dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA CONSIDERATIONS Preparation of EWS for pelagic HABs requires region -specific expertise. Three case studies (the Phillipines, the United States of America, and Spain/Mediterranean region) are presented at the end of the chapter (Section 4. 9) detailing all steps and actions regarding the implementation of an EWS, and in Chapter 5 (Figure  5. 7) there is a schematic overview of the emerging monitoring and early warning system under development for the Norwegian waters. Following are the key parameters needed to establish effective and accurate EWS for pelagic HABs: • In situ physical and biological observations (wind direction and speed, water temperature, salinity, chlorophyll, dissolved oxygen) and in situ HAB-specific biological sampling and observations at the coast (HAB cells and toxins in water and shellfish samples).
Samples need to be collected from known HAB initiation sites. These data are collected by coastal community members, including Indigenous Peoples. • In situ HAB -specific sampling (with a bucket for surface samples, a Niskin bottle for deeper water samples, a hose for an integrate sample of the water column and a phytoplankton net to concentrate cells) and physical and biological observations via ships and buoys at offshore locations (HAB cells, toxins, water column temperature, salinity, and nutrients). With two potential source locations of HABs, offshore samples need to be collected at these sites during bloom season. • Transport and particle tracks from models, such as circulation-biogeochemistry hindcast/forecast models, incorporating carbon chemistry and including particle transport.
Plans need to be feasible, unbiased, suitable for a range of oceanographic conditions, types of shellfish harvesting, and practical for technical and logistical requirements. It is important to define the minimum but adequate monitoring requirements to achieve the EWS goals. What are the core variables linked to the adverse outcome? All the variables needed to complete the interpretation of phytoplankton abundance data should be included, so that the requirements of the legislation regarding risk evaluation and early warning can be fulfilled. A complete list of relevant information available to support the implementation and monitoring of toxic pelagic HABs is detailed in Appendix 1, Existing Resources for Biotoxin Monitoring, Management and Regulation of this Technical Guidance. Of particular interest for daily use with toxic pelagic HABS are: • Guide for designing and implementing a plan to monitor toxin producing microalgae (IOC Manuals and Guides No.
, 2012). Developing forecasting capability for the transport and impact of toxic Pseudo-nitzschia blooms will require sustained monitoring as well as additional efforts in the critical areas of basic research and model development. Where there is sufficient long-term data on Pseudo-nitzschia spp. and accompanying environmental information, it has been possible to develop predictive models of toxigenic blooms. These models can incorporate multiple forcing factors including time of year, chlorophyll, silicic acid, water temperature, upwelling index, wind index, cell densities, river discharge and nitrate predict the timing, but not intensity of bloom events. Improvements are still required to predict more accurately the timing of a bloom's appearance and its intensity. Some examples are: • In Ireland (Europe), Pseudo-nitzschia blooms off the coast were linked to upwelling; a statistical model using a wind index, water temperature and recent cell densities helped predict the timing, but not intensity, of bloom events  (Cusack et al.
There is still a need though to determine how to obtain and assimilate real-time information on phytoplankton and/or toxicity. The coarser-scale remote sensing model SeAHABS is also a part of this EWS. For the third component, the data and models have been made available at the HABHub web site (https:// habhub. whoi. edu/) for dissemination and communication primarily to management and regulators. For the last component, apart from the already existing closures as response to HAB events, the participatory risk assessment activity also identifies some avenues to help mitigate HAB impacts on the stakeholders through changes in Joint FAO-IOC-IAEA technical guidance for the implementation of early warning systems for harmful algal blooms 134 134 policies and governance aspects. This system is still very much in its infancy and many challenges have been encountered and still remain, especially its sustainability since this effort has been supported by a time-bound program funded by the government science agency the Department of Science and Technology.
Additionally, the HAB f INDEX allows for comparison between monitoring programs  (Clément et al. , 2020). Classical machine learning (ML) development has focused on new and more advanced algorithms that can analyze datasets in Excel. Each iteration contributes to the improvement of the algorithm architecture and hyperparameter data, commonly known as the "model centric approach". Furthermore, innovative ML methods include the new "data centric approach" which focuses on improving the data rather than solely focusing on the importance of the model. At Plancton Andino, their enhanced knowledge of the problem provides the ability to focus on data improvement. Currently a model is being developed that uses water data from fish farms and Programa Oceanografico y Ambiental en Salmonidos (Oceanographic and Environmental Program for Salmonids, POAS) monitoring programs.
Unfortunately, in most regions forecasting models of such accuracy are not available, or even when available, they need a robust dataset of in situ data to continuously test the validity of the outputs and improve model performance. The DISCO biogeochemical model is an atmosphere-ocean, biogeochemical simulation model. It is essentially a regional and local down-scaling of the operational global Navy Coastal Ocean Model (NCOM, ocean circulation), coupled to the CarbOn Silicate Nitrate Ecosystem (COSiNE) plankton model  (deRada, 2009; and references therein). The model is forced by atmospheric fields (wind, heat and water fluxes), and boundary conditions prescribed by NOAA global atmospheric models that are tuned using all available data from Global Meteorological Data ground observation. This regional biogeochemical model is configured as a threee-nest grid system: 1/12° Global > 1/25° Regional (Arabian Sea) >1/50° Coastal (Oman).
These data are available to end-users in Oman directly via automated transfer or via DISCO-EASy-GIS. Work is currently being done to develop ocean colour algorithms specifically to detect Noctiluca blooms. The algorithms are awaiting further testing before they are cleared for operational use. In addition to these datasets, the GIS is set up to automatically access, grab and transfer via FTP, other satellite datasets from National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA) and European Space Agency (ESA) data portals -specifically altimeter measurements of sea surface height. At present the DISCO archive of satellite imagery exceeds one terabyte and contains times series that date to 2000. The control and analysis centre of the operational system is the Environmental Analysis System-GIS (EASy-GIS) platform  (Tsontos and Kiefer, 2002, 2003 ) (Figure  5.
This information is stored in the GIS's relational database. Gridded data from spreadsheets, CSV files, and relational databases are imported manually into the GIS using EASy's database wizard. Examples of this type of data include not only measurements from water samples but information from sensors in the field such as CTDs, current meters, optical instruments or electronic tags. • Analysis files, which are shown at the bottom of the figure, store the results of processing and analyses. Examples of these results include images of seven-day forecasts calculated with the biogeochemical model, matching in space and time gridded data from field surveys with imagery to build habitat models of fish and to characterize the environmental conditions leading to HABs. The particle tracking interface of the GIS is used to trace the movement of water carrying algal blooms, the movement of water at intake and discharge lines of water treatment plants, and the movement of water at eddies and fronts.
All input and results files reside on either the PC's internal or external hard drives or its local area network. As shown in the top left corner of the figure, data flow into the GIS project is of course available over the internet. Four categories are listed as examples: • Imagery Servers provide satellite data and output from models including of course the DISCO biogeochemical model. As stated previously, the GIS provides an interface to schedule and automatically download. Imagery is stored in their original format and when called for display are translated into the temporal and spatial co-ordinate system of the GIS. EASy handles roughly 100 different imagery formats. • Sample Data are generally available as CSV or NetCDF files and transferred to the project's ACCESS relational database.
This suddenly increased stakeholder and public awareness led to establishment of HAB-Fish, a program focusing on blooms causing fish mortality. Much of the advisory contribution to monitoring and EWS development is derived from historical data on species, presence, and events. In this geographical approach, the aim of retrospective data analysis has been to identify periods and locations of increased risk for the various HAB species/events. Recent efforts concentrate more on "coordinating" datasets on monitoring activities, to ensure as much synergy as professionally possible. The existing biological experience is then compared with the dominant water transport within or between fish aquaculture areas (currently 13 production areas for salmonids) for comprehensive assessment of the spread of HABs.
Here, part of the challenge for coherent progamme design as shown in Figure  5. 7 has been that the various bloom monitoring activities have been built up incrementally over decades, but with a shifting focus depending on different needs of industry, regulatory and health agencies and public stakeholders. At the moment, remote sensing data is not included in the reporting database. Data from remote sensing will be eventually available on the information portal, but as a separate data source (see preliminary webpage; https:// algestatus. hi. no/). In order for an EWS to be effective, actionable observational data for high cell concentrations should be delivered to in real-time or near real-time (2-day to 3-day notice at minimum). From the industry and site management point of view, broad spatial and temporal scales are less useful for decision making  (Grasso et al.
In DISCO, the effectiveness of satellite data was greatly enhanced with the help of outputs from a circulation-biogeochemical model. It provided forecasts of sea-state variables that can be used for mapping the trajectories of the bloom that is first visible in the satellite ocean colour data. However, routine handling, analyses and interpretation of model outputs or satellite data by coastal resource managers for effective decision making requires training of personnel to interpret the data. In general, even when adequate computational resources are available, handling large datasets provided by satellites and models and their interpretation can be challenging. To circumvent these and other problems, DISCO was deliberately built as a user-friendly, menu driven, windows-based GIS platform that can be used with a modest amount of funds and training.
What approach/technology was taken to solve the problem? Monitoring? What forecast data were used? One of, if not the most important, foci of the Chilean aquaculture industry is to forecast future HAB events before they occur. With the rise of new technology (for example, machine learning, ML), faster and less expensive access to technology, and the increase in data-driven decision-making companies -the possibility of forecasting HABs is closer than ever before. With Plancton Andino's recognition of proactive monitoring as a necessity and the Data Specialist Department's recent development in ML models, future HAB events can now be forecast. The approach to solving this problem includes a recent ML wave that differs from classical ML currents. Classical ML development has focused on new and more advanced algorithms that can analyze datasets in Excel®.
What were the results for forecast operation? With the automated algorithm, the refined database and a correct visualization of the information regarding the HABf INDEX, the efforts are focused on generating predictive models in order to be able to warn in advance a possible Harmful Algae bloom. Therefore, modern predictive ML algorithms are being tested. In a study still in development, statistical models of convolutional neural networks and assembly models such as Random Forest or Gradient Boosting Machine are being tested to predict possible increases in the HABf INDEX and thus be able to alert salmonid farming sites to take action before a HAB phenomenon occurs.
This is not the case for prokaryotes. It is worth noting that the term Harmful Algal Blooms (HABs) does include the cyanobacteria blooms. Planktonic marine and brackish water cyanobacteria span a size range from c. 0. 7 µm to a few mm. The smallest are picoplankton such as Prochlorococcus and Synechococcus while the largest are colonies of filamentous genera such as Trichodesmium and Aphanizomenon that are visible to the naked eye. At present 5145 species of cyanobacteria are recognised in the database AlgaeBase  (Guiry and Guiry, 2021).
Indeed, if not properly controlled, aquatic products contaminated with HAB biotoxins are responsible for potentially deadly foodborne diseases and when rapidly growing, HAB consequences include reduced dissolved oxygen in the ocean, dead zones, and mass mortalities of aquatic organisms. Improving HAB forecasting is an opportunity to develop early warning systems for HAB events such as food contamination, mass mortalities, or foodborne diseases. Surveillance systems have been developed to monitor HABs in many countries; however, the lead-time or the type of data (i. e. identification at the Species-level, determination of toxicity) may not be sufficient to take effective action for food safety management measures or other reasons, such as transfer of aquaculture products to other areas. Having early warning systems could help mitigate the impact of HABs and reduce the occurrence of HAB events.
Acknowledgements The contributions to this technical guidance from 40 experts are greatly appreciated. They all dedicated their time and efforts to develop the different chapters. Gratitude also goes to  Henrik Enevoldsen  and  Marie-Yasmine Dechraoui Bottein  for their support for the development of the document, and to  Karen Englander  for the editing and to and  Esther Garrido Gamarro  for leading the development process. The cyanobacteria chapter has benefitted from the  Alg@line collaboration  between countries operating Ferrybox systems in the Baltic Sea and also, research funded by the  European Union ,  JERICO Research Infrastructure. In addition, a special mention to  Tomaso Lezzi , who faced a challenging layout process, for his patience and great work.
What observational data are available, and what observational data are needed? • What problems do the stakeholders want to solve? • What are the most important time and space scales for an EWS? • What specific piece(s) of information should the EWS provide and in what format? • What concerns do stakeholders have, and how do they want to be involved in the process? Actions: • Collect and compile available observational data. Some preliminary thought can go into database design or data storage. • Survey of stakeholders and/or meetings with stakeholders. Funds might be required for convening. • Estimate costs of the needed monitoring and strategize long-term funding options. Joint FAO-IOC-IAEA technical guidance 20 20 for the implementation of early warning systems for harmful algal blooms Questions: •.
INTRODUCTION The exponential proliferation of scientific articles, facilitated by their online archiving, has emerged as a byproduct of intensifying scientific inquiry. Esteemed platforms such as ACM (Association for Computing Machinery), IEEE Xplore, Elsevier, among others, now serve as repositories for millions of scientific papers. Notably, Scopus stands out as the most extensive scholarly bibliographic database, housing 71 million articles and 1. 4 billion  [1]. Academic search engines have become indispensable tools for researchers and students, given their ability to access an expansive domain of scholarly literature. Among these, Google Scholar stands as a premier academic search engine, providing unrestricted access to approximately 390 million pieces  [2]. Other significant databases include Microsoft Academic, with over 250 million public records.
RELATED WORKS An assortment of studies has surfaced in recent years, contributing significantly towards enhancing the scholarly research terrain. One of the emerging fronts in this field is the Recommendation Systems (RS), which have gained considerable traction due to their potential for delivering pertinent information. RS employ a fusion of various technologies, fields, and methodologies, demonstrating proficiency with large datasets, a focus on textual data, and a capacity for incorporating user profiles. Four primary types of scientific article recommender systems have been widely recognized: Collaborative, Content-based, Graph-based, and Hybrid recommender systems. The succeeding discourse encompasses a discussion of selected works that embody each category.
Using cosine similarity, the authors measured the similarity between specified fields of the target paper and candidate papers. The paper was recommended if cosine similarity determined that the content was similar. Using Recall and Normalized Discounted Cumulative Gain (NDCG) as evaluation metrics for various query generation and ranking strategies, the accuracy of their method was measured. Experiments were conducted to evaluate the proposed paper recommendation methods, which revealed a significant improvement in results. The authors of "A Content-Based Approach to Citation Recommendation in Academic Paper Drafts" by Bhagavatula et al. [8]  presented a method for recommending citations that can improve the quality and efficacy of the literature review process. Instead of relying on metadata such as author names, they use a neural model to encode the textual content of all available documents and embed them into a vector space.
A parameter inference algorithm, grounded in Maximum A posteriori (MAP) estimation, was developed, with ample evidence of algorithm convergence provided by Dai et al. [9]. The authors further enhanced the model's efficiency by introducing a range of citation link probability functions, which have been shown to bolster recommendation performance. To assess the efficacy of their model, the authors juxtaposed TMALCCite against several existing algorithms, including BM25, PopRank, Random walks, ClusCite, LDA, Link-PLSA-LDA, TLLDA, and RTM. Trials conducted on the AAN and DBLP datasets, using citation information from training papers to train the model and citation information from test papers as the ground truth, demonstrated TMALCCite's superiority across all evaluation metrics, including precision, recall, and mean reciprocal ranking (MRR).
While the experiment yielded promising results in addressing the identified challenge, their method was shown to still be susceptible to the cold start problem. In a subsequent study conducted in 2020, Shi et al. [12]  introduced an AMHG-based hybrid paper recommendation method, predicated on a multi-level citation heterogeneous graph. In an effort to alleviate the cold start problem, the authors not only considered similar papers published by the same author but also incorporated metadata of the papers into their model. The model was further refined by reordering the output candidate list based on the author influence factor, with the intention to prioritize high-quality papers. The investigation employed the DBLP-REC dataset and the results demonstrated a significant improvement in the accuracy of recommendations provided by the AMHG method.
However, this approach was found to rely exclusively on offline data, thereby limiting its capacity to provide personalized recommendations. This limitation was attributed to the lack of a user's usage record and the exclusion of key metadata such as the publication journal. It is evident from the literature review that significant efforts and research have been dedicated to the development of filtering techniques for recommender systems. Despite these advancements, the reviewed methods exhibit inherent limitations. Collaborative filtering recommender systems necessitate vital information about users and items prior to generating recommendations. However, in instances where there is no information about the user or the item in the system, or when only a few ratings from users about various items are available, numerous challenges, including the cold start and sparsity problem, arise.
SOFTWARE DESCRIPTION This section introduces the MASSPR (Multi-Agents System for Scientific Paper Recommendation). The MASSPR tool consists of six primary modules: Graphic User Interface (GUI), Front-end modules, GUI Back-end Engine, Query Module, Searching Module, Data preparation and learning module, and Recommendation and Filtering Module. Each module performs specific tasks and collaborates with others to achieve the system's objectives. What sets MASSPR apart from other recommendation systems is its unique approach of extracting queries from a research paper provided by the user instead of relying on userprovided keywords to represent their interests. These queries, derived from the most relevant terms in the paper, are then submitted to existing online repositories storing academic papers.
Query modelling module The Query Modeling Module is a sub-module of MASSPR that plays a crucial role in constructing and modeling searching queries. Figure  2  provides an insight into the internal architecture of this module. One of the significant challenges students face when using search engines is the inadequacy of their keywords and queries in finding papers relevant to their needs. Our tool addresses this issue by generating search queries based on a series of base papers the user provides. This modeling approach utilizes a selection mechanism grounded in the Bayes theorem to extract concepts and keywords. The process begins by breaking down the userprovided papers into distinct sections: title, abstract, author and affiliation, keywords, introduction, references, and more. Each section undergoes preprocessing to transform the text into a more manageable format, thereby enhancing the performance of this module.
During the preprocessing phase, various filters are applied, including removing special characters and white spaces, converting texts to lowercase, transforming number words into numeric form, and other necessary transformations. Subsequently, similar sections are grouped into bags. The module calculates the conditional probability of their co-occurrence using Bayes Theorem for each pair of consecutive words within each bag. This step generates a list of conditional probability values for each section, which are then stored in a temporary database. Before the search queries are modeled based on the previous results, the calculated probabilities are sorted and arranged in descending order to select the highest probability. The query modeling phase employs the Markov Chain technique to construct coherent sentences for searching queries based on a list of consecutive words.