dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , list of ingredients for industrially processed foods), etc. In addition, FDBs can only provide an incomplete coverage of foods and/or their nutrients leading to gaps in values, as missing information on some minority compounds (from aromas to chemical contaminants). Despite efforts on updates, data ageing is inevitable due to limited resources. Food databases have been following the advancements of science, as highlighted above (see Sections 3 and 4. 7), and today's challenges include adding comprehensive information about the environmental impact of foods, health/sustainability linkages, as well as qualitative features, because food goes far beyond its composition (Figure  3 ). Concerning the relevance of FDBs for human health, they only indirectly address a reductionist view of it and should not be used for other purposes than building a balanced diet to fully address nutritional needs and avoid nutritional deficiencies.
foodb. ca accessed on 17 August 2021 Food data DTU foods' database-Frida Easily searched by food Updated every few years (frida. fooddata. dk DTU food (National food Institute (Denmark) Fødevaredata (Frida Food Data) http://frida. fooddata. dk/ accessed on 17 August 2021 Food Data reflects the food supply in Denmark and targets professionals in food Laboratory analysis item (alphabetic order), food group or by parameters, which include (last update 29/10/19) and food composition referred to be quite stable over the accessed on 17 August 2021), version 4, 2019, National Food Institute, and nutrition waste and added sugar past 50 years Technical University of Denmark European Food Safety The dataset presents energy, Authority (2013) 'Food EuroFIR AISBL, International non-profit association EuroFIR https://www.
eurofir. org/ food-information/ accessed on 17 August 2021 macronutrients, vitamins, and minerals as well as other bioactive compounds and daily recommended intakes for Estimations from FDB by expert panels and targets food and nutrition professionals Search by food name and by component Updated regularly (each few years)-last update 21 January composition database for nutrient intake: selected vitamins and minerals in selected European selected nutrients countries'. Zenodo. doi: 10. 5281/ZENODO. 438313. FAO InFoods http://www. fao. org/ infoods/infoods/en/ accessed on 17 August 2021 InFoods is a network bringing together food composition compilers, data generators (e. g. , chemists), and data users (e. g. , and decision makers nutritionists, food scientists), food composition database compilers retrieve analytical data on food composition for commonly published sources complemented with other consumed foods and Datasets are downloadable in xls and pdf formats, as well as searchable with software tools for e.
g. , supply/availability data labeling and food dietary assessment, Updated regularly FAO. 2020. International Network of Food Data Systems (InFoods) CIQUAL-ANSES French Food Composition Database https://ciqual. anses. fr/ accessed on 17 August 2021 Average nutritional composition of food consumed in France. Average value of each component, a minimum and a maximum, together with a confidence code (A = very reliable, D = less reliable). in calcium or poor in sodium) component (ex. list of food rich Information on a specific Compilation of different sources: yearly sampling of around 60 to 80 foods in collaboration with subcontractor laboratory; data from OQALI; research programmes on food composition with external composition tables reports; foreign food literature and laboratory partners. Scientific Easily searched by food item, food group, or by components Released every 2 to 4 years French Agency for Food, Environmental and Occupational Health and Safety.
The proposed model is evaluated using an in situ dataset collected in open oceanic, coastal, and inland waters. Comparisons with other standard semi-analytical algorithms (QAA and GSM), as well as match-up exercises, have also been performed. The applicability of the algorithm on OLCI observations was assessed through the analysis of global IOPs spatial patterns derived from 3SAA and GSM. The good performance of 3SAA is manifested by median absolute percentage differences (MAPD) of 13%, 23%, 34% and 34% for b bp (443), a nw (443), a phy (443) and a cdm (443), respectively for oceanic waters. Due to the absence of spectral constraints on IOPs in the inversion of total IOPs, and the adoption of an OWC-based approach, the performance of 3SAA is only slightly degraded in bio-optical complex inland waters.
(2015)  algorithm accounting for some bio-optical specificities through the use of optical water classes (OWC). This method, hereafter referred to as Three-step Semi Analytical Algorithm (3SAA), is tested using different in situ and satellite datasets that are first presented. Then the structure of 3SAA and its validation using these datasets and per OWC are provided. The performance of 3SAA is also compared to those obtained from the QAA  (Lee et al. , 2002)  and GSM  (Maritorena et al. , 2002)  algorithms using monthly Ocean and Land Color Instrument (OLCI) data.
The validation datasets The 3SAA algorithm is evaluated using four independent datasets. The first two datasets are composed of in situ R rs (λ) and IOPs, with the first measured in marine waters (DS-MW) and the second in coastal ocean and inland waters (DS-IW). The third (DS-Sat) and fourth (DS-Argo) validation datasets are both composed of in situ IOPs and colocated and nearly simultaneous satellite R rs (λ). The spatial distribution of each dataset can be seen in Fig. 1. Following the turbidity flag developed by  Robinson et al. (2003)  which assumes that waters with R rs (670) higher than 0. 0012 sr -1 are considered as turbid, the four datasets cover a variable fraction of turbid waters: 4. 3%(DS-MW), 88% (DS-IW), 4. 7%(DS-Sat), and O%(DS-Argo).
In situ IOPs in open ocean and coastal waters (DS-MW) This dataset includes in situ data collected in various environments that have been presented in  Loisel et al. (2018)  and  Valente et al. (2016) , and those collected (and not gathered in  Valente et al. (2016) ) in the frame of the BOUSSOLE  (Antoine et al. , 2006) , Plumes and Blooms  (Kostadinov et al. , 2012) , and PEACETIME projects (J. Uitz, E. Boss and N. Haëntjens, contribution to the "PEACETIME-OC" project funded by CNES). The IOPs values in DS-MW cover a broad range of variability (Fig. 2 ). The larger number of b bp data points is contributed by the BOUSSOLE  (Antoine et al. , 2006)  dataset in which b bp has been measured by an automatic system every 15 min between 08:00 AM and 04:00 PM since May 2015. For match-up, only the ones from the daily continuous-measurements in the BOUSSOLE and PEACETIME-OC that were closes to the satellite overpass were kept.
In situ IOPs in inland waters (DS-IW) The dataset for inland waters (DS-IW) is composed of measurements collected in optically complex lakes and rivers by different research groups in Canada  (Binding et al. , 2018; Bélanger et al. , 2017) , the United States  (Mouw et al. , 2017; Casey et al. , 2020) , Brazil  (Jorge et al. , 2017) , Estonia  (Kutser et al. , 2016)  and the Netherlands  (Spyrakos et al. , 2018). Even though the number of data points in DS-IW is much lower than in DS-MW, DS-IW still covers significant variability (Fig. 3 ).
Satellite-to-in situ matchup datasets (DS-Sat and DS-Argo) DS-Sat (Fig. 4 ) includes in situ IOPs measurements collected during oceanographic cruises and from the fixed buoy, BOUSSOLE  (Antoine et al. , 2006)  from September of 1997 to November of 2017, and  GlobColour R rs (λ) products merged from different sensors (http://www. globcolour. info/CDR_Docs/GlobCOLOUR_PUG. pdf), including the MEdium Resolution Imaging Spectrometer (MERIS), the Moderate-Resolution Imaging Spectroradiometer (MODIS), the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). Being a merged product, the GlobColour data ensure a higher coverage than that available from single sensor products, and then a higher number of match-up data points, but within a time window that can be as large as 12 h.
DS-Argo (Fig. 4 ) is exclusively composed of in situ b bp (700) measurements collected between 04/2016 and 09/2018 by autonomous profiling floats from the biogeochemical Argo (BGC-Argo) network  (Claustre et al. , 2020)  and the matched OLCI R rs (λ) data. The use BGC-Argo dataset increases considerably the number of match-up data points, as shown in  Bisson et al. (2019). We used the MERMAID tools protocol (http://mermaid. acri. fr/data proto/dataproto. php), which is in turn based on the NASA Ocean Color protocol  (Bailey and Wang, 2001)  to match satellite data with IOPs for DS-Sat and DS-Argo datasets. Basically, matchup was performed within a 3 × 3 pixel window, in which the coefficient of variation of R rs needs to be below 0. 1 for each wavelength and the number of valid pixels needs to be at least 5.
These two different match-up datasets allow the model to be evaluated accounting for uncertainties associated with atmospheric corrections as well as those related to match-up exercises such as the spatio-temporal mismatch between in situ sampling and satellite overpass. Compared to the in situ datasets, the number of data points in DS-Sat is considerably lower, although high enough to have a global  distribution and a 4-order range of variability, allowing the model to be properly evaluated from satellite data (Fig. 4 ).
, 2018). Sub-step 5 The non-water absorption coefficient (a nw (λ)), and the particulate backscattering coefficient (b bp (λ)), are then obtained by subtracting a w (λ) and b bw (λ) from a(λ) and b b (λ), respectively. Sub-step 6 The optical water class (OWC) of a given pixel is estimated from the R rs (λ) visible spectrum following  Mélin and Vantrepotte (2015). These OWC have been generated from SeaWiFS global R rs (λ) data using an unsupervised clustering technique applied to normalized R rs (λ) spectra to attenuate the impact of the change of concentration of OSC on OWC  ( Lubac and Loisel, 2007). Despite this dataset, used to calibrate the 17 OWC, having been collected over global coastal waters (but including the Mediterranean Sea), it covers almost all kind of optical conditions from very turbid to oligotrophic waters.
Statistic indicators for model performance To assess the model performance, we use scatterplots of model predictions vs. observations as well as quantitative statistical metrics of differences between the corresponding model predictions and observations. We calculated several statistical indicators that are typically utilized in the assessment of model accuracy or skill in ocean sciences, including ocean color-related models (e. g. ,  Brewin et al. , 2015; Friedrichs et al. , 2009; IOCCG, 2006; Stow et al. , 2009). These indicators include the root-mean-square deviation (RMSD), calculated in the logarithmic space: RMSD = ⎛ ⎜ ⎜ ⎝ ∑ N i=1 ( log10 ( IOP mod i ) - ( log10 ( IOP obs i ) ) 2 N -DF ⎞ ⎟ ⎟ ⎠ 0. 5 (4) where N is the number of data points, DF is the degrees of freedom (in this case DF = 2 and stands for the model-derived and in situ datasets), IOP i mod represents the model-derived IOP values, and IOP i obs measured IOP values.
We also report on the median bias (MB), representing the difference between the medians of the model-derived data and corresponding measured data: MB = median ( IOP mod i -IOP obs i ) (5) The median ratio (MR) of model-derived to measured values which provides a non-dimensional measure of bias: The median absolute percent difference (MAPD), is calculated as the median of the individual absolute percent differences between the modeled and measured data: MAPD = median ( ⃒ ⃒ IOP mod i -IOP obs i ⃒ ⃒ IOP obs i ) *100 (7) MAPD will also be calculated between IOPs estimated with two different models applied to satellite observations (Section 3. 4): MAPD sat = median ( ⃒ ⃒ IOP mod1 i -IOP mod2 i ⃒ ⃒ IOP mod1 i + IOP mod2 i ) *200 (8) where IOP i mod1 and IOP i mod2 are the IOPs values estimated from the different models considered.
Validation of 3SAA from in situ measurements (DS-MW and DS-IW) The performance of the 3SAA algorithm is evaluated using the marine (DS-MW) and inland (DS-IW) waters datasets. Over coastal and open ocean waters (DS-MW), we observe a tight correlation between the estimated and measured IOPs (Fig. 7 ), with no specific behavior according to the considered OWC (Fig. 8 ). Among the different IOPs at 443 nm, b bp is estimated with the best accuracy (MAPD = 13%), followed by a nw (MAPD = 23%), a phy (MAPD = 34%), and a cdm (MAPD = 35%). The latter two were retrieved with effectively the same accuracy. The MAPD value reaches 47% for a phy (  665 ), but with a relatively low bias (-0. 001 m-1). This result was expected due to the usually lower a phy signal in the red compared to the blue. The lower retrieval accuracy observed for a phy and a cdm is due to the propagation of errors from step 2 to step 3, and to errors inherent to step 3.
The validation results of the LS2 model used in the second step (i. e. estimation of b bp and a nw ), and slightly improved for 3SAA (see Section 2. 3), are consistent with the ones published in  Loisel et al. (2018). b bp is also retrieved with a relatively good and similar accuracy at the four considered wavelengths, with low bias values (between 2. 10 -4 and 9. 10-4 m -1 ). The MAPD values are 13%, 40%, 25%, and 35% at 443, 490, 510, and 560 nm, respectively. The lower retrieval accuracy observed at 490 nm compared to the other wavelengths could be explained by the BOUSSOLE dataset for which a net depressing effect in the b bp (λ) spectra can be observed at this band (not shown). The same pattern was observed in a recent study in which QAA-v6 was evaluated on the BOUSSOLE dataset, and where the bias (in %) increases by a factor of 1.
6 between 443 and 490 nm  (Pitarch et al. , 2020). Removing this dataset from DS-MW increases the performance of the retrieval at 490 nm by a factor of 1. 5 for MAPD (40% to 26%), from 1. 39 to 1. 11 for MR, and an increase in the determination coefficient from 0. 92 to 0. 98. The origin of such pattern (instrument vs. processing) has yet to be identified. The apparently higher values of b bp (490) (Fig. 7f ) compared to b bp (443) (Fig. 7e ) are simply due to the fact that b bp (λ) has not been equally acquired at each band for each station. For instance, b bp (λ) is only available at 490 and 510 nm at many stations. A slight underestimation is observed for all absorption coefficients at 443 nm, which is reflected by the negative bias (-0. 015 m -1 , -0. 004 m -1 and -0. 0109 m -1 , for a nw (443), a phy (443), and a cdm (443), respectively).
In contrast, a slight overestimation is observed for b bp at all wavelengths (13% < MAPD<40% and 0. 0001 < MB < 0. 0005). A similar behavior has previously been observed by other inverse models tested over the NOMAD dataset  (Werdell et al. , 2013; Brewin et al. , 2015). For instance, in their extensive inter-comparison exercise involving 11 inverse models  (Smyth et al. , 2006; Devred et al. , 2011; Lee et al. , 2002; Lee et al. , 2009; Lee et al. , 1999; Garver and Siegel, 1997; Maritorena et al. , 2002; Werdell et al. , 2013; Doerffer et al. , 2002) ,  Brewin et al. (2015)  showed a systematic negative bias for all 11 models for a nw (between -0. 05 m -1 and -0. 0124 m -1 ) and a cdm (between -0. 150 m -1 and -0. 03 m -1 ) and for 10 models for a phy , (between -0. 117 m -1 and -0. 03 m -1 ).
In contrast, MB presents a clear gradient with increasing MB values from group 4 to 1, with larger negative bias being observed for a nw and a cdm in turbid/ absorbing water type. Worsened agreements are expected for inland waters, especially for the third step (Fig. 9 ), because 3SAA was not initially developed for optically-complex inland waters. The DS-IW dataset mainly covers two OWC (Classes 1 and 2) which were originally identified as very coastal waters and largely influenced by a large river outflow  (Mélin and Vantrepotte, 2015) , except for Lake Superior, which have all classes between 1 and 6. The retrieval accuracy for all IOPs is satisfactory (all data points are located around the 1:1 line), but slightly lower than for the coastal and open ocean waters dataset and with some discrepancies according to the considered IOPs.
For the absorption coefficients, the best retrieval accuracy is achieved for a nw (443) (MAPD = 29%), a cdm (443) (MAPD = 36%), and a phy (443) (MAPD = 44%). For the backscattering coefficient, the retrieval accuracy strongly differs according to the inland water sub-dataset considered. For instance, b bp (  443 ) is retrieved with a much better accuracy over the St. Lawrence River and estuary, Canadian lakes, and Lake Superior datasets (MAPD of 23% at 443 nm) than over the Amazonia (MAPD of 83. 88% at 443 nm) dataset (Fig. 9 ). Moreover, in contrast to the Amazonia dataset gathering data from 4 different lakes mainly dominated by organic matter during the sampling period, the quality of the b bp (λ) retrieval for the Canadian lakes and Lake Superior data is much higher and does not depend on the wavelength.
07 m -1 instead of -0. 16 m -1 in the blue) part of the spectrum (unfortunately measurements have not been performed in the red). For such turbid water the correction method proposed by the Hydroscat's manufacturer may not be fully adapted  (Doxaran et al. , 2016). The lower retrieval accuracy of a phy (443) observed in inland waters compared with the results obtained in marine waters partly resides into the relative a phy (  443 ) and a cdm (443) range values. For the Amazon dataset, the median a cdm (443) and a phy (443) values are 4. 03 and 0. 66 m -1 , respectively. In this case, a cdm (443) contributes to more than 85% of a nw (443), making the retrieval of a phy (443) from the a nw (443) spectrum very challenging. This is also true for the whole DS-IW dataset for which the median values of a cdm (443) and a phy (443) are 3.
Performance of 3SAA from match-up exercises (DS-Sat and DS-Argo) Using the first matchup dataset (DS-Sat), which combines MERIS, SeaWiFS, MODIS and VIIRS images, a good retrieval accuracy is found for all IOPs (Fig. 10 ). The MAPD (and MB) values for a nw , a phy , a cdm and Fig. 10. Same as Fig. 7  but for DS-Sat. The different colors and symbols represent the different OWCs. b bp at 443 nm are 28% (-0. 005 m -1 ), 31% (-0. 001 m -1 ), 48% (-0. 0059 m -1 ) and 26% (0. 0003 m -1 ), respectively. The MAPD and MB values for a phy (665) are 51% and 0. 0003 m -1 , respectively. While a nw and a phy are retrieved with about the same MAPD values as with the in situ validation dataset (DS-MW, Fig. 7 ), the MAPD values using DS-Sat instead of DS-MW increase by a factor of 1. 34 and 2. 0 for a cdm and b bp , respectively.
0005 m -1. This saturation pattern in ultra-oligotrophic waters was not observed for the in situ dataset (see Fig. 7 ), and could be attributed to uncertainties in the R rs (λ) retrievals. 3SAA was also tested using the DS-Argo dataset which combines OLCI R rs (λ) data and in situ b bp (700) measurements. Because of the very low R rs signal in the red part of the spectrum in oligotrophic waters, b bp (700) was calculated from the OLCI-derived b bp (443) retrievals and the estimated b bp spectral slope, Y bbp , assuming that b bp (λ) follows a power law spectral model  (Morel and Maritorena, 2001; Reynolds et al. , 2001; Stramska and Petelski, 2003; Loisel et al. , 2006; Slade and Boss, 2015; Reynolds et al. , 2016). Deviations from this monotonic spectral pattern have been observed in coastal waters, and was generally attributed to absorption effects and improper calibration  (Slade and Boss, 2015).
Three different approaches have been examined to calculate Y bbp from OLCI observations. First, because b bp (λ) is estimated independently at each wavelength from LS2, Y bbp can be directly calculated from a linear regression between Log(b bp (λ)) and Log(λ), where λ is 443, 490, 510, and 560 nm. For the present dataset, this approach provides a median Y bbp value of 1. 24 with a coefficient of variation of 48%. Two empirical relationships between Y bbp and the blue to green reflectance ratio  (Lee et al. , 2009)  or chlorophyll concentration  (Morel and Maritorena, 2001)  were tested to extrapolate to 700 nm. The first of them is the parameterization of  Lee et al. (2009)  adopted in QAA-v6 and GIOPs default configuration (as described in  Werdell et al. , 2013). It provides a higher Y bbp median value (1.
97) with a much lower coefficient of variation (11. 1%) over the present dataset (Fig. 11 ). This pattern is explained by the fact that for a blue to green reflectance ratio higher than about 4, which represents a large part of the open oceanic waters, the Y bbp values estimated from QAA-v6 saturate at a value of ~2. 0. At last, the Chl-based parameterization of  Morel and Maritorena (2001) , based on theoretical considerations, provides a much lower median value (0. 63) and a coefficient of variation of 32% (Fig. 11 ). In both cases, the retrieved Y bbp is too low for oligotrophic waters  (Antoine et al. , 2011). The application of these three different algorithms on a monthly OLCI image (May 2018) clearly shows some spatial discrepancies between the 3 considered models (Fig. 12 ). While Y bbp saturates over a large part of open ocean waters using band ratio relationships (Fig.
Comparison between 3SAA, GSM and QAA with the in situ datasets The performance of the 3SAA algorithm is compared to that of QAA v6 and GSM-GC on the coastal and open waters dataset (DS-MW). This exercise is not performed for the inland water dataset as these models have only been developed for marine waters. Previous studies have indeed clearly shown the necessity to adapt QAA for inland waters  (Mishra et al. , 2013; Li et al. , 2016; Ogashawara et al. , 2016; Rodrigues et al. , 2018). The results of this inter-comparison are presented at 443 nm and at 665 nm for a phy. In general, the 3 algorithms show a good performance for the retrieval of all IOPs, and are able to retrieve a nw (443) and b bp (443) for all data points (1309 for a nw (443) and 11,292 for b bp (443)). The number of retrievals of a phy (443) decreases to 94% (72 negative solutions on 1139 data points) for QAA-v6, while no negative solutions are obtained with 3SAA or GSM.
Due to its inherent characteristics, 3SAA will also be usable to process data delivered from future hyperspectral missions such as the NASA Plankton, Aerosol, Cloud, ocean Ecosystem mission  (Werdell et al. , 2019). Nevertheless, further work is required to test the neural network algorithm used to assess <K d (λ) > 1 from hyperspectral data, and to properly set the spectral constraints used to partition a nw (λ) into its different sub-components. As a final remark, the source code in Matlab can be asked directly via e-mail, and the final version, with a more user friendly interface, will be made available in the IOCCG platform. A SNAP toolbox is also available in: https://gitlab. eumetsat. int/eumetlab/oceans/ocean-science-studies /olci-iop-processor/-/tree/master. This software, which include the C source code, is freely available under the term of GNU GPL3 License.
The variability of each IOPs estimated from 3SAA is consistent with expectations and with the DS-MW validation dataset used in the present study for marine waters. The geometrical mean IOP values, calculated over the global dataset (assuming a log normal distribution), are remarkably similar for 3SAA and GSM-GC: about 0. 022 m -1 , 0. 012 m -1 , 0. 01 m -1 , and 0. 00145 m -1 for a nw (443), a cdm (443), a phy (443), and b bp (443), respectively. The values of b bp (443) and a phy (443) are very close to those calculated from the Chl-based empirical relationships established by Antoine et al. (2011) (= 0. 0014 m -1 ) and Bricaud et al. (1998) (= 0. 014 m -1 ), respectively, using a mean Chl value of 0. 23 mg. m -3 obtained from the same image. The global similarity between 3SAA and GSM-GC is reflected into the low averaged MAPD values calculated between these two models: 7%, 20%, 20%, and 13% for a nw (443),.