dataesr/scientific-paragraphs-categorization huggingface dataset:
g. white-tailed deer in the USA  14  , brush-tail possum in New Zealand  15  and badgers in the UK  16. Proximity to badger sets has been linked to increased bTB risk in UK and badgers have been culled in an attempt to limit the impact of this transmission route. Machine learning (ML) modelling refers to statistical modelling techniques which detect patterns within data, as opposed to process-driven models which rely on understanding of system dynamics. ML algorithms make predictions by exploiting correlations between input variables (features) and an output variable (target), while being robust to inter-correlation within the set of input variables  17. Employing such techniques can potentially improve the accuracy of herd-level bTB diagnosis by including data which may influence bTB breakdown risk, e.
In the context of bTB control programme, however, risk factors would ideally be collated with the results of routine herd-level SICCT tests. This could inform interpretation of the herd-level SICCT test and improve diagnostic sensitivity. Records of results of individual animal SICCT tests were analysed by Adamskiy et al. 19. They used the ML methodology to improve the accuracy of the SICCT test. All the data were taken from the VetNet database whose features and purpose are described in the National Statistics report Background and methodology to the National Statistics on the Incidence of Tuberculosis (TB) in Cattle in Great Britain  20. VetNet contains only the records of the SICCT test results which has been positive. Post-mortem M. bovis culture or the gamma-interferon test follow each positive SICCT test in order to confirm it and their results are also recorded in the database.
In this way, the model can learn to identify false negatives. In this study, we used the new bTB surveillance database (SAM), which replaced the VetNet database since 2011 and includes positive and negative SICCT results, to develop a ML model which could accurately predict future herd-level bTB breakdowns in GB. To do so, we incorporated past and current SICCT test results and collated them with data directly and indirectly related to bTB infection risk. The model was evaluated as a means to increase the herd-level sensitivity (HSe) and specificity (HSp) of the bTB diagnostic testing by providing a more sophisticated interpretation of the herd-level SICCT test results.
Results In this study, the results of animal-level SICCT tests conducted in GB between 2012 and 2015 were aggregated and dichotomised at the herd-level, informing whether at least one animal had tested positive in a herd. In total, over 500,000 herd-level results were recorded during the study period. These records were then extended with risk factor data of the farms and used as input to ML models to predict future herd-level bTB breakdowns.
Summary statistics of the SICCT performance. To provide a measure of performance of the SICCT test, which will be used as a baseline to compare against a ML predictive model, we calculated the observed HSe and HSp of the herd-level SICCT test separately for data in every year from 2012 to 2015 according to the definition of future bTB breakdown in this study. The HSe and HSp of SICCT stratified by year are shown in Table  1. Machine learning predictive performance. The dataset used in this study consisted of collated data from different sources: SICCT test results of a herd, past bTB breakdowns, cattle movements, land cover and climate data. From these data we derived 139 variables characterising herds, including current SICCT test results. These variables were then combined into vectors, such that every herd at the time of a SICCT test was represented as a vector.
The vectors were fed into ML algorithms as input. The target output value of models was the 0 or 1 indication of a future bTB breakdown (where 1 corresponds to breakdown and 0 corresponds to no breakdown). The whole dataset was split into three disjoint subsets. A training set consisted of data from 2012 to 2013, totalling 9300 ground truth positives and 236,998 negatives. It was used to optimise the internal param-eters of ML methods in the training phase. A development set included data from 2014, where 4819 data points were positive and 123,949 were negative. This subset was used for initial evaluation to optimise the settings of ML algorithms and to select the best-performing model. A testing set of 2015 data consisted of 4690 ground truth positive herds and 126,255 negatives. A final evaluation of the best-performing model was conducted on the testing set and the performance of this model was compared to the observed HSe and HSp of the SICCT test calculated for the same data points.
It is impossible to know upfront which ML algorithm will provide the most accurate predictions for a specific problem. Therefore, we chose four commonly used ML methods-Support Vector Classifier (SVC), Neural Network (NNET), Random Forest (RF) and Gradient Boosted Trees (GBT)-and independently trained them on the training set. Their performance on the development set was measured and compared to select the best performing model based on the area under the receiver operating characteristic curve (AUC) of their predictions. SVC, NNET, RF and GBT achieved an AUC of 0. 875, 0. 899, 0. 904 and 0. 906, respectively. As a result, GBT was selected as the best-performing method and used for further analysis. The model obtained AUC of 0. 907 on the testing set of data from 2015 that was new to the model, which is consistent with its performance on the development set.
A portion of the curve shown in Fig. 1  reaches beyond the HSe and HSp of the herd-level SICCT test as calculated for the development and testing datasets. The decision threshold, used to dichotomize the model's output, was optimized to be in the middle of this portion for the development set. This resulted in the threshold value of 0. 695, i. e. all breakdown probability estimates above 0. 695 were classified as positives. The value of the threshold was transferable to the testing data and it led to improvement of both HSe and HSp. In order to obtain confidence intervals of HSe and HSp, and to test the statistical significance of the difference between the observed SICCT test results and GBT's predictions, HSe and HSp were independently calculated in 100 disjoint subsets of the testing set.
The next 20 variables were related to the past SICCT results and bTB breakdown history of farms, land cover data, animal births, animal movement and contact tracing. Importantly, no variable related to climate data was included in the 25 most important ones. Details on the list of input variables can be found in Supplementary Table  1. The ranks produced by the two methods were compared in order to check the consistency of the selection of the 25 best input variables. Figure  3B  presents high correlation between the rankings obtained by GBT and RF, especially in the high importance range. Such a high correlation implies that the same variables were consistently selected as the most important ones.
Both of these approaches lead to more   solution to improving herd-level diagnostics by utilising data which are routinely collected. In previous studies, known risk factors and data potentially related to bTB risk were used to inform models producing bTB risk maps  24  or predicting recurring bTB breakdowns  25. The results show that such data can be successfully used as input into the models predicting bTB risk. These models are, however, of limited use for the bTB control programme in GB. The risk maps lack granularity, i. e. they only identify areas of increased bTB risk instead of individual herds. It is costly to issue control measures area-wide, so herd-level predictions are preferred. The recurring breakdowns model predicts high risk herds, but it only applies to a small subset of all the herds.
The predictions of the model can aid issuing animal movement restrictions and inform targeting of undetected high-risk farms for follow-up. In the future, such a model can also serve as the backbone of a decision support tool helping veterinarians interpret the herd-level SICCT test results in the field. Here, we used an extensive dataset of 139 variables to characterise farms that were tested during bTB surveillance activities in GB and enable the development of our ML model. Some of these variables exhibit correlation between each other. For example, variables related to climate, such as temperature and downward radiation, are highly correlated with one another, while percentage of woodland is negatively correlated with human population density. However, an ideal set of input variables for developing a ML model contains variables which experience weak or no correlation between each other, but all are correlated to the target output.
In this sense, the dataset of 139 intercorrelated variables suffers from redundancy. To overcome such a limitation, we selected a subset of predictor variables yielding the same accuracy as using the full set of predictors. By doing so, we reduced our list of variables to 25 using the iterative variable elimination technique, while achieving similar level of predictive performance as the full model. This subset of variables included: farm location, between-farm animal movements, links to infected farms, breakdown history of farms, number of animals tested and land cover around the farm. Importantly, the model achieved the same level of predictive performance when all the variables related to climate data were removed. This is informative for the development of datasets in future studies on bTB breakdown prediction.
An alternative approach is to train a ML algorithm on low incidence regions only, which would result in a model specialised in these regions. The dataset from East England and Scotland, however, includes less than 1. 3% positives and it has insufficient size to train a ML algorithm. The spatial imbalance in the whole dataset was, therefore, inevitable and inferences from our model need to be made cautiously. All ML methods used in this study produce a continuous estimate of breakdown probability (from 0 to 1), so the balance between HSe and HSp can be adjusted by changing the value of threshold (Fig. 1 ). If higher HSe is required, it can be increased at the expense of HSp. Hence, the GBT model can predict breakdowns in East England and Scotland with HSe better than herd-level SICCT test, but it would then yield worse HSp.
According to the case definition, the ground truth positives in the dataset were derived from the results of inspections for lesions and M. bovis culture which has limited sensitivity. It is possible that some of the infected animals were identified as bTB-negative by the lesion inspections, which led to herds incorrectly labelled as ground truth negatives in the dataset. Due to the lack of a perfect bTB diagnostic test, these mislabelled data instances were inevitable, therefore limiting our ability to accurately and robustly validate our predictions of breakdowns. For this reason, together with the fact that the animal-level specificity of the SICCT test in GB is estimated as 99. 98%, it would be more conservative to treat reactors as bTB-positive irrespectively of the model's output when implementing the ML model in a real-world situation.
Methods Data sources and input variables. In GB, it is compulsory to record whether each animal-level SICCT test is positive or negative. The records are gathered by the Animal and Plant Health Agency (APHA) into the bTB surveillance database (SAM) where every holding is uniquely identifiable by a County Parish Holding (CPH) number. The same holding identifiers are used in the Cattle Tracing System (CTS) database  26  , which tracks between-farm cattle movements in GB. Based on the CPH number, the two databases were combined and variables related to animal movements and SICCT tests were derived to provide input into the ML algorithms. These variables were compiled into a dataset where each data instance represented a herd at the time of the routine SICCT testing. In general, every available characteristic of a farm was expressed as a numeric value, where real numbers corresponded to continuous values (e.
Between-farm cattle movement. The CTS database totals 160 million individual level movements between 2001 and 2016. This data was aggregated to provide the number of births, deaths and incoming and outgoing movements calculated within periods of 30, 60 and 90 days prior to a SICCT test. The sums were divided into direct movements and movements of animals which went through a market. Contact tracing was performed with the EpiContactTrace R package  27  to calculate the number of chains linking the farm in question with bTB-infected herds. Here, a herd is defined as bTB-infected from 90 days prior to its confirmed breakdown. The chains were divided into direct chains of length one and indirect chains which linked two herds via other herds. The input variables describing the farm in question extracted in this analysis were the numbers of movements from the bTB-infected herds to the farm (incoming chains) and movements from the farm to the infected herds (outgoing chains) accumulated over periods of 30, 60, 90, 180 days and 1, 2, 4 years prior to the SICCT test.
Furthermore, a variable specifying whether a farm was a dairy, beef or a mixed farm was included in the dataset. Location data. SAM and CTS use the CPH numbers of the holdings, therefore it is possible to infer geographical locations of the farms (northing and easting) and relate them to other types of data. The dataset was extended with data related to climate, land cover and human population, which can be indirectly linked to variability of the SICCT test sensitivity or the survival of the pathogen in environment. Robinson et al. 28  compiled the Climate Hydrology and Ecology Research Support System meteorology dataset for Great Britain (CHESS-met), which consists of meteorological variables for the period between 1961 and 2015. The data has a map structure which is formatted as a 1 km resolution grid containing daily mean measures of humidity, precipitation, air pressure, downward longwave radiation, downward shortwave radiation, wind speed, temperature and estimated evapotranspiration.
Machine learning. A ML method may be interpreted as a multivariate parameterised mathematical function taking a vector of features of a farm as an input and producing a classification of the farm as either bTB-free or future breakdown. The goal is to adjust the internal parameters, such that the output of the function matches the correct classification of the farms. The learning refers to optimisation of these parameters during a training phase according to a training dataset, which is a subset of data with known categories of instances. Once the parameters are set, the function can classify unseen data. In this study, four ML algorithms (Neural Network, Random Forest, Gradient Boosted Trees and Support Vector Classifier) were used and their performances were compared. All the methods were implemented in the Scikit Learn package of Python  31.
Data split. The whole dataset of herd-level SICCT test results extended with data related to bTB infection risk was divided into training, development and testing disjoint subsets by year. The training set included data from 2012 to 2013, totalling 246,298 data instances, 3. 78% of which were ground truth positive. The development set included 128,768 data points from 2014, where 3. 74% of data were positive. The testing set of 2015 consisted of 130,945 data instances, 3. 58% of which were positive. For training a ML model, ideally, a training set contains equal numbers of positives and negatives, so that the trained model is unbiased towards any of the classes  32. However, due to bTB-negative herds being more frequent than positive ones, the data instances within the training set were reweighted.
The two optimisation methods obtained equivalent sets of hyperparameters leading to almost identical performance of the predictive models, however, the random search converged faster. The models were evaluated based on the area under the receiver operating characteristic curve (AUC), which is a Se-Sp dependence curve. In general, models with AUC of 0. 5 can be interpreted as performing similarly to a random guess, models with AUC larger than 0. 7 obtain good performance while those with AUC above 0. 9 are excellent predictors. The AUC was also used to select the best-performing ML method. Variable importance. GBT and RF both provide levels of variable importance, referred to as mean decrease of gini impurity (MDG), calculated for every input variable. The details of importance calculation are given in the Scikit-learn package documentation  33.
The threshold value optimised with 2014 development data was also used in the testing phase. Hypothesis testing. To test whether the performance of the best ML model was significantly better than the herd-level SICCT test alone, both their HSes and HSps were compared. A paired samples Student's t-test was applied to the distributions of HSe and HSp to test if the mean HSe of the ML model was significantly larger than the mean HSe of the herd-level SICCT test, and whether the mean HSp of the model was larger than the mean HSp of the herd-level SICCT test. The distributions were obtained by randomly splitting the testing set of 2015 data into 100 disjoint subsets. Then, the breakdown predictions were made for each of the subsets, and the HSe and HSp were calculated for the herd-level SICCT test and for the ML predictions separately in every testing subset.
, 1974; Collins and Moon, 1979). However, no significant technological innovations in topographic techniques that would have improved and simplified field surveys appeared before the beginning of the twenty-first century  (James & Robson, 2012; Westoby et al. , 2012; Nadal-Romero et al. , 2015; Smith et al. , 2015; Dietrich, 2016; Eltner et al. , 2016). Numerous studies have analyzed the accuracy and resolution  (Smith et al. , 2015; Eltner et al. , 2016; Grussenmeyer et al. , 2016; Murtiyoso and Grussenmeyer, 2017)  of Terrestrial Laser Scanning (TLS;  Telling et al. , 2017)  and Structure from Motion (SfM)-derived topographic datasets for a wide range of geomorphic environments (Table  1 ). Compared to the first TLS instrument that was developed two decades ago, recent technologies (e. g.
Within this context, the combination of survey technologies (e. g. , mixing GPS and LiDAR data) is useful to generate complete and accurate datasets  (Wheaton et al. , 2009; Bangen et al. , 2014b). This provides a way to visualize and precisely quantify channel adjustments (e. g. , the sediment budgets) by applying DEM of Difference (DoD)  (Martin and Church, 1995; Brasington et al. , 2000a; Lane et al. , 2000; Wheaton et al. , 2009; Milan, 2012; Wyrick et al. , 2016). With the increased number of river restoration projects during the last three decades  (Morandi et al. , 2014) , many studies tried to develop indicators that would allow assessing the efficiency and sustainability of such management actions  (Berger, 1997; Palmer et al. , 2005; Brierley and Fryirs, 2008; Dufour and Piégay, 2009; Pander and Geist, 2013; Rinaldi et al.
TLS and SfM comparison Focusing on the three main curvature sectors of the artificial side channel (Fig. 1c ), the initial post-restoration state (state 0 = before floods and morphological evolution) was recorded before the first floods by a combination of TLS and SfM methods. TLS was set up with a FARO Focus 3-D with complete vertical and horizontal 360° view. This system can deliver an accuracy of 2 mm at 25 m. The system was setup to provide a spatial sampling of 3 mm at 10 m (Faro resolution setting: ½). Photogrammetric images were recorded using a calibrated Canon EOS 5D Mark II camera with a focal length of 28 mm. The camera was fixed on a tripod and translated along the riverbanks to satisfy a forward overlap of 70-80% between the successive parallel images. Additional oblique images were recorded to improve the geometry of the photogrammetry datasets.
Circular coded control targets permanently fixed on trees and regularly distributed on both riverbanks were used to process the images and georeference each curvature sector. To avoid errors related to the movement of trees (e. g. , tree growth), the XYZ data of each target were collected at least once a year, if possible, during the SfM acquisition. Dense point clouds (Medium quality setting, i. e. , ~1 pt/cm after post-processing) were generated using the Photoscan SfM software (version 1. 2. 6) and the optimization tool was used to georeference each sector. The internal projection errors for the GCPs were less than 5 cm and below 0. 5 pixel (Table  2 ). The accuracy of the SfM method was assessed by a cloud to cloud registration algorithm considering TLS as a reference. Cloud to cloud accuracy ranged from 2 to 5 cm.
Topographical data A Total Station recording was used to detect morphological changes beneath the water surface, as proposed by  Lane et al. (1994) , and beyond the water between the curvature sectors. Regularly spaced (20 m) cross sections composed of 5 to 8 points were recorded in XYZ along the whole artificial side channel. Supplementary thalweg points were collected between each cross section for the longitudinal profile. In fact, such a method is time-consuming and can only generate a relatively small set of bathymetric points  (Chandler et al. , 2002) , but provides a reliable terrain-modeling method to quantify changes at the scale of the whole channel  (Brasington et al. , 2000). Differential GNSS positioning was used to set up reference points upstream and downstream of the channel (Leica TS02 Total Station and Leica GPS 1200 System used in GNSS RTK Mode).
Point cloud comparison For each sector, accurate TIN models based on the dense point clouds were merged with DEM bathymetric data to generate "combined models". The CloudCompare M3C2 (version 2. 8. 1) method was used to record differences between point clouds from two consecutive surveys  (Lague et al. , 2013; Fig. 2). The normal-scale parameter has been set to 1-2 m. We consider this parameter satisfactory to average the topographical discontinuities of the riverbanks. To provide a reliable comparison of high morphological volume changes (especially with regards to lateral erosion), a gridding interpolation was defined before merging two point clouds. Finally, a script implementation based on MatLab functions developed by TRIO group was used to calculate volume differences automatically between two clouds at two spatial scales: the whole channel and the three sectors.
Sediment budget close to the three curvature sectors Comparisons between the 3-D combined models are locally affected by data gaps (white holes in Fig. 3a ), which results from removing of riverine vegetation and logjams during data processing. However, the colorimetric visualization of diachronic evolutions based on cloud to cloud comparisons allowed us to locate and quantify accurately erosion versus deposition processes at the channel boundaries. Suspended load was determined for 10 L of water sampled in the middle of the channel during an ecological flood (the upstream part of the water column was sampled). After the first short and low-intensity hydrological trial flow pulse (surveying period 1, top of the Fig. 3a ), the bottom of the channel in the three curvature sectors showed little morphological changes (i.