dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability All raw data used in the research has been made available in the supplementary data file 'Supplementary Data file 1'. This includes: Additional analyses in Supplementary Notes 1 to 6 and all the relevant data files to repeat this analysis. The Supplementary Data file 1 is available at the National Geoscience Data Centre at Keyworth (NGDC) at https://doi. org/ 10. 5285/d6b7c567-49f0-44c7-a94c-e82fa17ff98e. The full Mochras XRF dataset is in ref. 101.
Firms face an increasingly complex economic and financial environment in which the access to international networks and markets is crucial. To be successful, companies need to understand the role of internationalization determinants such as bilateral psychic distance, experience, etc. Cutting-edge feature selection methods are applied in the present paper and compared to previous results to gain deep knowledge about strategies for Foreign Direct Investment. More precisely, evolutionary feature selection, addressed from the wrapper approach, is applied with two different classifiers as the fitness function: Bagged Trees and Extreme Learning Machines. The proposed intelligent system is validated when applied to real-life data from Spanish Multinational Enterprises (MNEs). These data were extracted from databases belonging to the Spanish Ministry of Industry, Tourism, and Trade.
To do so, advanced classifiers based on Bagged Decision Trees (BDTs) and Extreme Learning Machines (ELMs), are applied. These supervised-learning methods are used to model the fitness function of a FS schema, where an evolutionary algorithm is applied in order to generate different combinations of features in order to predict the internationalization decision of companies with high accuracy. Furthermore, obtained results are also compared with those from other Machine Learning methods that have been previously applied  (Jiménez & Herrero, 2019)  to the same dataset. Similar, yet different, solutions comprising genetic FS have also been proposed for problems in other fields such as health  (Salcedo-Sanz et al. , 2004; Maleki, Zeinali & Niaki, 2021) , Bio-informatics  (Chiesa et al. , 2020)  or credit rating  (Jadhav, He & Jenkins, 2018)  among others.
Artificial Intelligence methods have been previously applied to FS  (Saad, 2008) ; although they are one of the newest proposals in the field of neural networks, ELMs have been previously applied as classifiers under the frame of evolutionary FS, since the seminal work was published  (Meng-Yao et al. , 2012). FS based on both basic ELM and Optimally Pruned ELM was applied in  Termenon et al. (2013)  and  Chyzhyk, Savio & Graña (2014) , where the data features were extracted from brain magnetic resonance imaging. In  Termenon et al. (2013)  the ELM-based FS was applied under the frame of an image biomarker identification system for cocaine dependance, while in  Chyzhyk, Savio & Graña (2014)  it was applied to better diagnose patients suffering from Alzheimer's Disease. Results were compared to those obtained by Support Vector Machines, k-Nearest Neighbour, Learning Vector Quantization, Relevant Vector Machines, and Dendritic Computing.
In  Xue, Yao & Wu (2018)  a variant of ELMs called Error-Minimized ELM (EM-ELM) is applied to measure the quality of each one of the subsets of features generated by a genetic algorithm. The proposed FS method is compared to some other Machine Learning methods that do only include one (C4. 5) basic decision tree. Furthermore, results are obtained from 10 benchmark datasets, none of them from the economics domain. In  Wang et al. (2018) , ELMs have been proposed for FS once again, but combined with Particle Swarm Optimization, for regression. Although the Bootstrap Aggregation (Bagging) of decision trees has been also applied to FS  (Panthong & Srivihok, 2015) , to the best of the authors knowledge, it has never been compared to ELM for this purpose. Thus, going one step forward to the previous work, two advanced FS methods are applied in the present paper to a real-life dataset on company internationalization and their results are compared to those previously obtained by some other FS methods.
The internationalization of companies has been previously researched by Machine Learning methods; in Rustam, Yaurita & Segovia-Vergas (2018) a dataset of 595 Spanish firms is analyzed by Support Vector Machines (SVMs) in order to predict the success of internationalization procedures. That is, SVMs are applied in order to differentiate between successful and failed internationalization of manufacturing companies. Differentiating from this previous work, the present paper proposes advanced FS to gain deep knowledge about the key features that are considered by companies in order to invest in a foreign country. The addressed topic of internationalization is explained in "Literature Review", while the Machine Learning methods proposed and applied are described in "Materials and Methods". Obtained results are compiled and discussed in "Results and Discussion" and the conclusions derived from them are presented in "Conclusions".
MATERIALS AND METHODS The present work aims at obtaining the most relevant features from enterprise-country data that will provide enterprise managers with the information to take decisions on internationalization. In this paper we employ a sample of firms coming from two sources belonging to the Spanish Ministry of Industry, Tourism, and Trade and the website of the Foreign Trade Institute (ICEX) (ICEX Spain Import and Investments, 2020). We compiled a sample of independent multinational firms operating in overseas markets by conducting FDI operations. Since small and medium firms have distinct capabilities and face specific challenges in terms of access to funding to internationalize, we focus on large firms and follow the well-established criterion of having 250 employes at least  (Jiménez, 2010).
We also focus on investments before 2007 to prevent distortions in the results due to the impact of the subsequent financial crisis  (Jiménez et al. , 2018). Following previous studies on the internationalization of Spanish multinationals, we collected the following variables for each foreign subsidiary of the companies in our sample: Characteristics from host country such as unemployment, total inward FDI, GDP, growth, and population. Bilateral geographic distance as measured in the CEPII database (Centre d'Études Prospectives et d'Informations Internationales (CEPII), 2020). Psychic distance stimuli between countries. We include all 6 dimensions identified by  Dow & Karunaratna (2006) : education, industrial development, language, democracy, political ideology, and religion. The first one, education, measures the differences on education enrollment and literacy between the two countries building on data from the United Nations.
Comprehensive data for all the variables across the majority of countries in the world can be found at  (Dow, 2020). Similarly, a more detailed description of the procedure to calculate the various psychic distance dimensions can be found at that website and at the seminal paper by  Dow & Karunaratna (2006). Vicarious experience: Following the previous literature on vicarious experience  (Jiménez & De la Fuente, 2016; Jiang, Sui & Cao, 2013)  we employ the total count of other Spanish MNEs present in the host country as our measure of vicarious experience. We distinguish between same-sector vicarious experience (total count of other Spanish MNEs in the host country belonging to the same sector), different-sector vicarious experience (total count of other Spanish MNEs in the host country belonging to a different sector) and total vicarious experience (the addition of same-sector and different-sector vicarious experience).
Firm product diversification: we distinguish between three alternatives, namely related product diversification, unrelated product diversification, and single-product firms  (Ramanujam & Varadarajan, 1989; Kumar, 2009). Industry: we identify five main groups including manufacturing, food, construction, regulated, and unclassified sectors. Other firm characteristics: Return on Equity, number of countries where the firm operates, Number of employes, and whether or not the firm is included in a stock market. All in all, data from 10,004 samples, compressing 25 features, were collected. Features from countries are:   2. Obtaining knowledge about decision making regarding the internationalization of companies is a challenging task that perfectly suits Feature Selection (FS). There are mainly two methods from the Machine Learning field that are able to identify the key characteristics of a given dataset.
Feature extraction is one of the alternatives, but it is not suitable for the present work as it generates new features from the dataset that are not in the original data. In the present work, the target is to select some of the features from the original dataset as conclusions can be generalized and obtained knowledge applied to other problems (i. e. , set of companies). Thus, feature selection is the most appropriate method in the present study. Hence, some advanced FS proposals are applied in the present research with the aim of identifying the key characteristics that lead to positive or negative internationalization decisions. In general terms, FS consists of a learning algorithm and an induction algorithm. The learning algorithm chooses certain features (from the original set) upon which it can focus its attention  (John, Kohavi & Pfleger, 1994).
Only those features identified as the most relevant ones are then selected, while the remaining ones are discarded. Additionally, there is also an induction algorithm that is trained on different combinations of features (from the original dataset) and aimed at minimizing the classification error from the given features. Building on Choudhury, Allen & Endres (2021), supervised Machine Learning is applied in the present work as the induction algorithm. Under the frame of FS, for every original feature, two different levels of relevance (weak and strong) can be defined  (Kohavi & John, 1997). A feature is assigned a strong-relevance level if the error rate obtained by the induction algorithm increases significantly and a weak-relevance level is assigned otherwise. In keeping with this idea, strong-relevance features are to be selected from the internationalization dataset in order to know which ones are the most important ones when taking internationalization decisions.
According to that, different classifiers have been applied as induction algorithms in order to predict the class of the data. In the present paper, both Bagged Decision Trees and Extreme Learning Machines are applied. Furthermore, results obtained by these two methods are compared to those previously generated  (Jiménez & Herrero, 2019)  by Random Forest (RF) and SVM on the very same dataset. The classifiers are fed with datasets containing the same data instances as in the original dataset but comprising a reduced number of features. In order to generate different combinations of them, standard Genetic Algorithms (GAs)  (Goldberg, 1989)  have been applied in the present paper. The main reason for choosing such approach is that, when dealing with big datasets, it is a powerful mean of reducing the time for finding nearoptimal subsets of features  (Siedlecki & Sklansky, 1989).
When modeling the problem under this perspective, the different solutions to be considered (selected features, in the present research) are codified as n-dimensional binary vectors, being n the total number of features in the original dataset. In a FS problem, a value of 0 is assigned to a given feature if it is not present in the feature subset and 1 otherwise. Once representation of solutions is defined, the GA (as defined in Fig. 1  below) is applied. In standard GAs, two operators (mutation and crossover) are usually applied depending on a previously stated probability (experimentation has been carried out with different values as explained in "Results and Discussion"). Additionally, when modeling a GA   (Kramer, 2017) , the fitness function is also defined, as the criteria to measure the "goodness" of a given solution, that is its quality.
In the case of FS, the fitness function is usually defined as the misclassification rate of the classifier when applied to the dataset compressing the features in the solution to be evaluated. As a result, the best solution is selected, being the one with the lowest value calculated with the fitness function. That is, the combination of features that led the given classifier to get the lowest error when being tested. As previously mentioned, some different classifiers have been applied, being the novel ones described below. Decision trees (DTs)  (Safavian & Landgrebe, 1991)  are one of the most popular Machine-Learning methods. They have been successfully applied, proving to be valuable tools for different tasks such as classification, generalization, and description of data  (Sreerama, 1998).
Being trees, they are composed of nodes and arches, as shown in Fig  2. Nodes in a DT can be of two different types; internal and leaf. The first ones are those aimed at differentiating responses (branches) for a given question. In order to address it, the tree takes into account the original training dataset; more precisely, the values of a certain feature. On the other hand, leaf nodes are associated to the final decision (class prediction) and hence they are assigned a class label. All internal nodes have at least two child nodes and when all of them have two child nodes, the tree is binary. Both parent/child arches and leaf nodes are connected: the first ones are labeled according to the responses to the node question and the second ones according to the classes or the forecast value. In the present research, performance of DTs is improved by a Booststrap  (Efron & Tibshirani, 1994 ) Aggregation (Bagging) strategy, resulting in a Bagged DT (BDT).
Unlike the well-known training algorithms based on gradient-based (they try to minimize training error without considering the magnitude of weights), the ELM training algorithm reaches the smallest training error and norm of weights, at the same time. As a side effect, the model also has a good generalization performance and is consequently considered an advisable learning model (for both classification and regression) mainly in those applications when many training processes have to be launched (as in the present case of evolutionary FS). It is widely acknowledged that one of the main disadvantages of FFNN is that all the parameters need to be tuned. In the case of ELMs, both the hidden layer biases and input weights are randomly assigned, obtaining acceptable error rates. All the methods previously described in this section have been implemented and run on MATLAB software.
Something similar can be said when analyzing the average number of features that is significantly lower than the number of features in the original dataset. The lowest average value (8. 8) is taken when applying ELMs and hence for the lowest classification error. When looking at the deviation of the number of features for the best individuals in the 10 iterations (Fig. 4A ), it can be said that the mean and the median are quite close in the case of ELMs results, with a significantly small deviation in the case of ELMs with the sinusoidal function (ELM-sin). In the case of BDTs, values greatly vary (from 3 to 17). Regarding the average error (in red in Fig 4B ) of the whole population in the last generation, ELMs have obtained lower values than BDTs. More precisely, the lowest value of all the iterations (0.
CONCLUSIONS In this paper we aim to employ sophisticated AI techniques to explore the various possible configurations of variables that may play a critical impact on internationalization, in order to overcome limitations related to bounded rationality  (Barnard & Simon, 1947)  and provide insightful information relevant for managers and policy-makers. From the previously presented results, it can be concluded that advanced FS can be successfully applied in order to identify the most and least relevant features concerning the internationalization strategy of enterprises. More precisely, the ELM has proved to be the wrapper learning model able to obtain the lowest error when predicting the internationalization decision on the dataset under study. The results obtained in this research clearly show that firm-level characteristics are more relevant than country-level ones.
, 2019; Choudhury, Allen & Endres, 2021)  highlighting the complementarities between Machine Learning techniques and other traditional tools, as the former permit identifying patterns from an abductive and an inductive way that deductive approaches such as classic regression, due to their constraints to fit models, sometimes overlook. We acknowledge that our paper is subject to some limitations, which open up interesting opportunities for further research. First, we are unable to include additional variables that could be relevant, such as the percentage of exports or the exact year the company started international operations, due to data unavailability in the data sources we were able to access on Spanish MNEs. Besides, a transnational study is planned as future work, comprising data from additional countries.
Participants Eighty-one French 28-month-olds participated in this study (mean age = 28. 0 months, range = 26. 9 to 30. 2; SD =0. 7; 40 girls), with 20 in the right-dislocated-only condition, 22 in the right-dislocated+intransitive condition, 18 in the intransitive-only condition and 21 in the transitive-intransitive condition. All participants were native French speakers with less than 20% exposure to another language. An additional thirty-six children participated in the study but were not included in the final analysis because of fussiness during the experiment (5), distraction during the dialogue phase (6), side bias (1), unusable test trials with missing eye tracking data (21), because they were exposed to other languages than French at home (1), or because of technical problems (2). Parents signed an informed consent form.
, 2014; and in English: Yuan & Fisher, 2009). It also allows us to compare all the Conditions together in the same statistical model structure (which is not possible within the framework of the first analysis). We modeled the proportion of looking-times towards the two-participants event for each trial in a mixed logit model with a fixed predictor Condition (4 modalities: transitive+intransitive; right-dislocated+intransitive; right-dislocated-only; intransitive-only) and a random Subject effect. The model was specified as: Prop_to_2P ~ Condition + (1 | Participant) and was used on data averaged at the trial level on the whole trial duration (such that each participant had 1 to 3 data points). Because the dependent variable is a continuous variable in the [0; 1] interval and is a mean of nonindependent samples, we used the glmmTMB package in R to model it with a beta distribution.
Participants Seventy-two 28-month-olds (ranging in age from 26. 7 to 29. 0, M age =27. 7; SD= 0. 5; 36 girls) participated in this experiment. All were monolingual native French speakers with less than 20% of exposure to another language. Twenty-four toddlers were randomly assigned to each of the three experimental conditions (transitive, right-dislocated, and intransitive). An additional twelve toddlers completed the experiment but they were not included in the final sample because of fussiness during the experiment resulting in more than 50% of trials with missing eye-tracking data (n = 9), because they cried during the experiment (n = 2), or because of technical problems (n = 1). Parents signed an informed consent form. This research was approved by the local ethics committee. Participants were recruited from the greater Paris area through direct contact with parents belonging to our local database (these children can be assumed to come from middle-to highsocioeconomic status homes).
While direct FCO 2 measurements over seagrass meadows have existed for roughly a decade  (Polsenaere et al. , 2012) , and some regional synthetic efforts have been made  (Tokoro et al. , 2014) , these individual datasets have yet to be synthesized globally. Therefore, a set of very basic questions remains unanswered. Are there global patterns explaining why some seagrass meadows are CO 2 sinks and others are sources? Are these reasons typological, climatological, or simply latitudinal in nature? Are there any generalizable features of air-water CO 2 exchange across these diverse coastal habitats? These questions are central to "blue carbon" science  (Legge et al. , 2020; Macreadie et al. , 2019) , but have yet to be addressed. In the present study, we synthesize a data set of direct EC measurements of air-sea FCO 2 over seagrasses.
Data Quality Control For all datasets processed using EddyPro software (Licor Biosciences, USA), data were screened to remove records with quality control (QC) code  (Burba, 2010)  greater than 1, resulting in a removal of 11. 6% of the full data set. A detailed description of this QC criteria is provided elsewhere  (Mauder & Foken, 2004 ), but briefly it seeks to combine tests for steady state and turbulence development into a single QC flag, where values >1 can be considered of "poor quality. " Next, in an effort to screen out data where a terrestrial influence was likely, we removed results where the shear conditions indicated a non-marine flux footprint. As described later, we discarded FCO 2 results when the ratio of u * /U mean exceeded a threshold of 0. 139, which was set as 150% of the average u * /U z (0.
Time-Frequency Analysis A wavelet coherence analysis  (Grinsted et al. , 2004; Torrence & Compo, 1998 ) was carried out to analyze the dependence of FCO 2 on net solar radiation (R n ), water depth (Z water ), air temperature (T air ), water temperature (T water ), wind speed (U mean ), and wind direction (U dir ) across temporal scales and through time. This analysis was completed in the "wavelet-coherence" package for MATLAB R2017  (Grinsted et al. , 2004). Due to the sporadic nature of these coastal EC deployments, the temporal coverage is somewhat patchy, creating a problem for time-series analysis. So, prior to wavelet coherence analysis, the largest period of contiguous data availability was identified for each site (black bars shown in Figure  1 ), and only this period was used for subsequent wavelet analysis.
This choice improved data quality at hourly to monthly time scales, but necessarily involved a loss of information at longer scales. The small remaining gaps in the pseudo-continuous datasets (due to poor QC, instrument failure, etc) were filled with mean statistics for each variable, and the edges were padded with zeros  (Grinsted et al. , 2004). Because this wavelet analysis requires that the probability distribution function is approximately normal, we used the "normalizepdf" function to transform each data set to have a mean of zero and a unit variance  (Grinsted et al. , 2004). We finally applied a Morlet wavelet to the time series using the "wtc" function  (Grinsted et al. , 2004)  and estimated the 95% confidence intervals with 15 Monte Carlo simulations.