dataesr/scientific-paragraphs-categorization huggingface dataset:
Predictive performance of the CarinaNet model The mean absolute error of the CarinaNet model for the ETT tip position prediction, the carina position prediction, and the ETT-carina distance prediction performed on the internal and external test sets are shown in Table  1. The mean absolute error on radiographs from the external test set where the level of confidence was superior to 0. 5 was also computed. The CarinaNet mean error for the prediction of the ETT tip position, carina position, and ETT-carina distance indicated that the model predictions were not statistically different from the ground truth positions. Table  2  shows the model's performance on the Radi-oICU dataset for each radiograph quality. The best predictive performance was obtained with the more frequent radiograph qualities (poor quality, hardly visible carina) rather than with the acceptable quality radiographs.
Image segmentation The successive steps of the image segmentation performed on a chest radiograph of the RadioICU dataset are shown in Figs. 7  and 8. First, a region of interest centered on the ETT was obtained thanks to the good accuracy of the ETT tip position prediction (Fig. 7 ). Second, the ridge detection algorithm was applied to the chest radiograph. The resulting edge map clearly highlighted the ETT. However, other elements of the region of interest were also highlighted, in particular the horizontal cable at the bottom of the image and the small artifacts on either side of the ETT (Fig. 7 ). Third, all non-vertical elements and smaller artifacts were removed from the original image by applying the vertical filter (3) to the edge map and then performing morphological opening. The horizontal cable and the smaller edges totally disappeared, leaving the entire ETT clearly highlighted (Fig.
63 ± 0. 55 cm for the ETT-carina distance prediction  [14]. This suggests a better performance than the CarinaNet model; however, the model has low interpretability as it did not explicitly detect the ETT tip and carina. Another study evaluated a cascaded convolutional neural network model for its accuracy in predicting the ETT tip or carina position on ICU chest radiographs from the MIMIC-CXR database  [13]. The prediction error of this model on the internal test set was 0. 82, 0. 64 and 0. 86 cm for the ETT tip position, carina position, and ETT-carina distance, respectively  [13]. While these findings are similar to ours, it should be noted that this study proposed no external validation. Moreover, in both these studies the models were evaluated on radiographs for which the ETT-carina distance was already specified in the medical report.
There was a considerable difference between the prediction error of the CarinaNet model and the interrater agreement for the ETT tip position. This can be explained by the fact that the ETT was designed to be radiopaque and easily identifiable by the human eye. By contrast, the prediction error was very close to the interrater agreement for the carina position. The model performance even surpassed the interrater agreement on the carina detection when considering predictions where the level of confidence was superior to 0. 5, which was half of the dataset. These findings suggest that deep learning algorithms have different perceptive qualities than the human eye and can therefore be used to complement the human interpretation of chest radiographs. Additionally, the model predictions were not statistically different from the ground truth for the ETT tip and carina positions.
Availability of data and materials The RadioICU dataset-used for training and internal validation-is not publicly available. The MIMIC-CXR dataset-used for external validation-is publicly available at https:// physi onet. org/ conte nt/ mimic-cxr/2. 0. 0/. All the codes and model weights needed to reproduce the results are available at https:// github. com/ USM-CHU-FGuyon/ Carin aNet.
Literature assessment We assessed the literature with a twofold aim: 1) to conceptualise modelling approaches through the lens of participatory food governance (Sections 3) and 2) to find modelling approaches that could enhance participatory food governance of the city-region food systems (Section 4). Contributions published between 2002 and 2020 were found by screening scientific bibliographic databases and using snowballing methodology (i. e. following up cross-referrals within scientific and grey literatures through key words) (see Appendix A for key words). The literature selection was based on two criteria: 1) the authors' expert judgement concerning well-established modelling frameworks and their suitability for participatory processes; 2) the application of these models to food systems, focusing on operational dimensions (i.
Foodshed modelling (FM) Objective. Foodshed modelling approaches focus on quantitatively assessing an area's capacity to supply food meeting the population´s specific consumption pattern so as to achieve a certain level of selfsufficiency in all food commodities  (Zasada et al. , 2019; Vicente-Vicente et al. , 2021b)  or in a specific commodity  (Vicente-Vicente et al. , 2021a; Mouléry et al. , 2022). Methodology. Foodshed modelling approaches require expert knowledge to manage spatial databases (GIS). Strengths and shortcomings. Despite potential limitations due to lack of available data or suitability of local agro-ecological conditions for producing locally consumed food  (Schreiber et al. , 2021) , the foodshed approach can usefully inform policies  (Bala, 2014). Example: It can be used to simulate the foodshed under different diet scenarios  (Zasada et al.
Structural equation modelling (SEM) Objective. Structural models aim to analyse the relationships between unobserved variables that often reflect concepts or willingness (i. e. latent variables, like the willingness to consume local food) and observed variables (i. e. manifest variables, like the producer's location and the consumer's income) that may drive or explain the former. Methodology. Manifest variables can be measured from available databases (e. g. census), specific publications (e. g. geographical measurements) and surveys. Strengths and shortcomings. SEM requires expert knowledge to hypothesise the relationships between the variables and to code them in modelling computations. Both processes usually employ participatory methods and can take several months. However, structural models produce easily readable graphs and diagrams.
Background: Transcatheter mitral valve implantation (TMVI) is a novel therapeutic option for treating symptomatic mitral valve disease. Evaluating patient anatomical suitability is a critical step in the TMVI screening process, but currently requires specialized software and computerized device models. Aims: This analysis sought to assess the effectiveness of simple and standardized multislice computed tomography (MSCT) anatomic measurements for their ability to discriminate between patients who passed anatomical screening for Tendyne™ TMVI. Methods: Subjects screened for the Tendyne Expanded Clinical Study from January 2016 through September 2019 were included. Core laboratory screening measurements included mitral annular (MA) dimensions at end-systole and end-diastole, simulated device implantation, and neo-left ventricular outflow tract (LVOT) area.
| INTRODUCTION 2] [3]  Patient anatomy significantly affects suitability for a TMVI procedure, and frequent reasons for anatomical exclusion of patients being considered for TMVI include mitral annular (MA) dimensions outside the range treatable by available prostheses, or a small predicted neo-left ventricular outflow tract (LVOT)   with anticipated increased risk of LVOT obstruction. 4, 5 MA measurements and simulation of the neo-LVOT are typically based on multi-slice computed tomography (MSCT), requiring dedicated MSCT postprocessing software and access to computerized models of the respective prosthesis type and size, as well as time-consuming, dedicated anatomical segmentation. 4, 6 e-screening measurements that provide a simpler likelihood of anatomic suitability for TMVI, that do not require device simulation and that can be performed without advanced postprocessing, are therefore desirable to facilitate TMVI screening.
Results of such an assessment could inform whether a more sophisticated CT simulation is warranted for a specific patient to further determine anatomic suitability, streamlining the screening workflow. The primary objective of this analysis was therefore to assess the predictive value of simple and standardized MSCT-derived anatomic measurements for their ability to accurately discriminate between patients who passed anatomical screening for potential TMVI in the multi-center Tendyne™ Expanded Clinical Study (ClinicalTrials. gov Identifier: NCT02321514).
| Statistical analysis All statistical analyses were performed using JMP 14. 0 (SAS Institute Inc). Normally distributed continuous variables are presented as means ± SD, continuous variables not normally distributed (Shapiro-Wilk test, p < 0. 05) are presented as median (interquartile range, IQR). Receiver operating characteristic (ROC) curves were constructed for all anatomic variables of interest. The area under the curve (AUC) was used to characterize the performance of each anatomic measurement in correctly classifying subjects as anatomically suitable. The sensitivity, false positive rate (1-specificity), positive predictive value, and negative predictive value were calculated at exemplary cutoffs. In this analysis, the positive condition is considered passing the screening process and undergoing treatment.
| DISCUSSION The key findings of this analysis are that simple MSCT-based dimensions of the MA (IC diameter) and left ventricle (LVESD) have a high discriminatory capability to determine whether a subject is likely to anatomically to screen-pass Tendyne-TMVI screening when undergoing subsequent advanced computerized postprocessing. These findings may facilitate and streamline patient work-up, and avoid cumbersome and time-consuming MSCT analyses in patients with low prospect of anatomical screen-pass (Central illustration 1). Cardiac MSCT has been integral part of patient screening in firstin-human, early feasibility, and pivotal studies of investigational TMVI devices. 10 MSCT analysis is typically performed using advanced postprocessing software, allowing for three-dimensional MA segmentation and subsequent simulation of the TMVI procedure by superimposing a stereolithographic representation of the valve onto the CT data set, matching the MA segmentation.
| Limitations This analysis has several limitations. First, the analysis is limited to a single TMVI device type and retrospective derivation cohort, lacking validation in a separate cohort. Second, the patient population may suffer selection bias. The vast majority of patients had secondary mitral regurgitation. Patients with severe MAC were excluded from this analysis, and thus results may not be transferrable to this particular patient population. Furthermore, findings are device specific and are valid only for the specific TMVI device investigated here. Finally, measurement data were derived from highly standardized analysis performed by experienced cardiac imagers in an independent core laboratory and not by multiple investigators at a site level, potentially underestimating the impact of measurement error.
Acknowledgments: The authors would like to acknowledge the skilled technical help of  Simon Dumolard ,  Ines Lopez ,  Élise Ansart , and  Laetitia Freymond , and the scientific support of  Aurélien Bornet  (NMR analyses),  Laure Menin  (MS analyses),  Alexander Nielsen  (purification of Talys),  Yann Lavanchy  (FTIR and purification of Talys), and  Rita Therisod  (STEM and EDX analysis). The  Ecole Normale Supérieure (Paris , http://www. chimie. ens. fr/?q=en) is gratefully acknowledged for M. W. 's fellowship.
Descriptions of models and data assimilation systems We evaluate seven ESMs using AOD measured during ORACLES 2016 and five ESMs using data from ORACLES 2017. The treatment of aerosol processes and the assumed microphysical and optical properties per species are significantly different among the ESMs. Table  1  describes the grid resolution, process schemes, meteorological parameters, emission sources, and other key features of each model. Modern-Era Retrospective-analysis for Research and Applications-Version 2 (MERRA-2) was developed at NASA's Global Modeling and Assimilation Office (GMAO)  (Gelaro et al. , 2017; Randles et al. , 2017)  using the three-dimensional variational data assimilation Gridpoint Statistical Interpolation (GSI) meteorological analysis scheme  (Wu et al. , 2002; Kleist et al.
We only use the clear-sky data in the HSRL-2 measurements since partitioning AOD between the FT and the PBL is possible only when the HSRL-2 does not identify cloud presence (i. e. , a cloud-free condition) below the instrument. In the second analysis, we aggregate modeled and aircraft AODs to 1° grid resolution, which is approximately the median native grid resolution of the ESMs that we examine in this study. Typically, up to 100 points of aircraft data are averaged into a 1° grid box. Varying the aggregated grid resolution mainly affects standard deviations and has a very minor influence on other statistics such as correlations and root mean square error (RMSE). In all comparisons throughout this study, we compare modeled AODs with aircraft-based AODs over the same altitudinal ranges. In FT AOD comparisons, whether a full-column or partial-column modeled FT AOD is evaluated against the HSRL-https://doi.
Acknowledgements Part of the computation in this paper was performed at the  Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma (OU). We acknowledge use of the WRF-Chem preprocessor tool (mozbc, fire_emiss) provided by the  Atmospheric Chemistry Observations and Modeling Lab (ACOM) of NCAR. YF would like to acknowledge the support provided by the  U. S. DOE Office of Science , under Contract No. DE-AC02-06CH11357 , and thank the  Energy Exascale Earth System Model (E3SM)  project funded by the  DOE Office of Biological and Environmental Research  and all the  E3SM  project team members for their efforts in developing and supporting the E3SM.
Data availability The P3 and ER2 data (ORACLES Science Team, 2020a, b, c) are available through https://doi. org/10. 5067/Suborbital/ORACLES/ER2/2016_V3 , https://doi. org/10. 5067/Suborbital/ORACLES/P3/2016_V3, https://doi. org/10. 5067/Suborbital/ORACLES/P3/2017_V3.
This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over ∼1 deg 2 in the AEGIS field. We developed two algorithms based on artificial neural networks (ANN) to classify objects into four categories: stars, galaxies, quasars at low redshift (z < 2. 1), and quasars at high redshift (z ≥ 2. 1). As inputs, we used miniJPAS fluxes for one of the classifiers (ANN 1 ) and colours for the other (ANN 2 ). The ANNs were trained and tested using mock data in the first place. We studied the effect of augmenting the training set by creating hybrid objects, which combines fluxes from stars, galaxies, and quasars. Nevertheless, the augmentation processing did not improve the score of the ANN. We also evaluated the performance of the classifiers in a small subset of the SDSS DR12Q superset observed by miniJPAS.
2022) , or the study of galaxy properties within galaxy clusters  (Rodríguez-Martín et al. 2022 ) and groups  (González Delgado et al. 2022). Unfortunately, the data available for spectroscopically confirmed sources in the miniJPAS area are not sufficient to train and test ML algorithms for our purpose. Therefore, we employed mock data developed by  Queiroz et al. (2022) , and used the sources identified spectroscopically within the AEGIS field by the Sloan Digital Sky Survey (SDSS;  York et al. 2000)  in the DR12Q superset catalogue  (Pâris et al. 2017 ) as truth table. Modern deep ANN generally perform better than traditional methods. They remain poorly calibrated most of the time, however  (Goodfellow et al. 2014; Guo et al. 2017). The probabilities associated with the predicted label classes may be overly confident because they do not correspond to true likelihoods.
Consequently, objects are classified as part of one class or another with high probability regardless of the prediction accuracy. Realistic probability distributions are particularly important for spectroscopy follow-up programs that typically prioritise the observation of high-probability objects of some particular class. Furthermore, some objects are indeed dual in nature. Although we consider as QSOs only galaxies with an extremely luminous active galactic nucleus (AGN), there are objects in which a significant fraction of the detected light comes from the stars in the host galaxy. In this scenario, ML algorithms should ideally provide a high probability in both classes. In a recent paper,  Zhang et al. (2017)  proposed an original idea called mix up to enlarge the dataset and improve the generalisation of the trained model, thus increasing the robustness to adversarial examples.
In  Rodrigues et al. (2023) , we study other relevant aspects, such as the stellar types that are more frequently confused with the QSO population, the J-PAS feature importance, or the stability of the CNNs predictions with respect to minor changes in the training set. In  Pérez-Ràfols et al. (in prep. a) , we additionally adapt the code SQUEZE  (Pérez-Ràfols et al. 2020)  to work with J-PAS data. This code is based on optical emission line identification to separate between QSOs and non-QSOs and also estimates the redshift of QSOs. Ultimately, all these codes will be merged in a combined algorithm  (Pérez-Ràfols et al. , in prep. b ) so as to classify the miniJPAS sources more efficiently and to provide a high-redshift QSO target list for a spectroscopic follow-up with the WEAVE multi-object spectrograph survey  (Dalton et al.
Artificial neural networks The ANN were coded with the Tensorflow  (Abadi et al. 2016)  and Keras libraries  (Chollet et al. 2015)  in Python. The ANN has eight hidden layers with 200 neurons each. As a regularisation technique, we used weight constraints and imposed a maximum value of two in each neuron (kernel constraint). We also removed 15% of the neurons in each layer. We used the rectified linear unit (ReLU) as our activation function  (Nair & Hinton 2010). Weights were initialised with the He initialisation strategy  (Géron 2019). The loss function we employed is the cross entropy. We trained two models that use two different sets of inputs, called ANN 1 and ANN 2. The inputs of ANN 1 (59 in total) were relative fluxes, that is, the flux in each filter was divided by the flux in the r band.
The expected calibration error (ECE) is then defined as ECE = 1 N c N c j=0 N b m=0 |B m j | N b |acc(B m j ) -con f (B m j )|, ( 12 ) where N c is the number of classes, and N b is the number of bins. The lower the value of the ECE, the better the calibration of the model. However, the output of the ANN only represents true probabilities under the assumption that our mock sample and the miniJPAS observations do not differ essentially. In order to have a better estimate of the ECE of the model, we would need to compute this metric in a sufficiently large true table, which is not yet possible. In the future, when more data will be gathered, we will be able to employ this metric on observations and evaluate the ECE of the ANN properly. In the remainder of the paper, we refer to the outputs of the ANN as probabilities, recalling that they are simply a proxy of the true probabilities.
Results In this section, we test the performance of the algorithms on the mock test sample (Sect. 4. 1). We discuss in detail the effect of augmenting the data through hybridisation and we compare the differences between classifiers. Finally, we evaluate the classification obtained with SDSS objects observed by miniJPAS in the AEGIS field. Fig. 1. f W 1 score for different magnitude bins as defined in Eq. (  14 ), and the score for the full sample (ALL BIN). Dashed (solid) lines represent the models trained with the hybrid (original) training set. ANN 2 and ANN 2 mix are trained with colours, while ANN 1 and ANN 1 mix are trained with fluxes (see Sect. 3. 1).
In Fig. 2 , the f 1 score is also shown for each of the classes. Overall, both classifiers (ANN 1 and ANN 2 ) are very similar, with small differences in each magnitude bin for each class. The ANN 1 classifier is slightly better probably not only because of the representation of the data (relative fluxes), but also because it captures the sky background better. As expected, the accuracy of the classifiers decreases for fainter objects. The performance obtained with the hybrid set is very similar to the original training set, suggesting that the latter already contains all the variance needed, and more examples do not necessarily imply a better performance (but see the next section for a more detailed discussion). The confusion matrices as a function of the magnitude bin for the ANN 1 model are shown in Fig.
After applying hybridisation, we generated two new training sets that were five and ten times larger than the reduced set, respectively, known as the reduced hybrid set ×5 and the reduced hybrid set ×10. We then compared the performance of ANN 1 in the mock test sample. In Fig. 7 , we show the difference between the f W 1 score in each one of the mentioned training sets and the f W 1 score obtained in the original training set as a function of the median S/N. We do not observe a significant improvement that might justify the use of hybridisation, at least in the form we implemented in Eq. (  3 ) for this particular data set. Finally, we compared the performance of ANNs with the results of RF classifier. In Fig. 8 , we show the weighted f W 1 score as a function of the median S/N in the observed filters for all classifiers.
Each bin contains roughly 1000 objects. We do not show the results of ANN 1 mix and ANN 2 mix because they are very similar with respect to their non-hybrid counterparts. The ANNs perform better and score 10% higher on average than the RF classifiers. It is remarkable that even with a median S/N of 5, the f W 1 score reaches 0. 9. We do not observe a significant difference between RF 1 and RF 2 or ANN 1 and ANN 2 , which suggests that both representation of the data encode essentially the same information. Hybridisation seems to improve the performance of the RF slightly. Nevertheless, the ECE increases from  3. 89 (3. 82) up to 4. 23 (4. 19)  for RF 1 and RF 2 , respectively. The ECE obtained with the original training set is much larger than what we found with ANN. Thus, the RF classifier tends to be under-confident in its predictions.
SDSS versus miniJPAS In this section, we test the ANN classifiers on the SDSS test sample. Figure  9  shows the f 1 score for each class and the f W 1 score. The performance of the algorithms that were trained with the hybrid set (ANN 1 mix and ANN 2 mix) are compared with the original training set (ANN 1 and ANN 2 ). Due to the limited number of objects, we did not separate these samples into magnitude bins. Most of the objects (75%) belong to BIN 1, and only three are at the faint end (BIN 2). Therefore, the f 1 score is mostly representative of BIN 1. The results of the SSDS test sample are compatible with those obtained for the mock data (∼0. 9), suggesting that the simulations reproduce the miniJPAS observations fairly well at least for magnitudes brighter than 22. 5. Unfortunately, we do not have enough labelled objects fainter than 22.