dataesr/scientific-paragraphs-categorization huggingface dataset:
• Linear kernel: K(x, x ) = x T • x. (12) • Polynomial kernel: K(x, x ) = (γ • x T • x + r) d , ( 13 ) where γ is a scaling parameter, r is a constant term, and d is the degree of the polynomial. • Sigmoid kernel: K(x, x ) = tanh(γ • x T • x + r), (14) where γ and r are scaling and constant parameters, respectively. Where x and x are the input feature vectors, and K(x, x ) is the kernel function that measures the similarity between two input vectors in a higher-dimensional feature space. The performance of OCSVM with different kernel functions depends on the nature of the data and the problem being solved. The RBF kernel generally performs well for most datasets because it can model complex decision boundaries. However, the linear kernel may be more appropriate if the data are linearly separable.
The Proposed KPCA-OCSVM Anomaly Detection Approach The proposed KPCA-OCSVM method for identifying COVID-19 infections from blood test data involves training the model using only non-infected samples to extract features. This is done through the use of the KPCA algorithm, which is designed to learn and accurately describe non-infected blood test data and produce relevant features to aid in the training process of the OCSVM algorithm. The OCSVM is then employed to detect infected cases in the testing dataset, which includes both infected and non-infected observations. The features extracted by the KPCA are used as input for the OCSVM, which is sensitive to outliers in the training set (Figure  1 ). The OCSVM's objective function is responsible for determining the presence of a COVID-19 infection.
The time complexity of ICA depends on the algorithm used to perform the ICA. Popular algorithms, such as FastICA, have a time complexity of O(n 2 p), where n is the number of samples and p is the number of features. Other algorithms such as Infomax have a time complexity of O(np log n). However, it is worth noting that the time complexity of ICA can also depend on the implementation and specific details of the dataset, such as the number of independent components being estimated. Table  1  compares the main features of the three investigated dimensionality reduction methods, KPCA, PCA, and ICA.
The time complexity of LOF is O(n 2 ) for both the training and testing phase. Isolation forest (iForest): iForest is a tree-based anomaly detection algorithm. It builds a forest of isolation trees, where each tree splits the data based on a randomly selected feature and a random split value  [36]. The goal is to isolate anomalies by creating shorter paths for abnormal points, and longer paths for normal points  [37]. The iForest algorithm has a computational cost of O(tl log l) during the training phase and O(ntl log l) during the testing phase, where l is the subsampling size of the dataset, n is the number of samples in the dataset, and t is the number of trees in the forest, as reported in  [38]. It is worth noting that for optimal detection performance, l should be kept small and consistent across different datasets.
Dataset 1 The first dataset, referred to as Dataset 1, was gathered from 5644 patients at the Albert Einstein Hospital in São Paulo, Brazil, with 559 of them being COVID-19-positive patients, according to  [39]. More details about this dataset can be found in  [16, 39, 40]. Figure  2  illustrates the violin plots of the important 18 features and it can be observed that these datasets do not follow a Gaussian distribution.
Dataset 2 In this study, a second dataset was used to evaluate the performance of the proposed methods. This dataset, referred to as Dataset 2, was collected from three different sources and includes hematochemical values from 1624 patients at San Raphael Hospital (OSR) collected between February and May 2020, 58 cases from the Istituto Ortopedico Galeazzi (IOG) of Milan, and 54 patients from OSR in November 2018  [41]. Among the 1624 patients from OSR, 786 were infected with COVID-19 and 838 were uninfected, while 29 of the 58 cases from IOG were infected and 29 were uninfected. The third sub-dataset contains 54 patients from OSR who were not infected with COVID-19 but were used as confounding cases. The use of different instruments to collect samples and the presence of confounding cases make Dataset 2 more challenging compared to Dataset 1.
In this study, 11 important features were used to detect COVID-19 infection (Table  3 ). Figure  4  shows the violin plots of features in Dataset 2 and indicates that these datasets are not Gaussian distributed. The distribution of the blood test data, as shown in Figure  4 , is non-Gaussian, which poses a challenge for traditional dimensionality reduction techniques, such as PCA, which assume linearity and Gaussianity in the data. In this scenario, nonlinear techniques, such as KPCA, which do not have any assumptions on the data distribution, could be more effective. The pairwise Pearson correlation matrix of the features in Dataset 1 is displayed in Figure  5.
Here is the setting of KPCA (number of components = 5, kernel = 'RBF', gamma = 0. 1). We used the grid search approach to determine the optimal values of these parameters. In the training phase of the four anomaly detection techniques, we used the following parameter settings: isolation forest (number of estimators = 150, contamination rate = 0. 05), OCSVM (kernel = RBF, nu = 0. 001, gamma = 0. 05), local outlier factor (novelty detection = true, number of neighbors = 20, metric = 'Minkowski', contamination = 0. 1), and elliptical envelope (support fraction = 0. 25). All experiments were conducted on a laptop with an i3 processor running Ubuntu 20. 04. 4 LTS, with 8GB of RAM, to ensure a fair comparison. The methods were implemented using Python 3. 8, and the Keras and scikit-learn libraries, version 0.
22. As per Table  4 , the KPCA-OCSVMRBF, PCA-OCSVMRBF, and ICA-OCSVMRBF methods showed excellent detection performance with an AUC of 0. 99. They were followed by the ICA-based EE method with an AUC of 0. 82. The other methods did not provide satisfactory results. In terms of all metrics, the KPCA-OCSVMRBF, PCA-OCSVMRBF, and ICA-OCSVMRBF detectors performed the best in detecting COVID-19 infection based on Dataset 1. The results showed that combining PCA, ICA, and KCA with OCSVM RBF provided better performance than the other combinations of dimensionality reduction techniques and OCSVM kernels. This indicates that using OCSVM with an RBF kernel with PCA, ICA, and KCA can effectively capture the non-linear relationships between the data points, resulting in improved anomaly detection performance.
To enhance the evaluation of the proposed KPCA-OCSM approach's detection performance, we utilized the bootstrap method to calculate confidence intervals for performance metrics. This involved generating "nboots" bootstrap sample datasets; each was the same size as the original test set. To create these sample datasets, instances were randomly drawn from the test set with replacements. Evaluation metrics were calculated for each sample dataset, and the 95% confidence interval was determined by computing the 2. 5th to the 97. 5th percentile among the "nboots" calculated metric values. In this study, we utilized the bootstrap method with 400 boots to calculate the 95% confidence intervals for 5 evaluation metrics based on the test data. Figure  6  presents the histogram of the 400 boot results, depicting a 95% confidence interval.
99, respectively, means that the model's performance on the test data is excellent. However, it is important to note that the confidence intervals calculated using the bootstrap method are based on a range of values that the evaluation metrics could take in repeated sampling from the same population. The confidence intervals give an estimate of the precision of the estimates, taking into account the variability in the sample data. In this case, the 95% confidence interval of accuracy and AUC was between 92% and 98%, while the precision was between 89% and 98%. The perfect scores obtained from the real testing data fall outside the range of these confidence intervals. This could be because the sizes of the testing data were relatively small. In the second experiment, we evaluated the effectiveness of the KPCA-OCSVM model in detecting COVID-19 infection using Dataset 2.
Comparison with the Existing Methods In this study, the performance of the developed KPCA-OCSVM detector was evaluated against a variety of current techniques applied to both Dataset 1 and Dataset 2, as detailed in Table  6. Previous studies, such as the one in  [40] , employed supervised machine learning techniques, including RF, ANN, logistic regression, and lasso-elastic-net regularized generalized linear (GLMNET) models, to predict SARS-CoV-2 infection. While ANN achieved the best classification results with an AUC of 0. 95, other studies, such as  [19, 42] , also applied various supervised machine learning techniques, such as NN, RF, GBT, SVM, MLP, RT, BN, and Naive Bayes. Despite the high overall accuracy of 95. 159% achieved by BN, these methods all require labeled data for classification.
Refs Dataset Model Metrics  [40]  Dataset 1 RF, LR, GLMNET, and ANN AUC = 95  [42]  Dataset 1 NN, RF, GBT, LR, and SVM AUC = 85  [19]  Dataset 1 MLP, SVM, RT, RF, BN, and NB Acc = 95. 15% Sens = 96. 8%, Spec = 93. 6% [16]  Dataset 1 XGBoost AUC = 99. 38 KPCA-OCSVM Dataset 1 KPCA-OCSVM AUC = 99 In summary, the proposed KPCA-OCSVM method offers several benefits, making it a promising approach for detecting COVID-19 infection using blood test data. Firstly, it is a semi-supervised approach, which only requires data from healthy cases during training. This makes it easier to implement and less time-consuming than supervised methods that require labeled data, which can be challenging to obtain in practice. Moreover, labeling data can be subjective and prone to errors, making semi-supervised methods a more robust alternative.
Essentially, the importance of identifying the most important variables for COVID-19 infection detection lies in the ability to develop more accurate diagnostic tools. By combining the most important variables, researchers can develop diagnostic tests that are more accurate and efficient in detecting COVID-19 infection. Figure  8  shows the bar plot of the mean absolute SHAP values for each variable based on Dataset 1. From Figure  8 , we can observe that the most important feature for detecting COVID-19 infection in Dataset 1 is 'Leukocytes'. Indeed, 'Leukocytes' are white blood cells that play a crucial role in the body's immune system response, so it is not unexpected that they are essential in predicting COVID-19. 'Platelets' is identified as the second most important variable in detecting COVID-19 infection, as shown in Figure  8.
Similarly, we investigated feature importance based on Dataset 2; Figure  9  displays the bar plot of the mean absolute SHAP values. Based on Dataset 2, the eosinophil count was identified as the most important variable in predicting COVID-19. This indicates that the eosinophil count may play a significant role in distinguishing between COVID-19 patients and non-COVID-19 patients in that dataset. From Figure  9 , we can see that aspartate aminotransferase (AST), white blood cells (WBC), and lymphocyte count (LYT) were also identified as important variables in predicting COVID-19. This suggests that these variables may also play a role in discriminating between COVID-19 patients and non-COVID-19 patients in that dataset. However, the exact impact of these variables on predicting COVID-19 may vary in different datasets or models, and additional research may be needed to fully understand their relationships with COVID-19.
Conclusions This study proposes a novel method for detecting COVID-19 infection using blood test data as part of an anomaly detection problem. The KPCA-OCSVM approach combines KPCA and semi-supervised OCSVM techniques to detect infected cases without requiring labeled data. The KPCA method identifies nonlinear patterns, while the OCSVM measures dissimilarity between normal and abnormal features. The approach outperformed other semi-supervised models when tested on two datasets of blood test samples from hospitals in Brazil and Italy. The proposed method shows promise for detecting COVID-19 infection using blood test data without labeled data. While the proposed approach for detecting COVID-19 infections based on blood test data shows promise, some limitations need to be addressed. One of the main limitations is that the study only tested the approach on two sets of blood test samples from hospitals in Brazil and Italy.
Therefore, it is necessary to validate the approach on more diverse datasets from different regions and populations to ensure its generalizability and reliability. Moreover, incorporating deep learning techniques, such as deep generative models, could potentially improve the ability of the method to detect anomalies in the data. This could include using generative adversarial networks (GANs)  [48]  to learn more complex representations of the data. These deep learning techniques have been shown to be effective for anomaly detection in other domains and could be useful for detecting COVID-19 infections based on blood test data. Additionally, incorporating other relevant features, such as demographic information and medical history, could also be explored to improve the accuracy of the model.
The Western Channel Observatory (WCO) comprises a series of pelagic, benthic and atmospheric sampling sites within 40 km of Plymouth UK, which have been sampled by the Plymouth Institutes on a regular basis since 1903. This longevity of recording and the high frequency of observations provide a unique combination of data; for example temperature data were first collected in 1903 and the reference station L4 has been sampled on a weekly basis since 1988 where nearly 400 planktonic taxa have been enumerated. While the component datasets have been archived, here we provide the first summary database bringing together a wide suite of the observations. This provides monthly average values of some of the key pelagic and benthic measurements for the inshore site L4 (50° 15. 00' N, 4° 13. 02' W approx depth 55 m), the offshore site E1 (50° 02.
and water column (from 2012). These data have varying coverage in time and depth resolution. The metadata tables describe each data set, provide pointers to the source data and other related Western Channel Observatory data sets and outputs not compiled here. We provide summaries of the main trends in seasonality and some major, climate related shifts that have been revealed over the last century. The data are available from Data Archive for Seabed Species and Habitats (DASSH) via the link http://doi. org/10. 17031/645110fb81749  (McEvoy and Atkinson, 2023). Making the data fully accessible and including units of both abundance and biomass will stimulate a variety of uptakes. These may include uses as an educational resource for projects, for models and budgets or for analysis of seasonality and long-term change in a coupled benthicpelagic system and for supporting UK and Northeast Atlantic policy and management.
The Western Channel Observatory (WCO) data contains an unprecedented collection of parameters both in terms of longevity and variety. Investigation of the marine environment in the western English Channel off Plymouth began with the opening of the Marine Biological Association (MBA) laboratory in 1888. Given the importance in the area of the pelagic fishery the remit focused strongly on research in applied fisheries. Initial studies centered on the eggs and larvae of commercially important fish. With the advent of the International Council for the Exploration of the Sea (ICES) and a growing realisation that hydrography had an influence on biological communities, plankton surveys and hydrographical measurements were soon added  (Southward et al. , 2005; Southward & Roberts, 1987). In the decades that followed, observations were expanded with the creation of stations E1(50° 02.
These stations experience classic, albeit highly variable seasonal production cycles with spring and autumn phytoplankton blooms. Figure  2  compares the key WCO sites in relation to the wider summer pattern of stratification (Fig. 2a ) and to the longer trend of climatic cycles across the North Atlantic  (Bode, 2023)  highlighting a recent phase of intense warming over the last 4 decades (Fig. 2b ). These environmental changes, and the response of the biota, are described more fully in Section 5 using plots derived from our summary database.
Objectives The individual data sets of the WCO are valuable, but differing levels of reporting and formatting hamper their use and prevent integration. Many are currently available through data repositories such as British Oceanographic Data Centre (BODC) and Data Archive for Seabed Species and Habitats (DASSH), however, some are lodged with individual scientists. To improve their overall utility, the various component data sets need to be brought together into a single format. We have done this here for the first time, but to make this project tractable we have summarized the core datasets as monthly averages, and for broad functional groups. This level of resolution (coarser than some of the measurements, which can be weekly and for individual species), was chosen as a first step to allow timely completion of this initiative, to provide a summary database that combines many diverse data sources.
This data paper combines in a single spreadsheet most of the key variables which have good seasonal or longer-term coverage (Table  1 ). Specialists who wish to access the underlying high-resolution observations, data for individual species, who require the most recent data available, or require other data sets not summarized here are directed to our WCO data catalogue: https://www. westernchannelobservatory. org. uk/data. php. This catalogue provides sampling details, doi's of the most recent versions, and points of contact for specific data sets. Additional information is also available in Table  A1  and Table A2. This data paper is aimed towards scientists who may not need weekly resolution or species-specific data, but who wish to compare the monthly-averaged physical, chemical and biological data.
Biological data are provided in units of both abundance and biomass, to enhance their utility for modelling. We have also made the spreadsheets as user-friendly and simple as possible to be of help as an educational resource at the undergraduate level. This data paper describes the database (Section 4); illustrates its utility to examine seasonality and longer-term trends while summarizing previous work on these topics (Section 5); provides a broader-scale context for the WCO (Section 6) and finally provides practical advice on the strengths, limitations and how to use and cite this database (Sections 7 and 8).
Data processing This paper consolidates 22 individual and diverse data sets using monthly averages. Data with comprehensive seasonal coverage which span at least two years are included. Detailed information on sampling and analysis protocols plus data coverage can be found in the Appendices (Table  A1  and A2 ). It is essential to read these appendices before extracting data to avoid errors, for example in distinguishing between zeros and absent data. A zero represents a parameter that was either looked for and not found (for plankton data) or was below the detection limit (nutrient data). A blank cell, by contrast, is where there are no data available for that particular month. To benefit models, budgets and size-based approaches, biotic data are reported both in units of abundance and biomass.
The only exception is the smallest plankton measured by flow cytometry. These use fixed conversion factors for the whole functional group, and have multiple groups and depths. Therefore to remove the complexity of having many data fields that are simple multiples of others, these are reported only as abundance per millilitre. Median cell diameters are provided, which enables estimation of biomass based on the volume of a sphere and carbon values from the literature (Table  2 ). Median cell diameters were derived by collecting seawater samples, filtering them sequentially through a series of membrane filters, analysing the filtrates by flow cytometry and the percentage of cells remaining plotted as a percentage of unfiltered seawater against filter pore size  (Burkill et al. , 1993).
Overall seasonality: E1 and L5 Fig. 4  summarises the data available for the E1 and L5, sites further offshore than L4. All measurements except those from the Young Fish Trawl and the shark catch data pertain to the E1 site. The Young Fish Trawl data are from site E1 and L5 combined. The shark data are from angling vessels from Looe and within 10 miles of E1. The E1 site is more strongly stratified than L4, as evidenced by slightly higher surface temperatures and a bigger summer temperature difference between surface and depth. Being further offshore than L4 and receiving less riverine nutrient input from the rivers Tamar and Plym macronutrient concentrations are more severely limiting in summer and indeed, iron stress in some seasons has been suggested  (Schmidt et al. , 2020). This is also reflected in the stronger reduction in DIC during the stratified period, resulting in an average seasonal amplitude of 83 µmol kg -1 at E1 compared to around 55 µmol kg -1 at L4.
Although phytoplankton and zooplankton samples are currently collected at E1 we have not summarised them in this paper because the available time series data does not cover as long a period as L4. However, a summary of phyto-and zooplankton seasonality at E1 is presented and compared with that of L4 by  Djeghri (2018). These authors showed that mesozooplankton biomass at E1 is lower than at L4 and at deeper, offshore sites in the Celtic Sea  (Giering et al. , 2019). Also in the context of these Celtic sea stations,  Schmidt (2020)  examined nutrient dynamics at E1 in relation to pico-and nanoplankton, and found that late season dominance of the picocyanobacterium Synechococcus (including intense blooms in some years) tended to follow summers of particularly severe nitrate stress. Data compiled here from the 1m 2 , 700 µm mesh Young Fish Trawl (Fig.
Calanus egg production experiments at L4 Although rate process measurements have periodically been made at the WCO, such as primary production  (Barnes et al. , 2015)  and grazing  (Bautista & Harris, 1992; Fileman et al. , 2010) , measurements of Calanus egg production rate have been made fairly consistently since 1992 (Fig. 7 ). This makes it one of the longest zooplankton production time series of its kind  (Harris, 2010)  and offers a valuable insight both into food quantity and quality for grazers and into the population dynamics of Calanus helgolandicus  (Green et al. , 1993; Irigoien et al. , 2000). While the original weekly data are archived at BODC, the monthly values averaged here (i. e. a mean of the component weekly mean rates) provide a good seasonal and long-term comparison with the respective monthly average water temperature and functional groups of phytoplankton.
Carbonate chemistry measurements Over a decade worth of data is now available for the carbonate chemistry at both L4 and E1, which has been used to provide evidence for a number of assessments relating to ocean acidification, including the OSPAR QSR2023 OA Assessment   et al. , 2023). Interestingly, aragonite saturation state shows no significant trend at L4, most likely resulting from the concomitant decline in both DIC and TA, but also the high level of variability in TA caused by organic alkalinity inputs from local rivers. At station E1, there has been a greater decline in DIC (-1. 35 ± 1. 12 µmol kg yr -1 at E1 vs. -0. 78 ± 0. 72 µmol kg yr -1 at L4), similar decline in TA and a slightly slower decline in pH (when including 2021 data: -0. 008 ± 0. 0022 yr -1 ). Since autumn 2017 additional water column measurements have been taken at L4, which provides a profile view of the carbon dynamics.