dataesr/scientific-paragraphs-categorization huggingface dataset:
The downgrading in regulatory status is also likely to result in further outbreaks across Europe, despite concerted efforts by national teams. This study drew together a range of management options which have been used globally to tackle BSNB, drawing especially on the long history of BSNB management in the USA. Some North American methods are very specific to host species not likely to be planted in Europe. Other, more generic methods will vary in acceptability across Europe, but an immediate priority must be to test them under European conditions. Practitioners should also be encouraged to record their actions, successful and otherwise, in the geo-database to enable researchers, surveillance teams and forest managers to benefit from others' experiences. Without a coherent strategy for surveillance or management there is a danger that the pathogen will spread unchecked across vast interconnected pine forests.
Acknowledgments This study was supported by the  Euphresco  project  BROWNSPO-TRISK ,  Forest Research, Britain (K. Tubby) ,  Estonian Science Foundation  grant  PRG1615 ,  Polish Ministry of Education and Science, statutory research mechanism (P. Boroń) , the framework of the  State Task of the Federal State Budgetary Institution "Federal Research Center "Subtropical Scientific Center of the Russian Academy of Sciences ", the research theme N •  FGRW-2022-0006 (T. Bulgakov ), the  Ministry of Agriculture , Regions and Tourism, Stubenring 1, 1010  Vienna, Austria (T. Cech) , the  Swedish Research Council Formas  Grant  021-01119  (M. Cleary), the  Ministry of Rural Affairs of Estonia (R. Drenkhan) , the  National Science Program 'Environmental Protection and and Reduction of Risks of Adverse Events and Natural Disasters ', supported by the  Ministry of Education and Science of Bulgaria , Agreement  D01-279/ 03.
jani, but no L. acicola P. mugo Turra Geodatabase (this paper) Switzerland, Native Natural regeneration in bogs P. nigra J. F. Arnold  ‡ Sadiković et al. , 2019a  ‡ ‡ Austria Slovenia Native Forests Adamson et al. 2015 Estonia Exotic Botanic garden P. nigra J. F. Arnold var. nigra Hintsteiner et al. , 2012 Austria Native Mature trees in gardens P. oocarpa Schiede ex Schlectendahl Evans, 1984 Costa Rica, Native Natural forests Although diagnosed as L. acicola, subsequent phylogenetic analyses suggest L. acicola not present in Central America van der Nest et al. , 2019a, Guatemala, Native Phylogenetic analysis detected L. variabilis, b Honduras, L. pharomachri, L. jani, and L. guatemalensis but not P. palustris Miller  ‡ Webster, 1930Skilling Nicaragua USA Native L. acicola Plantations and field inoculations in Christmas tree and Nicholls, 1974 plantations (continued on next page) K.
ONT reads generated via MinION with R9. 4. 1 flowcells were basecalled using three alternative models [Fast, High-accuracy (HAC) and Super-accuracy (SUP), available within ONT's Guppy software], assembled with Flye and polished using Medaka. Accuracy of typing using ONT-only assemblies was compared with that of Illumina-only and hybrid ONT+Illumina assemblies, constructed from the same isolates as reference standards. The most resource-intensive ONT-assembly approach (SUP basecalling, with or without Medaka polishing) performed best, yielding reliable capsule (K) type calls for all strains (100 % exact or best matching locus), reliable multi-locus sequence type (MLST) assignment (98. 3 % exact match or single-locus variants), and good detection of acquired AMR genes and mutations (88-100 % correct identification across the various drug classes).
SUP is the most accurate and slowest; the speed of the basecaller being a factor of the number of parameters in the neural network model. HAC is intermediate between Fast and SUP and about six times slower than the Fast model (https://github. com/rrwick/August-2019-consensusaccuracy-update#basecalling). ONT polishing, utilising Medaka-a neural network-based tool that generates consensus sequences by aligning the individual sequence reads against a draft assembly [31]-is rapid, highly effective and believed to improve sequence accuracy significantly  [32]. ONT continuously refines these algorithms to improve raw reads and consensus accuracy  [33]. However, despite continuous improvement over the years, ONT is still susceptible to non-trivial numbers of base-call errors, which can impact the identification of clinically important features such as acquired virulence and AMR genes  [34, 35].
Bacterial isolates and sequencing We utilised 54 K. pneumoniae that were previously isolated at the Alfred Health Clinical Microbiological Diagnostic Laboratory in Melbourne, Australia, as part of a year-long prospective study of isolates from clinical infections (hospital-wide) and screening swabs (in intensive care and geriatric wards) in the Alfred Health network  [43, [48] [49] [50]. The accession numbers for all genome data analysed in this study are provided in Table  S1. Isolates were sequenced first via Illumina HiSeq 2500, generating 125 bp paired-end reads, as described previously  [48]. Later, selected isolates were subcultured, and fresh DNA was extracted for long-read sequencing using GenFind (Beckman Coulter) kits (full protocol available under DOI: 10. 17504/ protocols. io. p5mdq46).
Basecalling and assembly Fast5 files generated by the ONT MinION were initially basecalled with Guppy v4. 0. 14 [51] on the Monash University M3 MASSIVE GPU cluster using the 'Fast' (r9. 4. 1_450bps_fast) and 'High-accuracy' , aka HAC (r9. 4. 1_450bps_hac) models for each sample. The 'Super-accuracy' (r9. 4. 1_450bps_sup) model was later used when it was released in Guppy v5. 0. 7  [51, 52]. The basecalled FASTQ files were then concatenated into a single file per basecalled sequence run and demultiplexed into individual FASTQ files (one per sample) with the qcat command-line tool v1. 1. 0 [53] based on the barcode sequences. As a quality control step before assembly, we first filtered out poor-quality reads using fastp v0. 20. 1  [54]  (short reads) and Filtlong v0. 2. 1  [55]  (long reads) at a sequence similarity threshold of 90-95 %, retaining only the reads with a minimum length of 1 kb and excluding the worst 5-10 % of the reads (for FASTQ files less than 200 Mb, a keep_percent of 95 % was used, while a keep_percent of 90 % was applied to read files of size greater than 200 Mb).
Using R to fit a linear regression model indicated that an ONT-only Pathogenwatch distance of 50 SNPs would approximate the Illumina-based Pathogenwatch distance of 10 SNPs or genome-wide distance of 25 SNPs (see Fig. 2 ). We compared the topologies of neighbour-joining trees generated from Pathogenwatch distance matrices calculated using SUP+Medaka, HAC+Medaka or Fast+Medaka assemblies against the reference tree (calculated from hybrid SUP+Medaka+pilon assemblies), using the tanglegram function in the R package dendextend v1. 15. 2 to generate comparative tree plots and calculate entanglement coefficients. We also used the phytools package v1. 0-3 in R to compute the Robinson-Foulds distance  [71, 72]  between tree topologies, which represents a sum of the number of partitions inferred by the first tree but not the second tree and that inferred by the second tree but not the first tree.
AMR determinants Kleborate screens for acquired AMR genes by interrogating each input assembly against a species-aware modified version of the Comprehensive Antibiotic Resistance Database  [47]. A total of 270 acquired genes were identified across the 54 reference genome assemblies (median nine per genome, range 0-18). These acquired genes were mostly identified correctly from SUP+Medaka (95. 8 %), SUP (84. 8 %), HAC+Medaka (82. 7 %) and HAC (60. 3 %) assemblies, with lower recovery rates from Fast+Medaka (53. 2 %) and Fast (25. 7 %) assemblies. Kleborate also screens for mutations in chromosomal core genes known to be associated with AMR  [47]. Here, reportable mutations were identified from reference assemblies in the gyrA and parC genes (substitutions associated with fluoroquinolone resistance, n=9 isolates), in blaSHV (substitutions associated with extended-spectrum β-lactamase activity, n=27 genomes), and in ompK35 and ompK36 (truncations associated with reduced susceptibility to β-lactams, n=2 genomes).
Our study considered K. pneumoniae only. However, the basecalling, assembly and genotyping tasks explored here are commonly applied across other bacterial pathogens. In particular, since Enterobacterales share common methylation patterns (which is a major determinant of basecalling accuracy) and AMR mechanisms, the results presented here can be considered informative as to the general accuracy of ONT-only analysis of the family more broadly. Notably, laboratory and informatics methods for ONT sequencing are under constant development-for example, new flowcells have recently been released with new (R10) pores; basecalling models can be trained on a larger or species-specific dataset, and assembly and polishing methods are under active development  [92]. These developments are expected to lead to improvements in  Reference assemblies are long-read-first ONT/Illumina hybrid assemblies generated using SUP-basecalled ONT data.
Acknowledgments This work was supported by  Generalitat Valenciana  (Project reference  AICO/2021/278 ) (JJQ), the  Spanish Ministry of Science and Innovation  (Project references  PID2019-110764RA-I00/AEI/10. 13039/501100011033  (JJQ) and  PID2020-119462RA-I00/AEI/10. 13039/501100011033 (AGM) ) and  Universidad CEU Cardenal Herrera Programa   INDI 20/40  (JJQ),  Institut Pasteur ,  Inserm , and  Santé Publique France (ML). J. J. Quereda  is supported by a  "Ramón y Cajal " contract of the  Spanish Ministry of Science, Innovation, and Universities  ( RYC-2018-024985-I ). C. Palacios-Gorba  and  Yuval Markovich  are supported by a Predoctoral contract from the  Universidad Cardenal Herrera-CEU. The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.
Call collection and stimuli preparation Mobbing calls used in the study were recorded from great tits responding to playback of mobbing calls of conspecifics  (Dutour et al. 2017) , given by individuals in response to predator mounts  (Kalb et al. 2019) , or were obtained from the Xeno-Canto online database (http:// www. xeno-canto. org/) with search criteria specifying the type of vocalization as "alarm call". In Xeno-Canto, we reduced our selection to sound files that we could identify as associated with a mobbing event (most of these mobbing calls have already been used in previous mobbing studies; e. g. Dutour et al. 2021; Salis et al. 2021) , that were quality "A" (i. e. highest recording quality), and that had no other bird species audible in the recording. We used mobbing calls of great tits that were composed of combinations of frequency-modulated elements followed by a string of a repeated loud broadband elements (FME-D call;  Dutour et al.
2019). From these recording files, 40 unique soundtracks of mobbing calls were built using Avisoft-SASLab software (i. e. with one calling individual per soundtrack). Chaffinch songs were obtained from the Xeno-Canto online database with search criteria specifying the type of vocalization as "song", quality "A", and without other bird species in the recording. From these recording files, 32 unique soundtracks of chaffinch songs were compiled (one calling individual per soundtrack). The song of chaffinch consists of two or three trill-like phrases, followed by an end phrase of more complex and dissimilar notes  (Nottebohm 1968 ). On each soundtrack, low frequency noise had been systematically removed (below 1 kHz, Kalb and Randler 2019) and natural calling rates were used for each species: great tit mobbing calls were repeated at a rate of 26 calls/min  (Dutour et al.
Background State-space models, such as Hidden Markov Models (HMMs), are increasingly used to classify animal tracks into behavioural states. Typically, step length and turning angles of successive locations are used to infer where and when an animal is resting, foraging, or travelling. However, the accuracy of behavioural classifications is seldom validated, which may badly contaminate posterior analyses. In general, models appear to efficiently infer behaviour in species with discrete foraging and travelling areas, but classification is challenging for species foraging opportunistically across homogenous environments, such as tropical seas. Here, we use a subset of GPS loggers deployed simultaneously with wet-dry data from geolocators, activity measurements from accelerometers, and dive events from Time Depth Recorders (TDR), to improve the classification of HMMs of a large GPS tracking dataset (478 deployments) of red-billed tropicbirds (Phaethon aethereus), a poorly studied pantropical seabird.
Results We demonstrate that even with a small informed sub-dataset (representing only 9% of the full dataset), we can significantly improve the overall behavioural classification of these models, increasing model accuracy from 0. 77 ± 0. 01 to 0. 85 ± 0. 01 (mean ± sd). Despite overall improvements, the sensitivity and precision of foraging behaviour remained low (reaching 0. 37 ± 0. 06, and 0. 06 ± 0. 01, respectively). Conclusions This study demonstrates that the use of a small subset of auxiliary data with known behaviours can both validate and notably improve behavioural classifications of state space models of opportunistic foragers. However, the improvement is state-dependant and caution should be taken when interpreting inferences of foraging behaviour from GPS data in species foraging on the go across homogenous environments.
However, the predictions of these models, both pre-and post-adjustments, are usually evaluated visually, and without cross-validation with other datasets making it difficult to measure the benefits of these changes (S1). Model performance can be improved by incorporating additional information on what an animal is doing from auxiliary sensors. For example, wet-dry sensors (WD) can distinguish when an animal is immersed in salt water  [3, 23] , Time Depth Recorders (TDR) can be used to detect dives below a specific threshold  [31]  and high frequency tri-axial accelerometers can provide unprecedented information on fine-scale movements resulting in inferences that go as far as separating individual preycapture attempts  [17, [32] [33] [34]. Data acquired from these sensors can be incorporated into behavioural models, allowing for more accurate classification.
Although several modelling techniques can be used to incorporate these data, HMMs have drawn particular attention due to their relatively high accuracy  [22, 34] , their robustness at lower GPS resolution  [16, 20, 22] , and the development of the flexible user-friendly R packages that can incorporate information from additional data streams, even when collected at different time resolutions (e. g. 'moveHMM' and 'momentuHMM';  [33, 35, 36] ). Nonetheless, the use of auxiliary sensors is often limited by their cost, size, and weight, and so they often only comprise a small fraction of a full GPS tracking dataset, and cannot easily be incorporated as additional datastreams  [33]. For this reason, many studies limit their use to validate behaviours identified from GPS positions, instead of directly using these data to improve the model classifications themselves (e.
g. [23, 37] ). When a small auxiliary sensor dataset is present, one potential solution is manually setting associated positions to a given inferred behaviour, and then use these positions to semi-supervise the model behavioural classification of the rest of the data-set, with an aim to improve the models' overall accuracy. In this study, we aim to assess whether the addition of information from auxiliary sensors can improve behavioural inference in animals mainly performing looping trips through relatively homogeneous environments, such as seabirds foraging in tropical waters. We use a large GPS tracking dataset of a tropical seabird species, the red-billed tropicbird (Phaethon aethereus), of which a subset was double tagged with a combination of accelerometers, wet-dry sensors, and/or TDR sensors.
Axytrack loggers weighed 17 g (2. 6% of tropicbird weight) and recorded GPS, acceleration and pressure data at 5-minute, 25 Hz and 1s intervals, respectively. The Migrate Technology C330 geolocators (GLS) with a wet-dry sensor weighed 3. 3 g (0. 5% of tropicbird weight) and register if the bird was wet or dry every 6 s. GPS and Axytrek's were attached to the 6 central tail feathers with Tesa tape while GLS were attached to the tarsus, on the bird's metal ring with the help of a zip tie.
Data processing To test whether adding data from auxiliary sensors improved the accuracy of HMM behavioural inferences, we first processed the wet-dry, accelerometry and TDR data separately before summarizing and matching the information to each GPS position (interpolated to 5-minute intervals). We matched the data forwards (e. g. the value of the wet-dry, accelerometry, and TDR metrics at a GPS position at time t summarized the values of the period between t and t + 1) to be consistent with the calculation of the step and turning angle by the prep-Data function of the 'momentuHMM' package  [36]. From wet-dry loggers, we extracted the proportion of time wet between each GPS position. From the accelerometry data, we extracted the proportion of time resting on water, diving, and flapping between each GPS position.
Creation of informed dataset To create an informed dataset of inferred bird behaviour to both semi-supervise and validate the HMM, we combined the information from the wet-dry, accelerometer, and TDR data based on the following conditions to assign positions as foraging, resting, or travelling. These positions are referred to as having a known state. 1. Foraging: diving was identified one or more times in the accelerometer or TDR data stream. 2. Resting: the wet-dry sensor recorded a period as 100% wet, or the accelerometers recorded a period as over 50% on water. No dives were detected in either the accelerometer or TDR data stream. 3. Travelling: the wet-dry sensor recorded a period as 0% wet or the accelerometers recorded a period as 100% flapping. No dives were detected in either the accelerometer or TDR data stream.
HMMs We ran two series of HMMs to determine if an increasing percentage of supervision can improve the accuracy of behavioural classifications. The first used only GPS tracks with auxiliary data (151 foraging trips) to determine whether accuracy at high porportions of supervision saturates, while the second used the complete GPS dataset (1084 foraging trips) within which only a small percentage (13. 9%) of trips contained auxiliary data to test whether even small auxiliary datasets can improve model accuracy. All HMMs were implemented in the R package 'momentuHMM'  [36]. Although GPS loggers were programmed to record positions every 5 minutes, poor satellite reception resulted in gaps in the data (of 6-20 minutes between 1. 5% of positions, and over 20 minutes between 0. 4% of positions). Therefore, to satisfy model assumptions, GPS data were linearly interpolated to a regularised five-minute sampling frequency to have an equal time period between each position when the gaps were less than 20 minutes long.
We chose a three-state HMM as a trade-off between model accuracy, interpretability of states, and biological knowledge of the species  [39]. States were delineated by the HMM using step lengths and turning angles between positions, and then classified as resting (short step lengths and low turning angles), foraging (mid step lengths and high turning angles), and travelling (longs step lengths and low turning angles). To select appropriate starting values for the models, a k-means clustering algorithm (with k = 3 for the number of states) was used for the state-dependent probability distribution parameters of each data stream  [23]. We used a gamma distribution to describe step lengths, and a von Mises distribution with a mean of zero for turning angles. To reduce the risk of models converging at a local rather than global maxima for the maximum likelihood, we reran each model 10 times using a randomization starting values, before selecting the model with the highest maximum likelihood and lowest Akaike Information Criterion (AIC)  [36].
Model validation To measure how the use of informed data increases model accuracy, we used an iterative approach similar to a k-folds analysis, in which we left out 10 random samples of 10% of the known states to be used as testing datasets, while the remaining 90% of known states were used as training datasets. For the first series of HMMs using only the GPS tracks with auxiliary data, we created models with randomly selected subsets of the known states representing 0 to 75% of this dataset (75% representing the maximum number of known states available for our dataset after setting aside 10% as the testing dataset). For each increase of 5% percent of known states from 0 to 75%, we ran 10 models, using the 10 different random samples of test and training datasets to validate the models. For the second series of HMMs using the complete GPS dataset, we only tested the increase in accuracy between 0 and a maximum percentage of known states (9%) due to computational restrictions and therefore ran 10 models at each of these percentages using the 10 different random samples of test and training datasets.
We then decoded the states of each model using the Viterbi algorithm. For each model, we then generated the assigned state confusion matrix to assess overall assignment accuracy using the confusion Matrix function in the 'caret' R package  [40]. In addition to the overall accuracy we also extracted the class-wise sensitivity, specificity, and precision from the confusion matrices (Fig. 1 ). These metrics are complimentary and the importance of each will depend on the research questions at hand. Using foraging behaviour as an example, high sensitivity of foraging would indicate that most known foraging positions are correctly classified as foraging by the model. However, this does not exclude the possibility of many resting and travelling positions being also misclassified as foraging. To measure this, one uses specificity, or the proportion of resting and travelling positions correctly classified as non-foraging.
If there is an uneven number of known resting, foraging or travelling positions, even a small proportion of one behaviour misclassified as another can dilute the proportion of correct classifications. Here is when precision is needed to determine the proportion of positions classified as foraging that are actually foraging, and not resulting from a misclassification of resting or travelling positions. To compensate for a lack of standardized practices in evaluating and reporting the performance of behaviour classification models  [18] , we also calculated additional measures of model performance to make it possible to compare our results to as many previous studies as possible (S5). Finally, to explore if the exclusion of positions with low state classification probabilities improved overall HMM behavioural classification, we used the stateProbs function from the 'momentuHMM' package  [36]  to extract the state classification probability of each position.