dataesr/scientific-paragraphs-categorization huggingface dataset:
A. DATA This CKD dataset used in this study was released by Apollo Hospitals, Tamil Nadu, India, in July 2015 and is available in the UCI machine learning repository  [22]. The dataset consists of samples collected from 400 patients with 24 attributes (13 nominal and 11 numerical attributes). The 24 attributes are age, blood pressure, specific gravity, albumin, sugar, red blood cells, pus cell, pus cell clumps, bacteria, blood glucose random, blood urea, serum creatinine, sodium, potassium, hemoglobin, packed cell volume, white blood cell count, red blood cell count, hypertension, diabetes mellitus, coronary artery disease, appetite, pedal edema, and anemia. The description of the dataset is given in Table  1. Based on these available clinical attributes 250 cases were classified as CKD and the remaining 150 were classified as non-CKD.
B. PRE-PROCESSING We have pre-processed the unrefined medical data by removing the missing values to enhance prediction capabilities. We have also conducted data-transformation to make them useful for the machine learning models, which are limited to process non-numerical data. The non-numerical data in the dataset are in the form of 'present', 'not present', 'normal', 'abnormal', 'yes', 'no', 'good', and 'poor'. The non-numerical data are identified and transformed into numbers. The 'normal', 'present', 'yes', and 'good' values for nominal attributes are replaced by '1' and 'abnormal', 'notpresent', 'no', and 'poor' values are replaced by '0'. Missing values are concomitant to real-world data. Ignorance of the record that contains the missing value is the simplest form of a solution, which is a less-desirable practice, especially for small dataset.
2) GRADIENT BOOSTING (GB) The gradient boosting algorithm  [31]  produces a prediction model that consists of an ensemble of weak prediction models. This means a collection of individual models results in a final model. The model is built in a stage-wise fashion. The individual models have poor prediction power and suffer over-fitting problems, but the ensemble of these models provides improved results. The individual models in the ensemble are not built on completely random subsets of training data but by putting more weights on the wrong predicted VOLUME 9, 2021 TABLE 1. Details information of the attributes of the UCI CKD dataset  [22] ,  [27]. samples; in other words, instances that are hard to predict will be more focused during model training by taking into account the past mistakes.
D. MODEL INTERPRETATION FOR FEATURE SELECTION Classification models map a test instance to output and most of the cases provide a single metric, such as classification accuracy. However, this is not a complete description of why the model made this correct prediction. Sometimes it is useful to determine how the prediction was made and the role of the individual features in making this decision. Model interpretability can let us know the feature importance of the model. We can also understand predictions from methods by attributing importance values to each input feature. To understand the classifier's overall behaviour, these important values can be computed either for a single prediction, or an entire dataset. We calculated feature importance using the training dataset. SHapley Additive exPlanations (SHAP) technique, proposed by Lundberg and Lee  [36] , has been shown to be effective for identifying important features in the dataset.
A. MODEL TRAINING AND FEATURES SELECTION Among the ten-folds-split data nine folds were used to train all the classifier models. Then the remaining 10th-fold data was used to evaluate the classifiers. Thus we had 360 samples for the training dataset and 40 samples for the test dataset. The training dataset contained 90% of the total samples, and the testing dataset contained 10% of the total samples. There were 225 CKD cases and 135 non-CKD cases in the training nine folds dataset, and in the remaining test one fold dataset, there were 25 CKD cases and 15 non-CKD cases. Six evaluation matrices were measured for each classifier to observe and compare the performances of the models. According to the evaluations matrices RF classifier showed the highest predictive performance for the test datasets with an average classification accuracy of 99.
50%, sensitivity of 98. 75%, specificity of 100%, precision of 100%, F1 score of 99. 35%, and AUC of 99. 38%, shown in Fig-  3(a). The GB and XGB classifiers showed almost similar classification performances. The GB classifier obtained classification accuracy of 99. 00%, sensitivity of 98. 79%, specificity of 99. 21%, precision of 98. 66%, F1 score of 98. 71%, and AUC of 99. 00%, and the XGB classifier obtained classification accuracy of 98. 75%, sensitivity of 98. 20%, specificity of 99. 25%, precision of 98. 62%, F1 score of 98. 34%, and AUC of 98. 72%. The classification performances for LR and SVM are relatively lower than the performances of the RF, GB, and XGB classifiers, shown in Fig. 3(b). Global feature importance was computed in terms of SHAP values. After training the classifiers with the training data sets including all the 24 attributes, SHAP values of every attribute were calculated to understand the feature importance of the classifiers.
Fig. 4 (a) shows the feature importance values obtained from RF classifier trained on fold-1 training data set. The magnitude of the feature values is color-coded from black to copper for the feature values from low to high, respectively. The features are vertically sorted according to their average impact on the predictions. From Fig. 4 (a), it is shown that the first 13 features are considered as the most important feature. The feature importance values were calculated for classifiers using all the training data sets. Fig. 4 (b) depicts the normalized mean SHAP value magnitudes obtained from each of the RF, GB, and XGB models using all the training set. The RF, GB, and XGB classifiers were considered based on their better performance than the LR and SVM classifiers. According to Fig. 4 (b), hemo, sg, sc, al, pcv, rbcc, htn, bgr, dm, age, sod, bu, and bp attributes had the highest impact on the RF, GB, and XGB classifier's decision making on whether the instance is CKD or NOT CKD.
The identified important features were then used to reduce the dimensionality of the dataset and minimize the costs and efforts of the diagnosis of CKD with perfect accuracy. The selected features had been used to produce a new lower-dimensional dataset than the original dataset. Hence, after discarding the less important features, the SHAP-based feature identification method reduced the dataset dimension to 13 attributes, which were further subjected to build six different datasets based on the different pathological tests in order to observe the predictive performances of ML models on the different combinations of the attribute sets.
B. INTERPRETATION OF THE IMPACT OF ATTRIBUTION INTERACTIONS ON CKD PREDICTION The interactions of the attributes on prediction offer more in-depth insight into models' decision making capabilities. The SHAP dependence contributions between attributes were observed to analyse the impact of attribute interaction on the model's decision making capabilities. The dependence contribution plots for the hemoglobin and specific gravity attributes is shown in Fig. 5(a) , where the x-axis represents hemoglobin level and the y-axis represents SHAP value for hemoglobin, and the color bar represents the values associated with specific gravity. Thus each dot in the plot represents each value of the hemoglobin attribute in the training dataset. Lower values of both hemoglobin and specific gravity made a higher impact on CKD prediction, though the interaction of the lower value of hemoglobin with some higher value of specific gravity produced higher SHAP value for hemoglobin.
C. REDUCED DATASET AND MODELS EVALUATION Based on the calculated SHAP values of the attributes with the RF, GB, and XGB models, 13 attributes were selected to reduce the dataset. The selected attributes are shown in Table  2. We created six different datasets by taking attributes from the selected attributes and based on test pathologies, shown in Table  3. Dataset 'DB-I' consists of all the selected 13 attributes. 'DB-II' dataset consists of attributes collected from blood and other pathological tests, including hemo, sc, pcv, rbcc, bgr, sod, and bu from the blood test and htn, dm, age, and bp from other tests attributes. Database 'DB-III' consists of urine test attributes, sg and al, and 4 other tests attributes. 'DB-IV' includes blood test attributes only, 'DB-V' includes urine test attributes only, and 'DB-VI' includes other test attributes excluding both blood and urine test attributes.
All the models were trained using the samples that were previously used to train the models for SHAP calculation. The models were tested using the test datasets that had not been previously introduced to the models. The datasets and models were evaluated for classification accuracy, sensitivity, specificity, precision, F-score, and the area under the curve (AUC), and results obtained are shown in Fig. 6. The RF model presented the highest classification accuracy of 99. 00% among other classifiers with the 'DB-I' dataset, whereas the accuracy of 98. 25%, 98. 50%, 97. 25%, and 97. 75% were obtained using GB, XGB, LR, and SVM classifiers, respectively. The 'DB-II' dataset consisted of selected blood and other test attributes including 11 attributes (7 blood and 4 other). RF gave the highest CKD classification performances with the accuracy of 97.
75%, sensitivity of 96. 12%, specificity of 98. 82%, precision of 97. 94%, F-score of 96. 88%, and AUC of 97. 47%. The GB classifier obtained an accuracy of 96. 75%, sensitivity  of 96. 12%, specificity of 97. 28%, precision of 95. 43%, F-score of 95. 55%, and AUC of 96. 70%. The performance of XGB was a little bit higher than the GB, LR, and SVM classifiers with the classification accuracy of 97. 00%. For the 'DB-III' dataset, both GB and XGB performed better than the RF classifiers with an accuracy of 97%. However, RF detected CKD with the highest specificity and precision than other classifiers. For the 'DB-IV' dataset, RF again achieved the highest classification accuracy of 97. 25% than other classifiers. The models showed relatively lower classification accuracy with the 'DB-V' and the 'DB-VI' than the other datasets, where RF achieved a classification accuracy of 88.
50% and 86. 25% using the 'DB-V' and the 'DB-VI' datasets, respectively. Database evaluation for the RF classifier, which showed better classification for most of the cases, is shown in Fig-  7. The database DB I showed the best CKD detection performance which is almost similar to the full database including 24 attributes. However, DB II, DB III, and DB IV also showed considerable classification performances for CKD detection. The results indicate that using all 24 features did not bring additional advantages in the diagnosis process, but took time.
V. CONCLUSION This paper has identified a reliable method for CKD classification and attributions selection with improved simplicity and cost effectiveness. First, we trained and selected suitable classifiers, calculated the feature importance based on SHAP values, and obtained a reduced dataset based on the pathological tests and measured feature importance. Second, we trained the classifiers with these reduced data sets and evaluated them with the test datasets. The results of this analysis demonstrated that the SHAP-identified important features were consistent with the current clinical thinking. It also found that an RF classifier method provides significantly high classification accuracy with the pathologically categorized attributes sets. The proposed RF classifier and reduced test attributes can therefore be potentially applied to reduce diagnosis costs and enable better management of early treatment plans.
S. National Science Foundation (NSF), with all NSF principal investigators joining the EXPORTS Science Team (Table  1 ). The objectives of Phase 1 are to collect the necessary data to answer science questions 1 and 2, submit collected data to national repositories (SeaBASS and BCO-DMO) making them publicly available for all interested users, and conduct preliminary syntheses and modeling. Following conclusion of the field components of Phase 1 and submission of collected data to public repositories, NASA plans to solicit proposals for the Phase 2 synthesis and modeling effort with a focus on further synthesis of the field results from Phase 1 and their application to improve global models of carbon cycle impacts of NPP fates. In March 2020, the COVID-19 pandemic led to the decision to postpone the North Atlantic deployment until the spring of 2021.
The parameter working group report (EXPORTS Science Team, 2020) provides short methodological descriptions for each parameter and the master table of parameters and data availability can be accessed at https:// sites. google. com/view/oceanexports/home. Data generated during EXPORTS follow the NASA Earth Science Data and Information Policy (http://science. nasa. gov/earth-science/earth-science-data/data-informationpolicy/), which requires data to be available from a designated long-term public data repository within a year of collection. NASA-funded primary data products are archived at SeaWiFS Bio-optical Archive and Storage System (SeaBASS). All EXPORTS data are being archived under one digital object identifier (DOI: http://dx. doi. org/10. 5067/SeaBASS/EXPORTS/DATA001) that further expands into the individual data subsets.
NSF-funded primary data products are archived and distributed by the Biological and Chemical Oceanographic-Data Management Office (BCO-DMO). The BCO-DMO EXPORTS data-products webpage provides access to all the NSF-funded EXPORTS projects and links to the individual data set (https://www. bcodmo. org/program/757397), where each individual data set has a unique doi. Data sets that cannot be handled Rates of primary productivity (GPP, NPP, NCP) are determined via the incorporation of isotopes (stable and radioactive) over 6-and 24-h incubations to measure uptake of dissolved inorganic carbon, nitrogen and silicon; the production of biogenic gases (e. g. , oxygen) to estimate GPP, PP and NCP. Respiration metrics are determined for bacterial and community respiration by O 2 drawdown assays and DOC remineralization assays.
Estimates of MLD were determined using CTD/rosette data from both ships as the first depth where the potential density exceeds the density at 50 m by 0. 1 kg m -3 (Figure  15D ). Values of MLD varied from 20 to 35 m with a mean depth of 31 + 4 m (n ¼ 227). Changes in time show a temporal pattern roughly similar to SST, where higher SSTs appear to correspond to shallower MLD determinations. Daily integrated rates of incident PAR fluxes from the R/V Revelle varied nearly four-fold over the experiment (Figure  15E ). Integrated daily incident PAR values were derived from the underway PAR data, collected every 15s on each ship, that have been cross-calibrated to the Compact-Optical Profiling System (C-OPS, Biospherical Instruments, Inc) deck sensor data before integration from local sunrise to sunset.
Euphotic zone depths were estimated using depths of 1% incident PAR (Z eu ; Figure  15G ). Values of Z eu were determined from both ships using both PAR sensors mounted on the CTD/rosettes and hand-deployed spectroradiometer casts using C-OPS. These data were available from each ship, although C-OPS profiles were not conducted as often due to adverse weather conditions. Values of the vertical attenuation of PAR were calculated via robust linear regression over the depth range of 10-80 m and converted into estimates of the depth of the 1% incident PAR isolume (also assuming a 4% loss at the air/ water interface). Vertical attenuation coefficients for PAR were averaged if multiple estimates were available for a particular day. Values of Z eu ranged from 70 to 90 m with a good deal of variability with mean 1% PAR depth of 78 + 6 m (n ¼ 149; Figure  15G ).
Next steps We have presented background and goals of the EXPORTS program, highlighted the logistics and operations of the 2018 field campaign in the NE Pacific, and provided a synthesis of oceanographic context during the cruise to set the stage for manuscripts on various core data sets for this special collection in Elementa. Documentation of the accomplishments of the EXPORTS team during the 2018 NE Pacific field campaign, accompanied by the online availability of likely one of the most comprehensive coupled biogeochemical-ecological-oceanographic data sets collected to date (https://sites. google. com/view/ oceanexports/home; EXPORTS Science Team, 2020), is critical for NASA's implementation of an EXPORTS Phase 2 program (see timeline in Figure  3 ). The papers in this special collection of Elementa are a first step towards answering the EXPORTS science questions and delivering on its promise to develop a predictive understanding of the biological pump.
Acknowledgments The  EXPORTS Science Team would like  to acknowledge support from the  NASA   Ocean Biology and Biogeochemistry program  and the  National Science Foundation Biological and Chemical Oceanography programs. Thanks go to the captains and crews of the  R/Vs Revelle and Ride  and to the  Ship Operations  staff of the  Scripps Institution of Oceanography. We are grateful for the support of our collaborators from  Canadian Line P program  and OOI. Special thanks to  Paula Bontempi  (formally  NASA HQ ),  Laura Lorenzoni  ( NASA HQ ),  Mike Sieracki  ( NSF BIO-OCE ), and  Quincy Allison  and his team ( NASA AMES ) for their vision and support throughout the development and implementation of the EXPORTS field campaign. Funding DAS, NN, KB, EF, SK, AB, AM, UP:  NASA 80NSSC17K0692. MJB, EB, JG, LG, KH, LKB, JF, NH:  NASA 80NSSC17K0568.
6 ), likely because the signal has been interfered with owing to the addition of terrestrial influx, as evidenced by the weak but positive correlation between microcharcoal and the relative abundance of terrestrial material (e. g. r = 0. 27). In the Jurassic, precession cycles had periodicities that averaged around ~20 kyr and obliquity cycles that averaged around ~35 kyr  10, 68, 69. The new 100 kyr eccentricity tuned data in the time Fig. 4  Multi-taper (MTM; 2π) power spectrum of the macrocharcoal record ((Si+Ti+Al+Fe) XRF /Ca XRF normalized) in time domain. This power spectrum was obtained in the Acycle software, with robust red noise models  100. Dominant peaks are shown at the obliquity (~46-29. 7 kyr) and precession (~20. 3-17. 6 kyr) frequency bands at 99% significance. The macrocharcoal record was tuned to the 100 kyr eccentricity of the elemental Ca record spanning the Margaritatus zone (see Supplementary Fig.
3 mbs, was sampled at a high resolution of ~10 cm for palynological processing, clay mineralogy, bulk organic carbon isotope analysis, TOC and CaCO 3. In total 151 cubes (~2 × 2 cm) were cut from the core sections or, where the correct depth was present in already cut and bagged sample from previous studies, this material has been used instead. All samples were split in half along the depth axis. One part of the sample was used for charcoal and palynofacies analysis and the other half for XRD and mass spectrometry. Clay minerals were identified using their main diffraction (d001) peak and by comparing the three diffractograms obtained. The MACDIFF 4. 2. 5. software  97  was used to estimate the proportions of each clay mineral on glycolated diffractograms. The identification of the clay minerals further follows the methods in Deconinck et al.
The remaining residue was sieved through a 125 μm mesh, and both fractions retained to separate the microscopic and macroscopic fraction of the charcoal, and the fine fraction sieved again at 10 μm, with the larger part of this retained. Macroscopic charcoal (>125 μm) was counted using a Zeiss Stemi microscope (10 × 4 × magnification) with top lighting from a 'goose necked' light source. The entire >125 μm sieved residue sample was dispersed in a Petri dish filled with DI water and the number of charcoal particles present counted. The number of charcoal particles is expressed per 10 g of processed rock (n/g) in raw data format. If large unprocessed clusters of sediment were present in the sample, they were taken out and weighted after being dried. The dry weight of this unprocessed sediment aggregation has been deducted from the original sample weight.
A total of 139 samples have been analysed for macroscopic charcoal. A known quantity (125 μl) of the <125 μm but >10 μm of sieved residue was mounted onto microscope slides using glycerine jelly and glycerate liquid. Microscopic charcoal was counted using an Olympus (BX53) transmitted light microscope (40 × 10 × lens). Four transects (two transects in the middle and one on the left and right side of the coverslip) of each palynological slide were observed and the numbers of charcoal particles counted. These data were then scaled up to the known quantity of the sample  98. A total of 144 samples were analysed for microscopic charcoal. Both size fractions of charcoals were identified according to the following criteria: opaque and black in colour, reflective of light, lustrous shine, often an elongated lath-like shape that shows sharp edges, original anatomy preserved, and fracture is brittle with splintery fragmentation  99  (Fig.
Statistical analyses. We used a Pearson correlation to test for possible correlation between the proxies and the significance using RMatlab2017b. The p value tests the hypothesis of no correlation against the alternative hypothesize of a positive or negative correlation, with the significance level at p = 0. 05. See Supplementary Fig. 5. Spectral analysis test for orbital cyclicity. Spectral and time-series analysis on the 934-951 mbs Mochras record of macrocharcoal, percentage illite, percentage terrestrial phytoclasts and CaCO 3 was aimed to test whether the visually observed lithological-scale fluctuations are paced by precession (~20 kyr) orbital parameters, and by comparison to the astrochronologically tuned Pliensbachian time-scale at Mochras. Data preparation and spectral and time-series analysis were performed using the program Acycle 100 for Matlab, version R2017b.
The proxy data-series were converted into a time series by tuning this interval to the ~100 kyr eccentricity cycle of the Ca record that exists for the Pliensbachian of Mochras  43. This dataset was originally tuned to the 405 kyr cycle by Ruhl et al. 43  , but for this interval we tuned the Ca-record of the Margaritatus Zone (~920-1015 mbs) to the 100 kyr eccentricity peaks, creating 19 tie points (See Supplementary Fig. 1 ). Using this age model, the power spectra were run in the time domain in the software Acycle 100 using the multi-taper method, time-bandwidth product 2 and the robust red noise model. The main periods (~17-20 kyr, ~21-27 kyr, ~30-50 kyr) observed in the CaCO 3 , illite, charcoal and percentage terrestrial phytoclasts, indicate the presence of precession and obliquity in the assessed proxy records.
Acknowledgements This is a contribution to the  JET  project funded by the  Natural Environment Research Council (NERC)  (grant number  NE/N018508/1 ). SPH, CMB, JFD, MR and TPH, acknowledge funding from the  International Continental Scientific Drilling Program (ICDP)  and TPH acknowledges funding from the  University of Exeter. SJB acknowledges previous funding from  NERC  ( NE/L501669/1 ) that provided the proof of concept data for this study. We thank the  British Geological Survey (BGS) , especially  James Riding ,  Scott Renshaw  and  Tracey Gallagher  for facilitating access to the Mochras core over an extended period. Magret Damaschke  and  Simon Wylde  are thanked for performing XRF scanning and for facilitating access to the  Core Scanning Facility. Also,  Clemens Ullmann  and  Mengjie Jiang  are thanked for their help in the Core Scanning Facility.