dataesr/scientific-paragraphs-categorization huggingface dataset:
Data treatment and analysis Local pest names given by retailers were harmonized using OpenRefine and compared with a synonymy table built by local experts to determine pest species (Spanish or scientific names). Wrong species identification and broad terms such as larva (gusano) or insect (insecto) were considered as incorrect as they are not appropriate for providing proper recommendations of chemical control. Commercial names of chemical products recommended by retailers were also harmonized with OpenRefine and then compared with national pesticide databases to enrich the dataset with several variables (e. g. active ingredients, concentration, toxic category). Unmatched commercial names were excluded from product and dosage assessments. When the concentration of a recommended product was not specified and matching products in the database showed several possible concentrations, the product was excluded from dosage analysis only (S3 Fig, Supplementary Material).
Product comparisons were achieved at national level because several products with identical commercial names among countries may have different formulations and/or concentrations. All products were subsequently pooled together and their active chemical substances (and CAS number) were determined from Wikidata through OpenRefine reconciliation function. Pesticides' mode of actions were determined matching active substance with the IRAC classification v9. 4  [45]  for insecticides and FRAC classification  [46]  for fungicides. Adequacy between retailer's recommendation and recommended use from datasheets was assessed in a three-step manner. First, the broad adequacy between pest phylum (insects or fungi) and product type (insecticide or fungicide) was assessed. Second, the presence of pest name (either Spanish or scientific name) and crop on the products' datasheets was checked for.
The first classification was based on the World Health Organization toxic categories (I, II, III and IV) identified from product labels' color. This classification was used for the expert model recommendation (see below) as it depends on a national classification, and is therefore a relevant indicator for a retailer to choose between products. The second classification used the EU Pesticides Database  [47]  to identify active substances not approved in the EU, reflecting potentially hazardous substances. While WHO toxic categories vary for the same active ingredient and between countries, the EU classification is defined for active substance and was therefore used to characterize the potential hazard of the active ingredients recommended.
The current study also revealed that 23. 7% (n = 182) of the products recommended were absent from the official pesticide databases (S3 Fig, Supplementary Material). These products may come from the black market, and could therefore be more toxic or less effective than official products  [22]. However, the current study could not take these products into account due to the inherent lack of information. Similarly, adjuvants that help to stabilize pesticide compounds were not taken into account, even though their presence can increase toxicity  [63]. The expert-retailer model may have underestimated toxicity because it only relied on products recommended by retailers. Another approach would be to feed the model with the complete official pesticide databases, from which less-toxic products absent from our survey could be recommended.
Conclusion This study offered for the first time reliable information about the quality of pesticide recommendations provided by pesticide retailers in the Andes. The empirical approach using fictional stories allowed to retrieve information close to what small farmers experience when seeking pest management advice. Moreover, by comparing recommendations with legal references from product datasheets and by enriching pesticide data from international databases, the adopted methodology allowed to identify margins of improvement at the root of pesticide misuse. The results showed that more than 88% of all recommendations were erroneous during at least one step of the recommendation process. These recommendations proposed pesticides pertaining to a few modes of action and roughly half at inappropriate doses, therefore increasing the risk of pest resistance.
The reaction was performed for 90 min at 30°C under continuous shaking in an Eppendorf thermomixer. A volume of 90 µL of N-methyl-N-trimethylsilyl-trifluoroacetamide was then added and the reaction continued for 30 min at 37°C. After cooling, 80 µL were transferred to an Agilent vial for injection. Four hour after derivatization, 1 µL sample was injected in splitless mode on an Agilent 7890B gas chromatograph coupled to an Agilent 5977A mass spectrometer (column: RESTEK RXI 5SIL MS 30MX0. 25MMX0. 25UM). An injection in split mode with a ratio of 1:30 was systematically performed for quantification of saturated compounds. Raw Agilent data files were analysed with AMDIS http://chemdata. nist. gov/mass-spc/amdis/. The Agilent Fiehn GC-MS Metabolomics RTL Library was employed for metabolite identifications.
As part of the Internet of Food & Farm project for the adoption of IoT in the agri-food sector, this paper presents the design, implementation, and evaluation of an end-to-end and open IoT system architecture. This use case aims to further improve the entire viticulture value chain in a renowned estate of 135 ha in the Bordeaux area (France). Based on the first generation of end-devices (~74) and gateways, the team designed the system's architecture and relied on a LoRa network to collect and analyze the environmental data that would ultimately allow them to optimize resource consumption, improve vine yield and wine quality.
Introduction Big Wine Optimization (BWO) was part of IoF2020 (Internet of Food and Farm 2020) [1] project, a H2020/Large Scale Pilot funded by the European Union to accelerate the adoption and deployment of IoT technologies in the agri food sector. Electronic components and systems plays a key role in precision agriculture and viticulture  [2] , bringing efficient and cost-effective solutions  [3]  to provide decision making data to the winegrowers  [4]. BWO aims to: 1) Improve the vine yield and wine production by defining and implementing an IoT system able to gather the data, coming from different vineyards and wineries, to perform data analysis, system and risk management, and decision making. 2) Provide to winegrowers and producers new tools to optimize resources (manpower, fertilizers, materials, electricity, water, etc.
Material and Methods This project started in 2017, when the IoT solutions were based on GSM communication using SIM-card  [5]. At that time, new communication solutions arrived on the market as SigFox [6] and LoRa  [7]. From the beginning of the project, the choice was to implement a Local Area network based on LoRaWAN. The rationale behind this choice was for technological and economic reasons. LoRaWAN supports the upstream and downstream communication, necessary to deploy a local area network supporting sensors and actuators. SigFox was mainly designed to support the upstream communication. GSM and 3G/4G solutions were not the right choice due to the business model (payment based on the number of sensors and among of data transmitted) and low autonomy, the devices were not optimized low-power consumption From another side, LoRaWAN was subscription free (cost saving in communication for the farmer) and allowed to build an ad hoc network according to the farm needs.
Description of the experimental site The goal of this deployment is to validate in large scale that an IoT system architecture can be scale up and correctly working end-to-end system with more than 74 end-devices (around 150 sensors) to monitor the vineyards and wineries of the 5 vineyards constituting DDD. The data gathered from the end-devices distributed in the estate are analyzed for decision-making to improve the vine yield and wine quality. The first sensors were deployed in 2018 in Doisy-Daëne and Floridène domains and were extended in 2019 to Reynon, Cantegril and Haura. Edge domain: it contains the end-devices interacting with the environment to measure the weather conditions and the vine phenological stages of vineyards as well as the environmental conditions in the wineries. Cloud domain: A secured LoRaWAN server is used to subscribe and monitor the sensornodes deployed, to recover the data coming from the LoRaWAN gateways and in conjunction with the SensiNact and FiWire middleware, they decapsulate the data and store it in a database for its analysis.
LoRaWAN Network Five LoRaWAN Gateways were deployed to gather the data coming from end-devices distributed in the vineyards and wineries in DDD. In each Doisy-Daëne and Cantegril domains was installed a LoRa Lorrier LR2 gateway. Three Kerlink gateways were deployed in Reynon, Floridène and Haura premises. The end-devices encode the raw data in Cayenne LPP  [11]  and send them, through the LoRa protocol, to the gateways where they are encapsulated in the IP protocol. These new frames are sent by the ADSL modem, available in each winery, through Internet to local cloud. To ensure a good communication, the local connection between the gateway and the ADSL modem is done via Ethernet rather than WiFi.
Software The software designed performs the processing at each system domain and ensure endto-end system communication from data gathering by the sensors to the display of its values in the Process2Wine interface. Process2Wine [14] is a commercial vineyard and winery management software from Ertus Group and extended to support IoT. The software used is the following. Camera embedded software: CapTrap sensor uses deep learning techniques to detect and count the insects in the field of the camera. The FixCam sensor uses NDVI computation and thresholding to estimate the vegetation surface on each image. For both cameras, the software in charge of these tasks is embedded in the end-device. The MobiCam uses both classical images tools (Hough Transform, Classification…) and Deep Learning techniques to detect vegetation and count the visible berries.
The embedded computer performances of the MobiCam being insufficient for real time processing, the images stored in a memory card are processed later on a desktop computer. A new MobiCam is under development to process the images online and send the results (metadata) through the LoRA network. LoRa Server: TTN was used at first as LoRa server, to test and validate the end-toend LoRaWAN communication. However, this solution did not match the project goals: to have a secure solution, to avoid dependency from third suppliers, to avoid unnecessary communication overhead. i. e. , the goal was to have a local area network and mastering all the software components to easily commercialize the project results. For this reason, the LoRa server software was customized for our system solution. Middleware: sensiNact  [15]  and FiWare [16] were integrated and debugged in the developed IoT System.
Thanks to the adoption of Cayenne LPP, few additional developments were required to easily adapt the data sent by the new end-devices such as the water and electricity meters. This showed the architecture openness. User Interfaces: These interfaces (for desk and mobile), based on Process2Wine, were extended with an IoT Module to manage the information coming from each sensor deployed in the vineyards and wineries of DDD. The desktop interface allows to configure the sensors and to manage the access rights for the data display according to the users' rights. The mobile interface is a simplified interface that also allows viewing the data, coming from the sensors, generally filter by winery and vineyard. For instance, selecting by Vineyard in the interface, the user can see the last values received from the sensor selected.
Discussion The main lessons learned, and actions taken between the beginning of the project and the IoT system large scale deployment in the DDD estate were the following. LoRaWAN deployment: 1) To protect electricity and telecom networks against lighting: antenna with lighting protection was installed for each gateway. 2) To avoid the impact of the Voltage variation (which often happens in the farm premises), on the electronic devices (e. g. , gateway) an inverter per gateway was installed to protect them. 3) To ensure the network coverage of the 135 ha of DDD, the distance between the endnodes and gateways should be considered as well as the antennas gain. 4) To allow network interoperability for end-devices coming from different suppliers, the adoption of Cayenne Low Power Payload (LPP) allowed, with very few modifications in sensiNact, ensuring the right data decapsulation.
Education and experience: Put in use an IoT system is not an easy task and more difficult when it is a new technology to be deployed and interdisciplinary competences are required such: electronics, computer science, communication, agronomy, viticulture experts, CAD tools designers, data scientist, etc. This problem was experienced and the learning curb took time, introducing important delay. Local support and maintenance: In top of education, one of the main issues in farming is to have well educated/trained people close to the farmers (locally) to provide support and maintenance in a short reaction time at cost effective. This is fundamental to introduce and accept by the farmer the adoption of this new technology.
Conclusion and perspectives End-to-end operation of the IoT System, based on the LoRaWAN network was validated in the 5 DDD Estate. In general, the system is working well but the reliability is not yet so good as expected. Improvements are ongoing for instance the mini-weather stations replacement by other ones smaller and with higher autonomy. A gateway should be added to improve the LoRaWAN coverage, mainly for the end-devices located at more than 10 km from the closest waterway. The algorithms used by the FixCam and the MobiCam must be completed to extend the possibilities of theses sensors use (growth stage monitoring, disease detection). Besides the FixCam, housing must be made more robust. The MobiCam image processing software must be embedded to obtain a viable "edge architecture". The validation of the data analysis models is going to get insight information from the raw data and help the winegrower to take the right decisions.
Oxylipin Analysis Oxidized metabolites of PUFA were quantified in DHA oil, omelets as well as plasma, heart, and brain by liquid chromatography combined with tandem mass (LC-QQQ) by the MetaToul lipidomic core facility (Justine Bertrand-Michel and Pauline Le Faouder, I2MC, Inserm 1048, MetaboHUB-ANR-11-INBS-0010 Toulouse, France). Briefly, frozen tissues (250 mg) were crushed with a FastPrep R -24 Instrument (MP Biomedical, Illkirch-Graffenstaden, France) in 500 µL of Hank's balanced salt solution (Thermo Fisher Scientific, Illkirch-Graffenstaden, France). After 2 crush cycles (6. 5 m/sec, 30 sec), cold methanol (300µL) and internal standard (5 µL of LXA4-d5, LTB4-d4 and 5HETE-d8) were added to homogenates. After centrifugation (15 min at 2000 g at 4 • C), supernatants were diluted in 2 mL H 2 O and then submitted to solid phase extraction using OASIS HLB 96-well plate (30 mg/well, Waters, Saint-Quentin-en-Yvelines, France) pretreated with methanol (1 mL), equilibrated with 10% methanol (1 mL), and washed after sample application with 10% methanol (1 mL).
Lipid mediators were finally eluted with 1 mL of methanol and reconstituted in 10 µL methanol prior to LC-QQQ analysis  (40). They were separated on a ZorBAX SB-C18 column (50 mm, 2. 1 mm i. d. , 1. 8 µm film) using Agilent 1,290 Infinity HPLC system coupled to an ESI-triple quadruple G6460 mass spectrometer (Agilent Technologies, Les Ulis, France). Data were acquired in multiple reactions monitoring mode with optimized conditions (ion optics and collision energy). Peak detection, integration, and quantitative analysis were done using Mass Hunter Quantitative analysis software (Agilent Technologies) based on calibration lines built with eicosanoid standards (Interchim, Montluçon, France).
Biological invasions by amphibian and reptile species (i. e. herpetofauna) are numerous and widespread, having caused severe impacts on ecosystems, the economy and human health. However, there remains no synthesised assessment of the economic costs of these invasions. Therefore, using the most comprehensive database on the economic costs of invasive alien species worldwide (InvaCost), we analyse the costs caused by invasive alien herpetofauna according to taxonomic, geographic, sectoral and temporal dimensions, as well as the types of these costs. The cost of invasive herpetofauna totaled at 17. 0 billion US$ between 1986 and 2020, divided split into 6. 3 billion US$ for amphibians, 10. 4 billion US$ for reptiles and 334 million US$ for mixed classes. However, these costs were associated predominantly with only two species (brown tree snake Boiga irregularis and American bullfrog Lithobates catesbeianus), with 10.
In this context, the monetisation of the effects of invaders is a valuable option to raise societal awareness as well as motivate and justify policy actions by decision-makers  5. The use of monetary values also allows highlighting of knowledge gaps among taxa using a common metric. The recent publication of the InvaCost database, where literature on the costs of alien species is compiled, standardised and described across a large number of indicators in a public form  31  , has increased interest in the synthesised study of invasive alien species monetary costs  [32] [33] [34] [35]. Here, we quantify and synthesise, for the first time, the economic costs of alien herpetofauna worldwide, based on the current state-of-the-art in the published literature. We expect our results to reveal high costs, but also patterns and possible spatial biases that will inform the direction of future research: (i) we expect a minority of species will occupy the majority of the research effort and costs  [36] [37] [38]  ; (ii) costs will be biased towards particular regions, as shown previously by other studies  5, 36  ; (iii) most economic costs will come from damage to primary sectors such as agriculture, due to these being commonly impacted by invasions  39  ; and (iv) temporal trends will be characterised by substantially increasing costs over time, due to the increase in invasion rates worldwide  1  and a growing number of pathways introducing herpetofauna.
Results Economic costs by order and family. The total reported costs of the invasive herpetofauna exceeded 16. 98 billion US$ between 1986 and 2020. We found that out of 280 invasive herpetofauna species recorded in the herpetofauna database  40  , 263 species did not have any economic impacts recorded in the InvaCost database (93. 92%, Supplementary Table  S1 ). For amphibians, we found that 75 of 81 species did not record a cost (92. 59%), while 188 of 199 reptiles did not record a cost (94. 47%). In particular, no cost was recorded for Caudata (Amphibia) and Crocodilia (Reptilia) entirely (Supplementary Table  S1 , Supplementary Appendix B). This lack of studies is magnified at the family level, where we only found costs for 5 out of 20 (25%) families of amphibians and 7 out of 37 (18. 91%) families of reptiles (Supplementary Table  S1 ).
3 million US$ (< 12. 50%; n = 93). For the reptiles, observed species-level costs totalled 3. 49 billion US$, and B. irregularis dominated with 3. 46 billion US$ (99. 14%; n = 38), while the remaining species (n = 14) cumulated less than 28 million US$ (< 0. 90%; n = 254). We identified significantly greater (W = 4051. 5, p-value ≤ 0. 001) numbers of publications for the species with recorded costs in the InvaCost database compared to those that did not have any record of economic costs. Further, for those species with costs, we found a significant positive correlation between the total cost of the recorded species and the number of respective publications (Adjusted R 2 = 0. 26, p-value = 0. 01) (Supplementary Fig. S1 , Supplementary Appendix B). This relationship was also significantly positive in the case of observed costs only (Adjusted R 2 = 0.
Discussion The present study found a total reported cost of global herpetofauna invasions of 16. 98 billion US$ between 1986 and 2020. However, reported costs were principally due to just a few species, namely B. irregularis, L. catesbeianus and R. marina (~ 96%). There could thus be a positive feedback loop between research effort and cost reporting-with most research focused so far on a few high-profile species. Indeed, we found significantly greater numbers of papers concerning species with reported costs than those without reported costs. While InvaCost synthesises available cost information and allows it to be distinguished using various descriptors, we note the potential variability in underlying data that reflects methodological differences across the underlying literature. In addition, we acknowledge that the differences in research effort based on the number of papers for certain species, paired with a conceivable time lag in invasion and monetised impact reporting, may alter the total economic costs found in this study.
Yet, many of these ecological impacts are not monetised or available as they are not in the Inva-Cost database, and all recorded economic costs generated by P. bivittatus thus far are from a single eradication program targeting this species in Florida  47. In addition, there is a possible cost bias as our study range is limited to 1986-2020, while some of the most notorious species were introduced long before that [e. g. R. marina were introduced in 1936 in Australia  48  ]. The lack of reported costs found for the vast majority of reported alien species corroborates other studies at various spatial and taxonomic scales, including for the United Kingdom  37  , Singapore and Southeast Asia  35  , fishes  49  , and ants 50 , among others. Concerning sectors of economic activity for amphibians, most costs impacted authorities-stakeholders.
6 billion US$  34. Alarmingly, we found a lack of economic costs for alien amphibians with substantial ecological impacts based on the Environmental Impact Classification for Alien Taxa (EICAT) and concomitant "Massive" effects on recipient communities, such as the tiger salamander (Ambystoma tigrinum) and Italian pool frog (Pelophylax bergeri)  41. However, the impacts of these species appear to be characterised by hybridization  52, 53  , and therefore these impacts are difficult to quantify economically due to their ecological nature. Regarding reptiles, we observed that three of the five most notorious aliens (A. carolinensis, Anolis sagrei and B. irregularis  54  ) had reported economic costs, but we did not find economic costs for the remaining two: the curious skink (Carlia ailanpalai) and common house gecko (Hemidactylus frenatus) in the InvaCost database.
On the contrary, 149 entries were reported from 2016 to 2020. We, therefore, suggest that the presented costs over time for herpetofauna invasions have been heavily influenced by the evolution of the costs of the three costliest species (B. irregularis, L. catesbeianus and R. marina) and the timing of underlying studies. Future directions. This study presents the first global synthesis of the costs of invasive herpetofauna based on the current state-of-the-art in the InvaCost database. While costs are high, they are likely severely underestimated due to knowledge gaps, with costs focused on only a few taxa, regions and limited to recent decades  32. Furthermore, because invasion rates are expected to increase in the future  69  , we expect this increase to be followed by a rise in economic costs.