dataesr/scientific-paragraphs-categorization huggingface dataset:
For this purpose, we use the Trust Region Reflective algorithm method, implemented in the scipy. optimize. curve_fit function, and used in the model fitting wrapper of LMFIT (Non-Linear Least-Squares Minimization And Curve Fitting for Python) python library  (Newville et al. , 2016). This optimization procedure is sensitive to the duration of the time period over which we apply it. In the next section, we discuss the assimilation of field data to our model over short periods of time, corresponding to a single flood event. The case of longer time periods, which encompass several floods events, will be discussed in section 4. 3.
Acknowledgements. All the data used in this paper were collected by the  Observatoire de l'Eau et de l'éRosion aux Antilles (ObsERA, INSU-CNRS , http://webobsera. ipgp. fr/)and the  Draix-Bléone Critical Zone Observatory  (https://bdoh. irstea. fr/DRAIX/),two observatories of the OZCAR research infrastructure. We thank  T. Kitou ,  V. Robert ,  J. Ajax  and  B. Lamaille  for their technical support. We are also grateful to  C. Le Bouteiller  for sharing her knowledge and expertise on the Laval catchment. A. L. acknowledge the support from  LabEx UnivEarthS  ( ANR-10-LABX-0023  and  ANR-18-IDEX-0001 ). 18 https://doi. org/10. 5194/esurf-2022-50Preprint. Discussion started: 22 September 2022 c Author(s)  2022. CC BY 4. 0 License.
1 Study Subjects And Design/experimental Section The rst experiment was carried out with spectra acquired on 4 different machines, without particular control of the protein extraction conditions (Section 2. 1. 1). Then, we implemented a second experiment with standardized parameters (Section 2. 1. 2). This study was carried out in accordance with the Declaration of Helsinki. The current study was not considered as a study involving the human person according to the French law No. 2012 - 300 as no clinical or identifying data were used. All the strains were stored anonymously in the Pitié Salpêtrière Hospital Mycology Laboratory.
Data sampling by cross-validation As described above, spectra for the 96 isolates were acquired with four MALDI-TOF mass spectrometers. We validated our classi cation system (described later, Section 2. 3) using a nested cross-validation (CV) technique strati ed on clone/non-clone classi cation. The isolates were divided into 5 equally sized sets, using a random selection preserving the clone/non-clone distribution. Each CV fold is made of: a training set composed of the data from 80% of the isolates, and from 3 of the 4 spectrometers. a test set composed of the remaining 20% of isolates from the remaining one spectrometer. In total, 5x4 = 20 folds are performed with strict separation between the training and the test set, both in terms of isolates and spectrometers. Note that each isolate is associated with several spectra.
5-Optimization ). Data analysis has been carried out on 764 and 768 spectra where isolates were grown on CHROMagar ™ and Sabouraud-GC agar respectively. We evaluated our classi cation system with different combination of data sources: training (including validation) and testing with data from the same spectrometer training (including validation) with data from one spectrometer, testing with data from the other spectrometer mixing all data in both training and test sets We applied the same cross validation technique as described above, stratifying against isolates, i. e. making sure that spectra from the same isolate cannot be present on both train and test sets of each fold. We repeated the entire process for both CHROMagar ™ and Sabouraud-GC media.
2 Maldi-tof Mass Spectrometry Data Analysis The rst step of this study was to load the mass spectra into ClinProTools 3. 0 software to produce peak statistics views. The purpose was to test whether an existing statistical approach made it possible to identify reliable differences of peaks or intensity between clones and non-clone groups. We loaded spectra preparations on ClinProTools with the default settings (Bruker Daltonik GmbH, 2011), including spectra smoothing, baseline subtraction and recalibration  [17]. Spectra analysis was performed with the Peak Statistic tool using the t-test/ANOVA sort mode to generate a list of peaks with differences in intensity. We analyzed the 20 most discriminant peaks, common to each group (p value < 0. 05). Results show that there were no signi cant peaks that allowed us to differentiate the two categories.
Preprocessing & Alignment MALDI-TOF raw data were preprocessed with smoothing using Fourier transformation, Asymmetric Least Squares method  [18]  for the baseline correction and Peak picking with the detection of the sign changes in the spectra derivative  [19]. Preprocessing steps has been illustrated in Fig. 6. An alignment technique was used after the preprocessing step, with MSIWarp, a Python package provided with C + + implementation. MSIWarp is a exible tool compatible with multiple instrument types to perform mass alignment of Mass Spectrometry Imaging (MSI) spectra  [20]. The alignment approach works on TOF data and reduces the mass range shift by applying recalibration function on mass m/z data and by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra.
Another aspect to consider is the mass range shift produced by the different machines that will require further data alignment [26]  [14] ,  [20] ,  [30] ,  [31]. In two side experiments, we used our machine learning algorithm to identify 1/ the spectrum's mass spectrometers and 2/ the agar medium in which the isolates had been cultivated, instead of the R2 clones vs other classes. In both cases, results of this classi cation were close to 100% accuracy (Data not shown). A possible interpretation of this result is that a "machine effect" and a "culture medium effect" introduce a bias in the dataset, due to the mass spectra diversity. This could be one of the reasons for the di culty to differentiate the spectra of the R2 clones from the rest.
Comparison with other work coupling MALDI-TOF and machine learning In most of the microbiology papers combining machine learning and MALDI-TOF, the preparation of the spectra is one of the highlights of the methodology. Delavy et al. [14] , Yan et al. [32]  and Mortier et al. [33]  have processed the raw data with R software allowing them to use the MALDIquant package offering several processing techniques. Apart from the main processing like smoothing, baseline subtraction and soft peak selection technique, most of the methods push the processing of the spectra to recalibration, peak extraction and normalization. We limited ourselves to the rst 3 steps (smoothing, baseline correction and peak picking) in order to keep the processing light and to keep as much information as possible on the spectrum.
Moreover, in most previous work, quality control algorithms have been used to improve the predictions. In our case, the sorting is limited to the removal of the empty spectra or presenting no signi cant peak during the acquisition on the MALDI-TOF. A simple and fast pre-processing was chosen so that all the processing of the spectra is done by the model. Given the di culty of the problem, we assume that the information relating to the clonal character is found in the peaks of low intensity and small variations in intensity, which justi es avoiding the loss of information by drastic preprocessing. The representation of the spectra at the input of the model is also a crucial point of the methodology. The identi cation is highly dependent on this step. Articles whose method requires the use of the MALDIquant package tend to perform a peak extraction step in the pre-processing  [14] ,  [32] , for feature extraction because traditional machine learning models are the most used  [33].
Strengths and limitations In this study, we have explored the different types of possible variations that are caused by mass spectrometers, culture media, dilution and growth kinetics using convolutional neural networks (CNN). The rst two (spectrometers, culture media) are the main elements to consider. Thus, we managed to distinguish similar spectra. Spectral analysis allowed us to notice that the clone/non-clone differences were less pronounced than the differences in machines, media, growth kinetics, etc. This study is more complex than previous work because of the similarity between clone and non-clone spectra (see Fig. 1  above), which was not the case in the work on Aspergillus Flavus for example  [9]. Nevertheless, our experiments show that adding diversity in the dataset leads allows for an improvement of the classi cation reliability even when all parameters cannot be strictly controlled.
The parameters listed in the article are not the only parameters of variation. Nevertheless, we decided to highlight these ones because they proved to have a strong impact on the identi cation of R2 clones. Note that even if our classi er distinguishes R2 clones from other isolates in general, our experiments do not investigate whether the system would be able to distinguish these clones from one another or from other resistant clones. Furthermore, generalization to the question of resistance itself is left to future work. The results obtained in the multi-site setting are encouraging despite the limited amount of available isolates to build-up an optimal dataset of spectra for training. Since we are in the situation of monitoring an ongoing outbreak with only one to three new cases a month, this lack of data is inherent to the task.
Although infested nuts have been intercepted on several occasions across the world, so far A. transitella has not established anywhere outside the Americas, which is consistent with the panel's conclusion on a low probability of establishment. Thus, entry does occur, but establishment of populations is expected to be rare and so far has not been demonstrated outside of the Americas. Recognising that the 90% certainty range for the time interval between new introductions is from 27. 5 to 79,400 years, there is significant uncertainty regarding the likelihood of introduction, mainly due to uncertainties on the processes of transfer of the insect to hosts and the establishment of new founder populations by successfully transferring insects. To identify where in the EU climate is suitable for establishment, a literature search was conducted to assemble a database with all known location records of A.
Data and methodologies Four steps were distinguished; the assessment of (  1 ) entry including transfer, leading to the initiation of a founder population, (2) establishment, (3) spread, (4) impact. Assessment was based on a combination of literature review, meta-analysis, interviews with hearing experts and expert knowledge elicitation with experts or panel members and EFSA staff to assess quantities that could not be well identified from literature or databases alone  (EFSA, 2014). To link pest entry with establishment potential, the distribution of infested plant material entering the EU was assessed using NUTS 2 spatial resolution. Phytosanitary measures that may be considered to reduce likelihood of pest entry were evaluated by comparing scenarios with and without additional measures in place.
(  2019 ) estimating the number of potential founder populations that would arise in the EU, taking into account biotic and abiotic factors. The literature on the biology and control of A. transitella was synthesised to summarise the insect's biology and life cycle and to assess the principal methods of control used in the field and the subsequent prevalence of infestation of commodities under field conditions in California. California is the main area of production of walnuts and almonds destined for export to EU. Walnuts and almonds were identified as the most important pathways for introduction of A. transitella. Pest information, on hosts and distribution, was retrieved from the EPPO Global Database (EPPO, 2021, accessed on 1 September 2021) and information gathered during the pest categorisation on A.
transitella (EFSA Plant Health Panel, 2021) was further updated with literature and reports, including both peerreviewed and grey literature sources. The data on hosts were supplemented with additional hosts retrieved from the database of the World's Lepidopteran Hostplants curated by the Natural History Museum (NHM, 2022), UK and freely available online: https://www. nhm. ac. uk/our-science/data/ hostplants/search/index. dsml. Data on EU imports of key hosts from third countries were obtained from the Eurostat Easy Comext platform: inshell walnuts (CN code 08023100), shelled walnuts (CN code 08023200), inshell almonds (CN code 080211), shelled almonds (CN code 080212). Import data including information on the type of the transport (by air and by sea) was extracted from Eurostat in March 2022  (years 2000-2021).
Relevant publications were searched in Web of Science (topic search and all databases) and Google Scholar (generic search with no limitations). An initial search with "Lepidoptera" AND "phosphine" resulted in a list of 162 publications. After screening, 20 papers were selected for further analysis, 14 of which were used for data extraction. Papers were eliminated at the initial stage because (i) the focus of the experiment was not Lepidoptera; (ii) the paper was not a primary study, but a review; (iii) studies were older than 1990; (iv) studies were done with methyl bromide, while phosphine was mentioned without providing data; (v) duplicated data were presented that were present in other, more comprehensive, sources. Similar analyses were done for sulfuryl fluoride. An initial search with "Lepidoptera" AND "sulfuryl fluoride" resulted in 477 documents.
, 2015). CLIMEX uses ecophysiological requirements of an organism to assess its potential to fulfil the life cycle across a geographic region, given historic climate data. CLIMEX assesses the influence of weather-related stress factors (cold, heat, drought, humidity) on survival. The two factors (growth potential and survival) are integrated to provide an integrated index (the EI: ecoclimatic index) for establishment potential, given climate. CLIMEX is calibrated using presence records. Details on the two approaches are available on the Zenodo platform  (Campese et al. , 2022). An extensive literature search for A. transitella global distribution and ecophysiological parameters influencing distribution was conducted by  Campese et al. (2022). The literature search was conducted on Web of Science (all databases, excluding Data Citation Index and Zoological Record) and Scopus on 2 August 2021.
All references were exported to the reference manager software EndNoteX9 (The EndNote Team, 2013) and checked for duplicates. References were then imported (as compressed Endnote library) in the software DistillerSR (Evidence Partners, 2021) and screened. Title and abstract of 771 documents were then assessed as a first-level screening. Out of these, 191 documents were selected for full-text screening (second level). Documents were considered eligible when providing information on the ecology, physiology and distribution of the pest. A total of 97 papers were used for the data extraction out of which 69 included information on A. transitella distribution and 51 information on its ecophysiology (Figure  2 ). All points of documented establishment of A. transitella were overlaid with climate maps to identify climate conditions allowing establishment.
Furthermore, data on ecological requirements of the insect were retrieved from the literature to develop a CLIMEX model to refine the potential area of establishment in the EU. Distribution of the pest was first based on information in the EPPO Global Database (accessed on 2 August 2021) and the CABI Crop Protection Compendium (accessed on same date). Those findings were supplemented with the literature search described above  (Campese et al. , 2022). The search in scientific databases was completed on 15 September 2021 (Table  3 ). Moher et al. , 2009)  describing the process followed to identify, screen and include eligible information on the ecology, physiology and distribution of the pest Amyelois transitella 2. 3.
Plants for planting The pest categorisation identified plants for planting as a potential pathway for A. transitella (EFSA PLH Panel, 2021) based on publications from Australia  (AQIS, 1999; NSW, 2012). The HS/CN customs code system for classifying commodities traded internationally does not sufficiently discriminate plants for planting to provide import or export data on individual plant species. To overcome this problem, enquiries were made to the Netherlands, the major EU member state that imports plants for planting. Specifically, the EFSA PLH Panel sought data about imports of the following hosts of A. transitella: Juglans regia (walnut), Prunus armeniaca (apricot), P. dulcis (almond), P. domestica (common plum), Pyrus communis (European pear), Malus domestica (syn. pumila) (apple) and Cydonia oblonga (quince) from the USA.
TRACES began recording interceptions in May 2020 and is the system in place today. Both databases were consulted during the pest categorisation (EFSA Plant Health Panel, 2021) and no records of interceptions of A. transitella were found in either database. However, nuts of almonds, walnuts and pistachios only became regulated in December 2019 since when they have required a phytosanitary certificate if they are to be introduced into the EU (Implementing Regulation 2019/2072, Annex XI, part B). 1% of consignments are required to be inspected. Although no interceptions of A. transitella are recorded in Europhyt or TRACES -findings of A. transitella would not necessarily be notified even if they were made because A. transitella is not an EU quarantine pest, so there has been no legal obligation for NPPOs to notify interceptions in either Europhyt or TRACES.
3 ). An index equal to 0 indicates no stress, while 100 indicates conditions that are detrimental to the organism and do not allow establishment. The CSI is based on two parameters, the cold stress temperature threshold (TTCS) and the cold stress temperature rate (THCS). The TTCS defines a temperature below which cold stress begins to accumulate. The rate at which this stress accumulates is determined by the THCS. The stress is accumulated weekly based on the average weekly minimum temperature  (Kriticos et al. , 2015). In CLIMEX, weekly climate data are derived from monthly data through interpolation  (Kriticos et al. , 2015). Based on the literature on A. transitella  (Johnson, 2007; Tebbets et al. , 1978) , TTCS was set to 0°C  (Campese et al. , 2022 ) (Appendix G). Figures  8  and 9  can be interpreted as the areas where climate is most suitable for establishment of A.
new crop almonds) on the size of the predicted area suitable for the development of transient generations. ). The legend shows the number of generations (the darker the colour, the higher the potential number of generations). The observed distribution in California is represented using white plusses in panels A1 and B1. Panels A1 and A2 use a heat-sum of 615 degree days for completion of one generation of the insect while in panels B1 and B2 use a temperature sum of 417. 5 degree days. These temperature sums are averages of the values reported by  Seaman and Barnes (1984)  and  Sanderson et al. (1989b). Weather data are from the Copernicus Climate Change Service information. Degree-day calculation was based on hourly temperature data from the ERA5-Land data set (Mu ñoz Sabater, 2019). For each pixel of the ERA5-Land 0.
Weight of individual shelled walnuts Individual walnut weight without shell (kg) Based on the Turkish data in the walnut dossier (Appendix A) and keeping in mind the estimates for the walnuts inshell, the panel estimated a plausible range from 5 to 10 g per nut, with 7. 5 as midpoint. Due to limited information there is fairly high uncertainty, which is expressed in a broad range (5-10 g). Elicited mean walnut weight (kg) without shell is given in the table below together with the probability distribution. Median (P50%) = 0. 0075 g The 90% certainty interval (P5% to P95%) is between 0. 0056 kg and 0. 0094 kg.
The eight parameters that were elicited are discussed per pathway. The pathways are: • Almonds inshell • Shelled almonds Step 1. Trade volume The first parameter is trade volume. Data on trade volume were retrieved from Eurostat and are expressed in 100 kg. The panel focused on the trade by sea as the flow by air is a very small fraction of the total. The average percentage of nuts shipped by air from the USA to the EU was 0. 07% for almonds inshell, and 0. 10% for shelled almonds, on average from 2000 till 2021 (Eurostat data).
Trade volume for almonds, inshell The panel evaluated the history and trends including or excluding the first few years. There is a weak tendency to increasing trade, but the increase is not strong, particularly not if the first 2 years are excluded from regression. Two years (2014 and 2017) are outliers. Data series could also be interpreted as stationary. The panel converged on 18-32 * 100,000 kg nuts per year as a plausible interval for average yearly flow in the next 5 years. The mean is somewhere between 18 and 32, but it is difficult to decide where, so we come up with a rather flat (almost uniform) distribution.
Weight of individual almonds inshell The panel estimated the almond weight with shell on the basis of the elicited values of the almond weight without shell after drying (see below). The panel converted the quantiles elicited for nuts without shell (see below) to quantiles for nuts with shell using the regression equation y ¼ 3:48xÀ0:48 where x is the kernel weight (g) and y is the total nut weight (kernel + shell; g). This regression equation was fitted on data presented in a study by  Godini (1984)  on almond kernel weights and total nut weights for a broad set of varieties grown in  Bari (Italy). Relationship between kernel weight and total weight of walnuts including shell, based on data from  Godini (1984). The orange point is for the variety Nonpareil which is the most widely grown variety in the USA.
3 g per nut. Smaller unshelled almonds weigh around 1 g, and larger almonds can weigh up to 1. 5 g. ' California marketing board provides extensive information on almond weights (https://www. nass. usda. gov/Statistics_by_State/California/Publications/Specialty_and_Other_Releases/Almond/Objective-Measurement/201606almom. pdf). These data are summarised in the table below. Results from Godoni (1983) in Bari suggest higher weight than those reported in above sources, but these weights appeared fundamentally different from those in the USA and were therefore disregarded. Nonpareil is somewhat heavier than other varieties but represents only 40% of total production so the panel combined data on Nonpareil and other varieties. Synthesising and discussing the evidence, and giving most weight to data from California, the panel estimated the following distribution for almond nut weight without shell.
5°C. They reported their data as LT50 and LT95, i. e. the time to 50 and 95% mortality. Data were analysed using probit analysis. From the reported data, the panel reconstructed the mortality-time relationships and plotted these relationship along with the reported LT50, LT95 and extrapolated time till probit-9 mortality, i. e. 32 survivors out of a million exposed individuals. For shelled nuts (almonds, walnuts) we assumed that only larvae or pupae would be present. For unshelled nuts (almonds, walnuts) we assumed that also eggs would be present. We assumed equal numbers of eggs and larvae. 2022-06-13 The data from  Johnson (2007)  indicate that survival will be well below the probit-9 level of survival if the temperature is 0°C. For eggs, time to probit-9 is 2. 2, 5. 5 or 4. 2 days at 0°C.