dataesr/scientific-paragraphs-categorization huggingface dataset:
Detailed investigations of a duricrust layer from the terrace sequence demonstrated their pedogenic origin as well as variability in the climate of north-western Africa since MIS 3. The earliest readily dateable evidence for occupation of the site extends to MIS 6 (~135 ka); the lower deposits, containing comparable stone tool assemblages, remain undateable. Tanged pieces first occur at Rhafas at ~123 ka and continue until ~57 ka, which is consistent with the chronological dataset of the Maghreb for the Aterian. Archaeological layers associated with the LSA are dated to ~21 ka and ~15 ka; although it is unclear whether the site was periodically or continuously occupied during this phase, the nearby site of Taforalt suggests that the latter is possible  [2].
Acknowledgments The authors would like to thank the members of the  Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology , and especially  Vera Aldeias ,  Steffi Hesse  and  Stefanie Stelzer  for their great support during field work, sample preparation and OSL measurements in the laboratory as well as during the writing process. Special thanks go to  Detlev Degering  ( VKTA Dresden ) for performing the γ-ray spectrometry measurements. We would also like to thank  El Hassan Talbi  and  Lahbib Boudchiche  for providing invaluable assistance in understanding the local geology of the site and all members of the excavation team in Morocco for their dedicated and active cooperation during fieldwork. We would like to thank  Zenobia Jacobs  and the other anonymous reviewers for their insightful comments which improved our paper.
This study was funded by the  Max Planck Society. A. Bouzouggar 's work was also supported by  Aix-Marseille University, IMéRA , with the support of LabexMed, of  Labex RFIEA  and  ANR -Investissements d'Avenir. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. Results-When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0. 708 and 0. 797) against the state-of-the-art (AUC scores ranging from 0. 527 to 0. 705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0. 78) against the state-of-the-art (AUC scores ranging from 0. 665 to 0. 717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. Conclusions-This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention.
The data and its problems As is typical for most medical BN building projects, in the forensic psychiatry studies we were presented with a set of unstructured patient data from questionnaires and interviews that had been collected independently of the requirements of a BN model. The questionnaires were large and complex and the data extracted from them was combined with other relevant patient data, such as criminal records, retrieved by the Police National Computer. The questionnaire data includes patient responses to questions over the course of an interview with a specialist. They also include assessment data based on certain check-lists formulated by specialists, and which are taken into consideration for evaluating certain psychological and psychiatric aspects of the individual under assessment.
The responses can take any form, from binary scale (such as Yes/No) and ordinal scale (such as Very low to Very high), to highly complicated multiple choice answers (with one or more possible selections), numerical answers (e. g. salary, number of friends), as well as free-from answers. For example, in the DSVM-P study individuals were asked to complete up to 939 questions. All of the responses are coded in a database, and each response is represented by a variable. Since many of those questions were based on multiple choice answers (with up to approximately 20 choices), and with more than one answer being selected in most of the cases, the resulting database included a number of responses that was a multiple of the number of questions. As a result, there were thousands of variables in the relevant databases, excluding the data from criminal records retrieved by the Police National Computer.
In the DSVM-MSS study, which was based on less extensive questionnaires, the total number of data variables was still well over 1,000. Yet, despite the large number of variables, the databases in both studies had relatively small sample sizes (953 and 386 samples respectively for DSVM-P and DSVM-MSS) -again something that is very typical of many such studies. This makes them a poor starting point for developing effective BNs for decision-support and risk assessment, which normally require a very high ratio of samples to variables and/or substantial expert knowledge. This point is increasingly widely understood; we do not restrict the complexity of the model simply because we have limited or poor quality data  [12, 13]. BN applications which incorporate expert knowledge along with relevant statistical data have demonstrated significant improvements over models that rely only on what data is available; specifically in real-world applications requiring decision support  [5, [14] [15] [16] [17] [18].
Model objectives The starting point of the method is the Model Objectives component. Although the availability of some existing patient/medical database is often the motivation to develop a BN ("we have this really great/important data -we think you should be able to use it to build a BN model to support decision making for problem X…") it should never be the real starting point. This is true even in the scenario (which is the one we are assuming) whereby the available data cannot be extended except by expert knowledge. Instead, irrespective of whatever existing data is available (and certainly before even considering doing any kind of statistical analysis) the first step involves determining what the actual objective of the model is. For example, the following are very different classes of objectives for BN models: 1.
Managing the number of states of the BN nodes Although many variables in the dataset are typically binary (i. e. Yes/No, True/False, High/ Low), some have multiple states and some are continuous. For example, in the DSVM-P study we have 79 categorical nodes with the number of states ranging from two to nine, and nine continuous distribution nodes (four different types); whereas in the DSVM-MSS study we have 80 categorical nodes with the number of states ranging from two to eight. If we are learning the prior probabilities of the states from the data alone we need to ensure there not too many states relative to the sample size. If there are the learned probabilities will suffer from high variability, which typically results in model overfitting; i. e. leading to a model that performs well on the training data but poorly on unseen data.
Mutual exclusivity Datasets resulting from questionnaires and interviews will likely incorporate multiple variables that are mutually exclusive. Such variables can usually be more simply modelled in a BN as the set of states of another single generalised variable (by definition such states are mutually exclusive). An example of this common phenomena arising in the DSVM-P study is shown in Figure  5. Here there is a set of seven mutually exclusive Boolean variables in the dataset (there were many more but the experts identified these seven to be sufficient and the most important); they can be collapsed into a single generalised categorical node. This assumes that all of the mutual exclusive variables share identical parent and child nodes, and are therefore not required to be modelled as distinct nodes  [30].
Data-driven learning Unfortunately, in both our studies the vast majority of the data-driven variables had missing data. The only data-driven nodes with complete data were those based on criminal data provided by the Police National Computer (e. g. Age, Gender, Number of violent convictions). This is typical of real-world data in medical domains where it is generally accepted that patient data collected during the course of clinical care will inevitably suffer from missing data  [38]. In dealing with datasets which include missing data, decision scientists typically have three options  [39] : 1. Restrict parameter learning only to cases with complete data: For the reasons explained above this is not a viable option for typical medical studies.
Maximisation (M) step. This solves: for p which, assuming the expected counts were the true counts, maximises the likelihood function. For this task, we have made use of the EM learning engine offered by the freely available GeNIe  [42]. This is because GeNIe offers the two following important features during the learning process: 1. Fixed nodes: During the parameter learning process, expert-driven variables must be indicated as Fixed nodes in order to retain their prior probabilities as suggested by the experts; assuming that no data exist for these variables that will allow EM algorithm to generate meaningful inferences (see Figure  6  for an example);.
Structural validation In terms of validating the structure of the BN model, apart from predictive accuracy, we found sensitivity analysis (SA) to be particularly useful. SA is a simple, yet powerful, technique for examining the impact of specified model factors against a desired targeted factor within the same model. This is achieved by recalculating the outcomes of the targeted factor under alternative assumptions. It is possible to see diagrammatically which nodes, and under what states, have the greatest impact on any selected target node as we subsequently show in Figure  9. We used the freely available AgenaRisk software for the sensitivity analysis since this process is fully automated in that package  [49]. SA can serve as an extremely useful tool for rapidly evaluating: 1. The structure and the CPTs of the BN: The sensitivity results depend on the structure of the model and hence, the overall robustness of a BN model can be swiftly assessed with domain experts, in an attempt to identify possible irrationalities for both the BN structure and the underlying CPTs  [50, 51] ;.
5. PCL-R  [58] : is a checklist of 20 variables which measure psychopathy, and which are strongly related with offending behaviour in prisoner populations. The DSVM-MSS model was assessed against the 12 predictors shown in Figures  11  and 12 , and in terms of both General violence (i. e. minor violent incidences) and Violent Convictions (i. e. major violent incidences). The DSVM-P model was assessed against three predictors shown in Figure  12 , and in terms of Violent Convictions. Table  2  provides a summary of the results. Overall, the DSVM-MSS model demonstrated competitive predictive capability, whereas the DSVM-P model demonstrated superior predictive capability, when compared against the current state-of-the-art predictors that are employed with the same dataset. More specifically: 1.
Complexity: The proposed method should lead to a minimum number of variables and a conceptually well-structured and rational model. However, because the method encourages incorporation of expert-driven variables (which are additional to those in the dataset) there is a risk that experts will overcomplicate the model, adding multiple layers of detailed variables. Having identified the important limitations of depending on expert knowledge in almost all of the development stages, we need to justify its usefulness. It is important to note that, with the advent of 'big data' much of the current research on BN development assumes that sufficient data are available to learn the underlying BN structure, hence making the expert's input minimal or even redundant. For example, we could have made use of: 1.
2. BN learning methods that are appropriate for use with small datasets but which include a large number of variables  [60] ; Making use of such algorithms eliminates, or minimises, the requirement for expert elicitation. This is very convenient in the sense that a BN model can be generated without much effort since we can skip the process of knowledge elicitation, which is extremely time consuming since it typically requires collaboration with multiple domain experts. Conversely, the method illustrated in this paper involves extensive use of expertise, in almost all of the development stages, which greatly increases the effort required. This is because: 1.
2. Poor quality data means meaningless BN structure: As discussed in Section 2, our case studies were based on datasets consisting of thousands of variables, but the sample sizes of those variables was below 1,000. To address this problem, the experts identified (in each case study) less than 100 model factors as a requirement in order to construct a comprehensive BN. On the other hand, if we were to make use of a structure learning algorithm we would have ended up with a extensively large network of associations between hundreds/thousands of responses as recorded from questionnaires and interviews. Even when these available responses represent information that associates with all of the ideal variables identified for inference, there is typically far too many variables and far too few samples in many medical applications to achieve any sensible structural learning with the state-of-the-art algorithms; especially in the case of complex and imbalanced data  [61].
The proposed expert Bayesian network development process on the basis of learning from questionnaire interviewing data. How the BN is revised after introducing synthetic nodes  [11]. Collapsing mutual exclusive data variables into a single generalised node with mutual exclusive states. A small BN fragment from the DSVM-MSS indicating the CPTs of expert-driven synthetic variables are preserved after performing parameter learning with EM algorithm. AUC score comparison, in terms of predicting incidences of Violent Convictions, between DSVM-P and the specified three well known predictors, when employed with the same dataset.
Acknowledgements We acknowledge the financial support by the  European Research Council (ERC)  for funding this research project,  ERC-2013-AdG339182-BAYES_KNOWLEDGE , and  Agena Ltd  for software support. We also acknowledge the expert input provided by  Dr Mark Freestone  and  Professor Jeremy Coid  in developing the two Bayesian network applications, which are based on the method presented in this paper, but which are described in two other papers [10,11].
In this study, the recently launched Sentinel-2 (S2) optical satellite and the active radar Sentinel-1 (S1) satellite supported by active fire data from the MODIS sensor were used to detect and monitor forest fires in the Congo Basin. In the context of a very strong El Niño event, an unprecedented outbreak of fires was observed during the first months of 2016 in open forests formations in the north of the Republic of Congo. The anomalies of the recent fires and meteorological situation compared to historical data show the severity of the drought. Burnt areas mapped by the S1 SAR and S2 Multi Spectral Instrument (MSI) sensors highlight that the fires occurred mainly in Marantaceae forests, characterized by open tree canopy cover and an extensive tall herbaceous layer. The maps show that the origin of the fires correlates with accessibility to the forest, suggesting an anthropogenic origin.
The two first missions, Sentinel-1 (S1) and Sentinel-2 (S2) operational since October 2014 and December 2015, respectively, provide time series that can already be assessed for monitoring tropical forests. For the mapping of burnt areas, optical satellite data from the Multi Spectral Instrument (MSI) on S2 with a potential five-day temporal resolution (when the two satellites are operational) fits the requirement for a higher spatial resolution burnt area product. Images from the radar C-SAR sensor on S1 can overcome cloud cover limitations in tropical evergreen forests. The complementarity of the two sensors can be used for the design of a joint passive and active burnt area mapping products at regular intervals for this region. The objective of this study is to review the recent occurrence of forest fires in the north of the Republic of Congo and to evaluate the potential of a joint use of S1 and S2 satellites to detect and monitor burnt areas in tropical evergreen forests.
The exceptional character of the fire event for this region and the rainfall anomaly will be illustrated by reviewing climatic data and historical fire record. The vegetation types in the study area will be investigated to assess which type of forest has been burnt. Active fires detected by the MODIS sensor on board the Aqua and Terra satellites are used as a first evidence of the location and timing of this event. S1 and S2 time series are used to map the location, extent and speed at which these fires have progressed. The burnt area maps produced are used to validate the hypothesis that the fires happened almost exclusively in Marantaceae forests. The proximity of the burnt areas to logging roads is also evaluated.
Climatic Data Information on rainfall is available from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product  [32]. Starting in 1981, CHIRPS is a 30-year quasi-global rainfall dataset. Spanning 50 • S to 50 • N (and all longitudes) CHIRPS incorporates 0. 05 • resolution satellite imagery with in-situ station data to create gridded monthly time series for seasonal drought monitoring and water resource management  [33]. The monthly mean average of the CHIRPS dataset for 1981-2015 highlights the two dry seasons; a short dry period in July and a longer dry season in December-February characteristic of the north of the Republic of Congo. To highlight rainfall anomalies from these data, the Standardized Precipitation Index (SPI) is used. The SPI is the standard index for quantifying and reporting meteorological drought.
Active Fire Data Active fire data over the area from the Moderate Resolution Imaging Spectroradiometer (MODIS) were acquired from the Fire Information for Resource Management System (FIRMS) MODIS Fire Archive Download webpage  [40]. The data are from the Collection 5 Near Real Time (NRT), extracted from the standard MCD14ML fire product produced at the MODIS Fire Science Computing Facility (SCF). The MODIS data are available from 2000 to date, comprising of 1 km detections from the combined 4 overpasses of the sensors on the Aqua and Terra satellites. The fires that are actively burning at the time of the satellite overpass are detected using thresholds in the middle and thermal infra-red channels  [41]. A quality control channel accompanies the data. Although such data have a number of sampling issues, the comparison of the current number of fire counts in a given region with the long term (i.
Burnt Areas Detection Burnt area mapping is carried out using S1 and S2 datasets independently and all pixels detected as burnt will be combined into a map composite. Independent burnt area maps will be generated from the images acquired respectively by the sensors on S1 and S2 during the period of the 1 November 2015 to the 30 April 2016 for S1 and the 3 December 2015 to the 21 May 2016 for S2. The maps will then be combined at a 20-m resolution. Three different scenarios are foreseen when generating the map composite: (i) a pixel is detected as burnt in either S1 or S2 maps; (ii) both S1 and S2 detected a pixel as burnt; and (iii) neither S1 nor S2 detect a pixel as burnt. The different results are presented and discussed but the final goal is to combine S2-based and S1-based maps to take advantage of the sensors complementarity.
All SAR images are first corrected using Map Gamma filter technique described in Lopes et al. ,  [58]  to reduce speckle. Map Gamma is an adaptive filters that reduces the variance (i. e. , speckle) within the image while preserving both radiometric and spatial resolution. The filter is based on the assumption that the (unspeckled) intensity of the scene is gamma distributed. The filter minimizes the loss of texture information and it is thus suitable for a wide range of gamma distributed scenes, such as forest and agriculture areas. The S1 temporal aggregation is performed on bi-monthly basis calculating the average of the SAR stack. The 2-month time span is necessary to further reduce the noise of SAR data records. The calculation of backscatter differences between consecutive periods allow to detect fire scars that exhibit large differences in backscatter.
Forest Cover Percentage Using VHR Data In order to assess the opening of the forest canopy and whether the fires were limited to the most open areas, a very high resolution image (VHR) was analysed to calculate the percentage tree cover of the forest over a small part (100 km 2 ) of the study. The image is a GeoEye-1 four band pan sharpened image at a nominal spatial resolution of 0. 4 m acquired the 28 November 2011. GeoEye-1 spectral bands are the following, Blue (450-510 nm), Green (510-580 nm), Red (655-690 nm) and Near Infra-Red (780-920 nm). The opening of the canopy is evaluated by segmenting the image into homogeneous textural objects. The data were segmented with the eCognition software to an average of 0. 2 ha objects with a scale factor of 70 (shape and compactness of 0. 5 and 0.
The test is carried out by detecting fire scars between: (1) the first and the second period (i. e. , March-April aggregation vs. January-February one); and (2) the first and the third period (i. e. , March-April aggregation vs. December-January one). Fire scars found for the first pair that are not subsequently confirmed by the second pair are rejected. Therefore, pixels classified as burned that do not display a time-consistent change in backscatter signals are considered as outliers. This procedure is applied to ensure that changes in backscatter signal are due to the vegetation structure rather than to temporary differences in soil moisture. To remove possible errors caused by topography, SAR data are usually first corrected using a maximum slope threshold. As the terrain is flat in the study area, such correction is neither needed nor applied.
Timing of the Fires The timing of the fire can be derived from the active fire product derived from MODIS data and complemented by the S2 maps. As illustrated in Figure  8 , coherence between the two in term of temporal pattern is visible. The active fire product gives us the precise date of the fire observation with a daily temporal resolution. The timing detected by S2 strongly depends on the time of acquisition of the image, theoretically every 10 days, and the absence of clouds at the time of acquisition. From the MODIS data active fires, it is observed that fires occur from the 26 December 2015 to the 10 April 2016. From S2 based maps it can be derived that the most important progression of fire happened between end of January and February 2016. From MODIS active fire detection and S2 maps (Figure  8 ), it is evident that fires started in different locations rather than expanding from a single origin.