dataesr/scientific-paragraphs-categorization huggingface dataset:
In few cases, super-resolution images show new featurese. g. coloring some pixels in greenin leaf-shaped shadows or tractor tracks in the background leading to an increase in the proportion of false positives in certain microplots of the Tartas site (Figure  9b ). Training the super-resolution model with a larger dataset might help the generator network to limit those artifacts. Alternatively, some studies  [39] ,  [71] ,  [72]  have proposed to integrate the training of the super-resolution model with the training of the Faster-RCNN. The use of a combined detection loss would provide additional information on the location of the plants, thus forcing the super-resolution network to differentiate between plants and background while up-sampling the images.
Conclusion We evaluated the performances of automatic maize plant detection from UAV images using deep learning methods. Our results show that the Faster-RCNN model achieved very good plant detection and counting (rRMSE=0. 08) performances when high-resolution images (GSD0. 3cm) are used both for training and validation. However, when this model is applied to the low-resolution images acquired at higher altitudes, the detection and counting performances degrade drastically with rRMSE=0. 48. We demonstrated that this was mostly due to the hyper-specialization of Faster-RCNN that is expecting plants of similar size as in the training dataset. The sensitivity of the detection method to the object size is a critical issue for plant phenotyping applications, where datasets can be generated from different platforms (UAVs, ground vehicles, portable imaging systems, etc.
) each one of them providing images within at a specific ground resolution. Concurrently, it would be optimal to share labeled images to get a wide training dataset. Data augmentation techniques where high and low-resolution images populate the training dataset were proved to be efficient and provides performances similar to the ones achieved when the model is trained and validated over the same image resolution. However, the native low-resolution images acquired from the UAV have significant low quality that prevents accurate plant detection. In some cases, the images are difficult to visually interpret which poses a problem both for their labeling and for the detector to localize plants due to the lack of pertinent information. These low-quality images were characterized by a loss of image texture that could come from camera intrinsic performances, inadequate settings and the jpg recording format.
It is thus recommended to pay a great attention to the camera choice, settings and recording format when the UAV is flying at altitudes that provides resolution coarser than 0. 3 cm for maize plant counting. Finally, we evaluated a super-resolution Cycle-ESRGAN based method to partially overcome the problem of sub-optimal image quality. The super-resolution method significantly improved the results on the native low-resolution dataset compared to the classic bicubic upsampling strategies. However, the performances when applied to the native low-resolution images were moderate and far poorer than those obtained with the native high-resolution images with simulated super-resolved images showing sometimes artifacts. A future direction to reduce the artifacts of such super-resolution algorithms can be to integrate the GAN training along with the training of the plant detection network.
TartasFigure 8 8 Figure 8 An example showing the same plants extracted from the exact same locations in two versions of the validation dataset: native LR ( ) and the synthetic LR obtained from gaussian downsampling ( ). The first and third column show the raw images while the second and fourth column shows the detector predictions. The ground truth bounding boxes are shown in green and the predicted bounding boxes are shown in blue. The green text indicates the IoU of the predicted box with the ground truth and the blue text indicates the confidence score of the predictions.
The plates were designed with AutoCAD software and 3D printed (Ultimaker 2 +) using a white PLA (polylactic acid) filament , at a resolution of 0. 6 mm. The cost of one system (including the webcam, the LED panel, and the plates) is less than 400 $ US. Instructions for building this experimental system are freely available on a dataverse  (Sanane et al. 2020b). In this experiment, two to four webcams (Logitech C920 HD Pro) were connected to the USB ports of a PC computer. Time-lapse images were taken every minute from each camera by an open-source video surveillance software (Vision GS BE 3. 1. 0. 4) as 1920 × 1080 pixels jpg files and stored to a disk.
The corresponding area is overlayed in red over the original image. On this figure, we have selected and zoomed over 4 cages at the beginning and at the end of an experiment. The whole stack of images is then analyzed to measure the number of corresponding pixels in each ROI. The filtering and measuring (and data export) are done interactively under ICY, using a custom plugin AreaTrack c. Evolution of the surface of the 4 disks represented in Fig. 2b  results. Although this approach worked well in many cases, some experiments could not be analysed this way, because the leaf colours and transparency were such that none of these filters allowed enough contrast between the leaves and the background over the whole image. We introduced another approach, to filter all pixels whose colour fell were close to an array of reference colours.
Once ROIs (to define the cage limits) and the colour filters were properly defined (to extract the leaves), the entire set of data could be analyzed. The surface of the leaves was estimated by counting the number of pixels over the threshold in each ROI and in each image. The resulting measures were exported as an Excel file, including (i) the raw measure of surface for each ROI (i. e. the number of pixels over the threshold in each cage), (ii) a running average of these L1 = | | R i -R k | | + | | G i -G k | | + | | B i -B k | | L2 = √ R i -R k 2 + G i -G k 2 + B i -B k 2 measures, computed over consecutive images (generally n = 10) and (iii) a running median. Data Analysis Series of R scripts were written for further data processing and statistical analyses. The scripts, along with the raw data and documentation are available in a dataverse  (Sanane et al.
Data Clustering The whole dataset, comprising the experiments performed with NeemAzal, quinine, and the control (4 batches, 24 plates, 1200 cages corresponding to 1200 consumption curves), was used to run the unsupervised clustering algorithm SOTA  (Herrero et al. 2001)  on the individual curves to obtain 14 clusters. The total number of clusters was empirically chosen to avoid having clusters containing only a couple of curves and proved to be robust over the different experiments.
Results Feeding Activities During these experiments, larvae exhibited different behaviours, ranging from immediate feeding until the whole disk was consumed to not feeding at all. We defined feeding types based upon the kinetics of their feeding. First, by considering the time course of the feeding of each of the larvae tested, we defined a typology of possible behaviours exhibited in the whole dataset. Second, we compared the distribution of behavioural types between treatments of the same product. Feeding Types According to this approach, the individual consumption curves can be classified into 6 different types using the SOTA algorithm, which is particularly adapted to the classification of temporal data. By running this method on the 1200 consumption curves, we obtained 14 different clusters of curves (Fig.
Bioassays Confirm Neemazal as an Antifeedant After using the whole dataset to attribute a behavioural type to each individual (Fig. 3 ), the effect of each treatment (i. e. antifeedant concentration) was analyzed separately for each antifeedant product by a multinomial regression (see Methods). Each analysis included the data of each dose of antifeedant and the controls, and resulted in the estimations of the probability distribution among behavioural types for each concentration of each product that is represented in Fig. 4. A Wald test was used to test the significance of the comparisons between treatments (SI-Table  1  and SI-Table  2 ). In the NeemAzal bioassays, an increasing concentration of the product was associated with a decreasing proportion of A and B larvae and an increasing proportion of E and F larvae, while intermediate concentrations corresponded to a higher proportion of C, D, E larvae (Fig.
To understand the extent to which AOD estimates the spatial variation in dust emission magnitude and frequency we calculated the probability of dust occurrence modelled by the dust optical depth (DOD>0. 2) using the criteria established previously  (Ginoux et al. , 2012). We note the stated limitations of DOD to be largely restricted to bright land surfaces in the visible wavebands which implies reduced performance over areas where vegetation is present. To calculate DOD, we used wavebands available from monthly Moderate Resolution Imaging Spectroradiometer (MODIS; MOD08 M3 V6. 1) at a 1-degree pixel resolution  (Platnick, 2015). The DOD was retrieved from those pixels in which dust emission was observed from point sources (DPS) in space and time throughout 2001-2016. All available MODIS DOD data were used, quality flags were not used to filter these data.
1 & 5) with R(z0, z0s)≈0. 91 fixed over space and static over time. Following the current practice, we calculated  * from the modeled 10 m wind velocity using the logarithmic layer profile theory and aeolian roughness length  (Darmenova et al. , 2009)  (details are provided in the Appendix). In the TEM we allowed soil moisture to vary and used MODIS Normalised Difference Vegetation Index (NDVI only in the TEM) data to calculate the bare soil fraction E. For comparison, we used the AEM (Eqs. 3, 4 & 5) with soil wind friction velocity   * / 10 obtained from MODIS albedo (MCD43A3; Collection 6) varying daily, every 500 m pixel across the study area. MODIS is aboard polar-orbiting satellites which cause incomplete coverage. However, the variation in roughness at the daily scale is so small that we were able to smooth the available data to improve the coverage.
Soil clay content was represented with a digital soil texture map  (Dai et al. , 2019)  and used in both models (see Methods). All data were available from the catalogue of the Google Earth Engine (GEE)  (Gorelick et al. , 2017)  which then required no downloading and reformatting. We used the Java script coding environment to calculate daily dust emission (kg m -2 y -1 ). Given the availability of DPS validation data at sites in south-western USA, we restricted the mapping to North America including dust source regions bordering the USA. Testing the code and visualising the results for smaller time periods across the study area was almost instantaneous in the GEE. Data processing at 500 m and daily resolution between 2001-2020 across North America took typically less than 12 hours. These data were exported from the GEE for the calibration / validation in a Python coding environment and images (TIF) from the GEE were also exported for manipulation and presentation using ArcGIS Pro.
Data Availability 465 The data used are identified in the main text and below using the the Google Earth Engine data description and catalogue references, link and DOI. When the dust emission scheme  (Marticorena and Bergametti, 1995)  is implemented (Eq 2 and 4), the wind friction velocity ( * ) is assumed to be the total  *. We set c=1, the air density was fixed for simplicity (  =1230 g m -3 ). The acceleration due to gravity was also fixed (g=9. 81 m s -2 ). Following the current practice, we calculated  * from the modeled 10 m wind velocity using the logarithmic layer profile theory and aeolian roughness length  (Darmenova et al. , 2009)  following the Monin-Obukhov similarity theory:.
Such an evidence base would also indicate the extent to which different countries are investing into relevant actions, and where funds or political pressure may be targeted to enhance the economic security of both nations and wider trading blocs. In this context, the InvaCost initiative  (Diagne et al. 2020a (Diagne et al. , 2020b) )  tackles this lack of collated data, presenting a comprehensive and urgently-needed database that can be used to thoroughly investigate the costs of IAS at a range of scales, from subnational to continental. Here, we use the InvaCost database to (i) describe Europe-wide impacts of IAS among countries, cost types and economic sectors, (ii) investigate the causes for differences in these costs among European countries, and (iii) examine the temporal trends in costs of IAS in recent decades.
Data compilation and extraction IAS in InvaCost represent those which have established and spread in novel ranges and have reported socioeconomic impacts (i. e. monetary costs). To estimate the cost of biological invasions on the European economy, we used the InvaCost database (InvaCost v. 1. 0; Diagne et al. 2020a  and subsequent additions, see below). The InvaCost v. 1. 0 database comprises 2,419 entries of reported economic costs of IAS retrieved from published peer-reviewed and grey literature (as of December 2017). Data in InvaCost v. 1. 0 were retrieved from publications in English identified in the Web of Science platform (https://webofknowledge. com/), Google Scholar (https://scholar. google. com/), Google (https://www. google. com/), and through direct contacts with regional experts.
InvaCost is a living database for which correction of potential errors and addition of new cost entries are further expected  (Diagne et al. 2020a ). The InvaCost v. 1. 0 database has been extended recently with 5,212 data entries from non-English sources  (Angulo et al. 2020). This dataset was derived from a search in fifteen languages, including languages relevant for Europe: French, Spanish, Portuguese, German, Greek, Dutch, Ukrainian, and Russian (as of May 2020). The cost search protocol was similar to the original InvaCost protocol  (Diagne et al. 2020a ); however, the majority of these entries resulted from targeted searches, i. e. via searching web pages and directly contacting IAS experts and stakeholders to request for potentially unpublished/publicly unavailable documents containing cost information.
Data processing First, we cleaned the raw data in the InvaCost database. We removed obvious duplicate or overlapping costs, identified through chains of citations or identical cost details. Where necessary, we split aggregated costs (e. g. if the InvaCost database contained a single cost for Europe but the original source contained costs for each individual country). The period of estimation across reported costs varied considerably, spanning periods of several months to several years. For the purpose of the analysis, and in order to obtain comparable IAS costs, we considered all costs for a period of less than a year as annual costs, and re-calculated costs covering several years on an annual basis. This was performed using the "expandYearlyCosts" function of the 'invacost' package version 0.
3-4  (Leroy et al. 2020 ) in R version 4. 0. 2 (R Core Team 2020). We thus estimated average annual costs represented in the InvaCost database. Deriving the total cumulative cost of invasions over time requires consideration of the probable duration time of each cost occurrence. The duration consisted of the number of years between the probable starting ("Probable_starting_year") and ending ("Probable_ending_year") years of the costs reported by each publication included in the InvaCost database  (Diagne et al. 2020a ). When information was missing for the starting year, we conservatively considered the publication year of the original reference. For the ending year of costs, however, information was missing only for costs likely to be repeated over years (i. e. "potentially ongoing", contrary to "one-time" costs occurring only once along a specific period).
Therefore, we considered that these costs might still occur until 2020: the last year from which publications were included in InvaCost and in the non-English dataset. Subsequently, to obtain a comparable total cumulative cost for each estimate over each defined invasion period, we multiplied each annual estimate by the respective duration (in years). All analyses were performed for the period from 1960 to 2020, as monetary exchange rates could not be obtained from official institutions (e. g. World Bank) prior to 1960. The overall number of cost entries before expansion was 4867 and 7461 after expansion, whereby "expansion" refers to the process of annualising cost data of different durations using the aforementioned "expandYearlyCosts" function.
Economic cost descriptors To examine the costs of IAS incurred within Europe, we filtered the full dataset based on the geographic region "Europe". We provide our final dataset used as a supplement (Suppl. material 1). Naturally, these analyses include species which are native in some European countries, but invasive in others (e. g. European rabbit), but invasion costs are only documented in novel ranges. Costs that were incurred from multiple or unspecified taxa were included in analyses but categorised as "Diverse/Unspecified". The resulting invasion cost totals were examined according to different descriptive fields of the most up-to-date database available when writing this manuscript: i. Official_country: describing the national origin of the listed cost for European countries only. For technical reasons, Kosovo and Serbia were considered as one country, while Turkey was excluded entirely as costs were not clearly attributable to Europe.
Economic cost correlations We first explored whether the two main types of costs, "Management" and "Damage-Loss", can be explained by country-specific factors. To do so, we calculated the cumulative reliable observed costs for 1960-2020 of each type of cost at the country level and selected a range of socio-economic variables that we hypothesize could be linked to biological invasions (Suppl. material 3). Then, we calculated Spearman rank correlations (r s ) between the country-level expenditures and damage costs and the selected socio-economic variables using the R package 'ggpubr' (Kassambara 2017). Further, we also explored correlations between country-level expenditures and damage costs.
19. 12, www. mapequation. org; Rosvall and Bergstrom 2008). This approach groups nodes into clusters with high intragroup connectivity, enabling clusters of similar costs to be established (i. e. countries sharing costs from the same invasive taxa)  (Leroy et al. 2019). Network analyses were performed with the 'biogeonetworks' R package version 0. 1. 2 (Leroy 2020), and the network was represented with Gephi 0. 9. 2 using the ForceAtlas2 algorithm  (Bastian et al. 2009).
Temporal dynamics of accumulated costs For the temporal estimation of the average annual costs, we used the 'invacost' package in R  (Leroy et al. 2020). This package allows modelling the trend of costs over time with an array of linear and non-linear model types and enables a summary and comparison of their respective outputs. Given the evidence that numbers of IAS show no sign of saturation  (Seebens et al. 2017) , we expected their associated costs to be stable or increasing. In addition, we can expect a time lag between the occurrence of costs, their publication, and their reporting in InvaCost  (Leroy et al. 2020). Therefore, as per  Seebens et al. (2017) , we excluded recent years from model calibration. The last eight years appear to have less than 75% completeness within the global InvaCost database  (Leroy et al.
2020) ; therefore, we chose to exclude them from model calibration (i. e. years post-2013). A range of modelling techniques were then applied to model the temporal dynamics of reported costs ("modelCosts" function): ordinary least squares regressions (linear, quadratic), robust regressions (linear, quadratic -R package 'robustbase';  Maechler et al. 2020) , multivariate additive regression splines (MARS -R package 'earth';  Milborrow et al. 2018) , generalised additive models (GAM -R package 'mgcv';  Wood et al. 2016 ) and quantile regressions (quantiles 0. 1, 0. 5, 0. 9 -R package 'quantreg'; Koenker 2020). These approaches enabled quantification of average annual costs, measurements of variation in cost estimates over time and assessment of predictive performance across models (based on RMSE).
Method reliability % cost % entries Implementation %cost % entries High Low Observed Potential 0% 25% 50% 75% 100% 0% 25% 50% 75% 100% Figure 1. Nature of reported costs (monetary totals and numbers of database entries) for IAS across European countries according to percentages considering method reliability (high vs. low) and implementation type (potential vs. observed). Highly reliable figures are from peer-reviewed, official and/or reproducible sources; observed costs have been empirically realised (i. e. excluding expected cost estimations). A list of the costliest invasive alien species in Europe can be found in Table  1. Considering all costs, five invertebrates, three vertebrates, and two plants were present in the top 10. When considering only reliable observed costs, three invertebrates, four vertebrates, two plants and one fungi genera were included in the top 10.
9: dark grey (b) between 1960 and 2020, as well as reliable observed costs, MARS (c red) and quantile regressions; from bottom to top: 0. 1: light grey, 0. 5: grey, 0. 9: dark grey (d) between 1970 and 2020. Error bands on MARS represent prediction intervals (i. e. the interval of cost that any individual year can have). Error bands on quantile regressions represent 95% confidence intervals. Yearly data are triangles (until 2013) and circles (after); only the former are used in the models. between 1960-1969 was below US$0. 16 billion, it increased to an average annual cost of US$6. 35 billion per year in 2010-2020. Considering only reliable, observed costs, the first database entry occurred a decade later than when considering all costs, totalling at an average annual cost of US$963. 9 million per year (€802.
2009 ). However, the west-European dominance in IAS costs may also be explained by the limited reporting of costs for Eastern European, and potentially also some Nordic, countries. Additionally, the limited reporting of the invasion costs may partly be attributed to the gap of the InvaCost database in sources/documents in languages other than English. The non-English data were collected for only a subset of European languages  (Angulo et al. 2021b) , leaving aside several languages from Eastern and Northern Europe (e. g. Romanian, Hungarian, Serbian, Polish, and Nordic languages -Finnish, Swedish, Danish etc. ). For Eastern European countries, e. g. those of the former communist bloc, one reason for their low reported costs may be that up until 1990, there was little documentation of monetary impacts or, if there was, this information was not made publicly available.
2002; Ahmed et al. 2021) , and that countries with a higher proportion of money spent on biosecurity experience generally lower damage costs  (Jay et al. 2003; Kritikos et al. 2005). The InvaCost data also indicate more than a 10-fold increase every ten years in costs associated with IAS since 1960. This finding is likely a result of several trends: foremost the increasing number of IAS in Europe  (Seebens et al. 2017) , global cost trends  (Diagne et al. 2021a; Cuthbert et al. 2021c ) and the increasing number of publications within the field of invasion science  (Richardson and Pyšek 2008). This is followed by the increase in the GDP of most European countries; and the increasing awareness and number of legislative instruments (at national and EU levels) adopted to tackle IAS (Garcıa de Lomas and Vilà 2015;  Turbelin et al.