of 14 mJ per pulse. Samples were analyzed with 50 consecutive laser pulses on each of five different locations on the sample surface, producing 225 spectra per sample (after excluding the first five laser shots due to potential surface effects). The experiment was performed in a chamber containing 7 torr of CO 2 to simulate a Mars-like atmosphere. Replicating the conditions under which the ChemCam data set on Mars is collected is important for making relevant comparisons to data obtained by the mission  [Cousin et al. , 2011]. Emission was collected using three detectors over the following wavelength ranges: ultraviolet (UV, 240-340 nm), blue-violet (VIO, 380-470 nm), and visible and near-infrared (VNIR, 490-850 nm). The first batch of samples was measured in September-October 2013 at a laser-to-sample distance of 1.
6 m and the second batch in August-September 2014 at a distance of 3 m. The change in distance was not an ideal difference between batches but was a consequence of the setup available at the time. Distance corrections were applied during data processing to account for the viewing geometry (see sections 2. 2 and 4. 2). Samples from the first batch of analyses with compositions measured by Actlabs were included in the database compiled by  Clegg et al. [2017]. The identification labels for all samples are provided in Table  S2  in the supporting information. A comparison of the total integrated LIBS emission per shot for each location on a sample surface showed similar behavior among, in most cases, all five and in a few anomalous cases, at least 3-4 of the spots. This suggests that the pellet material was reasonably homogeneous.
Data Processing Standard ChemCam data processing techniques were applied to produce clean calibrated spectra (CCS) files used for analysis of ChemCam data  [Wiens et al. , 2013]. In addition, "minimally processed" data products were produced to preserve characteristics of the raw spectra including the continuum emission. First, the "dark" background spectrum, the median of 50 spectra collected without the laser, was subtracted from each LIBS spectrum. This removes reflected light from the sample surface, any stray light in the instrument, and instrument noise, e. g. , due to dark currents. A wavelength calibration was performed by applying an offset correction derived from the comparison between a Ti reference spectrum and the median spectrum of a Ti calibration target measured on the same day as the sample.
The line wavelengths are calibrated to match those obtained in a vacuum to provide appropriate comparisons to reference databases. Each spectrum was then multiplied by a smoothed gain function to correct for instrument response. Gaussian white noise was removed using the wavelet analysis method of  Wiens et al. [2013]. The spectrum was decomposed into a series of consecutively lower resolution scales each described in terms of the wavelet basis and corresponding coefficients. A noise hard thresholding was defined at each scale by iteratively selecting wavelet coefficients less than 3× the standard deviation from the average value. After 10 iterations, wavelet coefficients less than the noise threshold were put to zero and the sum of the resulting wavelet scales became the denoised spectrum.