dataesr/scientific-paragraphs-categorization huggingface dataset:
As dFADs data contain both positions while the dFAD was onboard the purse-seine vessel and positions while the dFAD was drifting at sea, four discriminative classification methods are compared for their ability to correctly identify dFAD drift phases on a subset of the data with known state. The classification method with the highest performance is then applied to the full dataset and used to describe the spatio-temporal patterns of dFAD use by a major component of the tropical tuna purse seine fishery in the Atlantic and Indian Oceans. Classified data serve as a basis (i) to identify dFAD density hotspots and measure time and distance at sea, all essential to understanding the impacts of an array of floating objects on tuna stocks and pelagic ecosystems, (ii) to detect dFAD beaching events and their corresponding deployment positions so as to evaluate potential damage to fragile coastal ecosystems and propose management strategies, and (iii) to identify "ineffective" or "ghost" dFAD fishing effort as characterized by dFADs moving out of established fishing areas.
researchers, for the replication of the present study or another purpose, regardless of the intended use, by contacting Dr. Michel Goujon, CEO of ORTHONGEL, the organisation of the French freezing tuna producer companies ( mgoujon@orthongel. fr) and Dr. Pascal Bach, from the IRD Tuna Observatory ( pascal. bach@ird. fr). Funding: The work of DMK and AM was supported by the AMPED project (www. amped. ird. fr) through a grant from the French National Research Agency (ANR, www. agence-nationale-recherche. fr), Systerra Programme, grant number ANR-08-STRA-03. AM also benefited from a grant of France Filière Pêche (www. francefilierepêche. fr). R. Data on catch and effort of French purse seiners have been collected by the 'Institut de Recherche pour le Développement' (IRD) since the development of the fishery in the Atlantic and Indian oceans in the early 1970s and 1980s, respectively.
For the present study, fine-scale operational data based on skipper logbooks were available for the period 2007-2011. They describe the activities of purse seiners, the association type of tuna schools detected (i. e. free swimming or dFAD-associated school), the positions of purse seine fishing sets, as well as the tonnage and commercial size categories of tuna catches. Similar catch and effort data are available for other components of the purse-seine fishery, notably the Spanish fleet, on a 1°lon-lat grid. In addition, French purse seiners have been equipped with Vessel Monitoring Systems (VMS) since the early 2000s as part of the monitoring, control, and surveillance program of the European Union. The GPS position of each vessel is recorded on an hourly basis, enabling construction of vessel-specific trajectories over their typical 4-6 week fishing trips.
Buoys also occasionally erroneously produce two consecutive identical positions separated by a finite period of time. These "doubled" positions can produce inconsistent, extremely-high perceived speeds (reaching sometimes 100 m. s -1 ) between the second repeated position and the position immediately after it. We eliminated such repeated positions, keeping only the first of the two identical positions. The resulting dataset is stored in a PostGreSQL 9. 1. 9/PostGIS 2. 0. 1 database, and includes approximately 1,741,000 positions from 9,289 buoys used by 29 purse seiners operating in the Atlantic and Indian Oceans during the period 2007-2011 (Fig  1 ). The fraction of purse seiners and auxiliary vessels that have provided GPS buoy positions varies between years and fishing companies, with a gradual increase towards 100% coverage of French fishing vessels in recent years (Table  1 ).
Given the complex utilisation of dFADs and GPS buoys described in the previous section, it is useful to define specific terminology for different parts of the dFADs positions dataset. Position data are referred to as "GPS buoy positions". The term "GPS buoy track" is used to refer to the ensemble of positions belonging to a single GPS buoy. Tracks are broken down into "on board" and "at sea" trajectories, consisting of sequences of positions classified as having a consistent state. "At sea" trajectories are also referred to as "dFAD trajectories" or "dFAD positions" as these correspond to periods the GPS buoy is generally attached to a dFAD.
Construction of the learning dataset The true state of a subset of the GPS positions available was manually determined using complementary fishery data. Vessels trajectories were inferred from VMS position measurements and superimposed in space and time on GPS buoy trajectories to detect shared pieces of tracks (Fig  2). VMS tracks possessing positions close in space (<5 km) and time (<1 d) to GPS buoy positions were initially selected for closer comparison. These "nearby" VMS trajectories were interpolated at the emission times of GPS buoys, and distances between buoys and fishing vessels at identical times were calculated. Original buoy tracks and nearby VMS tracks, spatial separations between the two, buoy speeds, and locations of fishing sets were simultaneously visualized using Matlab  [22].
For example, it was sometimes difficult to visually determine a transition from "on board" to "at sea". In such cases, if buoy speed decreased between time t and t+1, then position at time t was assigned a class "on board" and "at sea" at t+1. A total of 19,927 points corresponding to 207 different buoy trajectories were classified using this method (2. 3% of the buoy dataset). The majority of this learning dataset consisted of "at sea" positions, with 13. 8% of the learning dataset classified as "on board" positions.
Classification model selection Four binary classification methods were compared for their ability to correctly predict the "at sea" (S) or "on board" (B) state of each dFAD position based on a set of predictor variables characterizing buoy speed, acceleration, time step, water temperature, etc. at each position (Table  2 ). The intended, long-term use of classification models is to optimally classify new dFAD position data received from the fishing companies on a quarterly basis. As the resulting large dFAD dataset will be used by multiple individuals having disparate levels of statistical expertise, it is desirable to identify the simplest, most-computationally-efficient classification method that can accurately predict buoy state. Therefore, although one would expect that more sophisticated classification methods (e.
Configuration of classification models The performance of the best model configuration for each of the 4 classification models was evaluated using cross-validation. The learning dataset was randomly split 100 times into a training dataset (used for model calibration) and a validation dataset (used to evaluate model performance) each containing 50% of the learning trajectories. During the calibration phase, each of the 100 training datasets was used to build an optimal version of the MLR, ANN, and RF models. The full list of predictor variables can be found in Table  2. With the exception of the VEL model, which was manually calibrated based on the maximum "at sea" speed observed in the learning dataset, all model calibrations and predictions were carried out using R version R. 2. 14  [31]  with the caret package (version 5.
15-023,  [32] ) and its train function. The train function uses a bootstrap approach, with 200 iterations, to determine an optimal set of model configuration parameters (i. e. , parameters that affect model structure and complexity, such as the number of hidden neurons in the ANN model; Table  3 ). For each of the 100 training datasets described above, 200 different random subsets are generated by resampling with replacement the training dataset, and then each given classification method is calibrated for each subset using all possible combinations of model-configuration parameter values. For each subset, the accuracy rate (fraction of correct predictions) and the Kappa statistic (which measures to what degree the prediction will be repeatable and reproducible) are computed using the remaining, unused part of the original training dataset.
The set of configuration parameter values that maximizes the mean accuracy and mean Kappa among the 200 bootstraps is used to calibrate the given classification model to the entire training dataset. In the end, this procedure produces 100 optimized predictive classification models, one for each training dataset. The train function internally calls a different model for each classification method: the MLR and ANN   [33] ), and the RF method of the Random-Forest package (version 4. 6-6,  [34] ). As the learning dataset is imbalanced in favour of "at sea" positions (86. 2%), we also considered two approaches for correcting for imbalanced data: (1) using an optimal threshold other than 0. 5 as the minimum probability required to declare a point "at sea" and (2) forcibly balancing the training dataset before model calibration.
Comparison of classification methods The validation phase consisted of using the models calibrated on the 100 training datasets to predict the class of the positions in the corresponding 100 validation datasets. Classification model performance was evaluated through a balance of 5 indicators of performance based on minimization of the misclassification of "at sea" and "on board" positions ("position based" indicators, Table  4 ) and based on the ability to minimize the incorrect segmentation of trajectories (when sequences "at sea"-"on board" or "on board"-"at sea" occur along a trajectory) due to classification errors ("trajectory based" indicator, Table  4 ). 100 values of each indicator were calculated over the cross-validation procedure to obtain a distribution of their values. Pairwise comparisons of the performance of the models were then performed based on two sided t-tests (α = 0.
Model application and data analysis The best classification model including post-processing corrections was applied to the full buoy position dataset, and "at sea" and "on board" predictions were made for each position. Model predictions were then employed to detect potential fishing set positions assuming that transitions from "at sea" to "on board" potentially correspond to the retrieval of a dFAD and its buoy from the sea. Spatial patterns in fishing positions predicted by the model were compared to observed fishing positions as declared in fishing vessel logbooks. Note that predicted retrieval locations include some operations on floating objects that do not correspond to a fishing set (e. g. , maintenance, buoy displacement to a different location or foreign buoy replacement), as well as buoys lost at sea due to the sinking of the attached floating object.
In addition, possible dFAD beaching events were identified using the original, unclassified dataset by series of repeated geographical positions. The unclassified dataset was used to avoid any possible confusion in the classification algorithm between at port and beached positions. We assumed that at least 3 repetitions of the same position were necessary to identify a possible beaching event as occurrence of 2 repeated positions is known to be related to failures to correctly capture a GPS signal (see section Buoys positions data and pre-processing). Final results were obtained using 2 successive filters on these potential beaching-event positions. First, we eliminated positions located within 10 km of a port, assuming that these are likely to be simply fishing vessel anchorage points. Second, we eliminated positions located more than 5 km from land (accounting for 5.
2%, 17. 8% and 12. 3% in the MLR, ANN and RF (without post-processing) models, respectively. The segmentation rate for these 3 models decreases from a 143% increase in state transitions (i. e. , predictions of 'BS' or 'SB' transitions relative to the true rate in the learning dataset) for VEL to +90. 8, 93. 2%, 62% for MLR, ANN, RF models respectively. Though the MLR, ANN and RF models produce similar values for True Sea Rate, all other indicators of performance indicate that the RF model performs considerably better than the MLR and ANN models, especially with regards to False Sea Rate, precision and segmentation rate (Table  5 ). Because of its superior performance, the RF model was chosen as the best classifier for dFAD trajectory data. Replacing RF predicted classification sequences of the type BSSB (i.
e. two, isolated points classified as "at sea") with BBBB considerably improves performance indicators. Error rate drops from 2. 6% to 2. 2% on average for the 100 validation datasets (Fig  3 ). This correction also significantly improves all other performance indicators (Table  5 ), notably reducing the segmentation rate from 60% to 25%. Using the RF model with post-processing correction, we predict that 15. 5% of the full dFAD trajectory dataset consists of "on board" positions, showing the importance of separating "at sea" and "on board" positions before analysing patterns of dFAD use.
Lost GPS buoys Putative beaching events, identified by positions that repeat at least three times far from a port, occur in 26. 4% of the GPS buoy tracks, corresponding to 10. 5% of the "at sea" trajectories in the dataset (a lower percentage because GPS buoys have more than one "at sea" trajectory). When distance to the coast is also taken into account, the percentage of beached at sea trajectories decreases to 9. 9% (i. e. , 5. 7% of all putative beaching events occurred more than 5 km from the coast). More potentially beached GPS buoys are detected in the Indian Ocean (1328) than in the Atlantic Ocean (1128), in line with the larger number of dFADs deployed by the French fleet in this ocean. In the Atlantic Ocean, potentially beached buoys tend to concentrate in the Gulf of Guinea but some buoys also cross the entire ocean to strand on the Brazilian coast (Fig  6A ).
'Ineffective' dFAD effort A total of 6,563 GPS buoys (i. e. 68. 4% of the dataset) were found to be drifting outside French fishing grounds (see Fig  4A  for the location of French fishing sets on dFADs) at least once during their whole "at sea" set of trajectories. By comparison, with fishing grounds based on all fleets for the period 2006-2012 (Fig  7B ), this number decreases to 5,420 (57% of the dataset). Though the average fraction of total drift time spent outside fishing grounds is relatively small (3. 05% for French fishing grounds; 2. 2% for all fishing grounds), for some buoys, the time spent outside fishing grounds is extensive. For example, 20. 6% of the drifting trajectories spent less than 50% of the time inside French fishing grounds (8. 5% if all fishing grounds are considered).
However, our aim was not only to identify the best classification method, but also to assess trade-offs in terms of model transparency and computational time. In this context, RF produces a non-negligible improvement in performance indicators that justifies its use, though this comes at a computational cost (~3-4 hours computational time to classify all currently-available dFAD position data with RF, versus ~10 minutes for MLR). Overall, the False Sea Rate indicates that the RF model is highly efficient at identifying when the buoy is drifting at sea. Nevertheless, erroneous splitting of "at sea" or "on board" trajectory segments as a result of misclassifications remains important. For example, the RF model predicts 59. 3% more trajectory pieces than observed in the training dataset. Though post-processing to remove very short "at sea" trajectory segments reduces the segmentation rate from 59.
One possibility is to use a learning dataset that is balanced in terms of number of "at sea" versus "on board" positions. This approach was tested when developing our classification model, but did not improve results (see S1 File). A balanced learning dataset is generally desirable in cases where either one prefers to err in favour of the minority class (e. g. , when prediction the species distribution of a rare, endangered species) or one believes that the true prevalence of the minority class is higher than what is observed in the learning dataset  [47, 48]. Neither of these is the case for our dataset. Furthermore, balancing the learning dataset does not contribute to taking into account the temporal relationship between successive observations (see following paragraph). Performance indicator improvements due to post-processing corrections to the RF model outputs suggest that the sequence of "on board" and "at sea" states in buoy trajectories is informative.
In addition, they consider that the probability of being in a given state at a given time depends on the past states. HSMM, in particular, considers the probability of being in a given state as a function of the time already spent in this state  [49]. The use of HSMMs is not trivial in our case due to the highly irregular timesteps of dFAD trajectories and the high computational costs involved in applying these methods to large datasets. Furthermore, when fishing vessels concentrate in the same area, the probability of a dFAD to be found and its buoy to be transferred after a short drift is higher. Short "at sea" sections of trajectory during periods of intense dFAD fishing may, therefore, be real events and applications of HSMM to these data must take this seasonality into account. The simplest and most direct solution to these issues would be to increase availability to information on deployment and recovery events of individual dFADs.
Acknowledgments The authors wish to acknowledge the contributions of the staff of the 'Observatoire Thonier' and all the technicians and fishermen who have been involved in data collection, processing, and management. The authors are grateful to ORTHONGEL for providing us with the data and information on the buoy positions, especially  Laurent Pinault (CFTO) and Anthony Claude (Saupiquet)  for their helpful assistance. The analysis benefited from fruitful discussions with  Alain Fonteneau  and  Sophie Bertrand. The authors also thank all the skippers who gave time to share their experience and knowledge on the purse seine fisheries. We are grateful to the two anonymous reviewers and the associate editor for their useful comments and suggestions. This is contribution number XXXX of the  Virginia Institute of Marine Science, College of William and Mary.
Expression of Pfl01_2810-tspo Operon is Regulated Partly through the Alternative Sigma Factor RpoH A bioinformatic analysis of the 227 bp region lying upstream of Pfl01_2810-tspo using the MEME software  (Brown et al. , 2013) , failed to identify an algU consensus binding site (data not shown), suggesting that the effect of AlgU on the expression of this promoter region may be indirect. However, a sequence that showed 90% of identity with the previously proposed binding consensus sequence for the E. coli RpoH sigma factor (σ 32) was found 95 bp upstream the putative +1 translational start site of Pfl01_2810  (Cowing et al. , 1985 ; Figure  4A ). This consensus sequence was also detected upstream P. fluorescens Pf0-1 dnaK, groEL, or grpE genes (Figure  4A ) encoding established direct RpoH targets in P.
Discussion Based on the Pseudomonas database  (Winsor et al. , 2011) , P. fluorescens Pfl01_2810tspo was predicted to form an operonic structure with the Pfl01_2810 gene encoding a putative hybrid histidine kinase. We confirm in this study that the two genes are indeed co-transcribed, suggesting that both genes are genetically, but also possibly functionally linked. This latter hypothesis is supported by a previous STRING functional analysis  (Leneveu-Jenvrin et al. , 2014). Histidine kinases are often associated with their response regulator, which contains a receiver or response regulator domain, forming a two component signal transduction system. Hybrid-type histidine kinases comprise a histidine kinase with a receiver domain within one molecule  (Stock et al. , 2000). It is conceivable that TSPO could participate in the signaling-transduction pathway triggered by Pfl01_2810.
Acknowledgments We wish to thank  Mark Silby  for the gift of P. fluorescens Pf0-1 strain,  R. E. W. Hancock  and  M. Bains  for the gift of the rpoH mutant strain. CL-J is a recipient of a doctoral fellowship from the  region Haute-Normandie  ( GRR-SéSa ). This study was supported by grants from the  Conseil Général de l'Eure , the  Région Haute Normandie , the  Grand Evreux Agglomeration  and  FEDER  funds.
Microplastic debris floating at the ocean surface can harm marine life. Understanding the severity of this harm requires knowledge of plastic abundance and distributions. Dozens of expeditions measuring microplastics have been carried out since the 1970s, but they have primarily focused on the North Atlantic and North Pacific accumulation zones, with much sparser coverage elsewhere. Here, we use the largest dataset of microplastic measurements assembled to date to assess the confidence we can have in global estimates of microplastic abundance and mass. We use a rigorous statistical framework to standardize a global dataset of plastic marine debris measured using surface-trawling plankton nets and coupled this with three different ocean circulation models to spatially interpolate the observations.
In this study, we focus on assessing the amount and distribution of 'small' (nominally <200 mm) plastic debris on the ocean surface, as these are by far the most sampled data set and also have demonstrated biological impact  (Rochman et al 2015) , although larger items can also impact biota. At the sea surface, microplastic marine debris is typically measured by surface-towing plankton nets with mesh ranging from 0. 1 to 0. 5 mm, which capture particles limited to the size of the net aperture. Net tow sampling efforts typically capture plastic particles smaller than 10 mm in size (Morét-Ferguson et al 2010), while less numerous larger items are observed by visual surveys with ships or aircraft. The vast majority of observations since the 1970s have been made using plankton nets, with broadly similar sampling methodologies but variable reporting units (particle count per area or volume, or mass per area or volume).
With the recent addition of relatively large and more geographically widespread datasets, and oceanographic numerical models that predict debris accumulation at the sea surface from surface current patterns, the first global estimates of the reservoir of floating plastic debris have recently been reported  (Cózar et al 2014 , Eriksen et al 2014). Using plankton net data (1127 trawls) spatially averaged in accumulation and non-accumulation zones defined from a statistical oceanographic model  (Maximenko et al 2012) ,  Cózar et al (2014)  estimated between 7000 and 35 000 tons of floating plastic (0. 20-100 mm in size) in the Atlantic, Pacific and Indian Oceans combined. Using a nearly independent plankton net dataset (680 trawls),  Eriksen et al (2014)  computed a global estimate of floating plastic (0.
33-200 mm in size; 66 140 metric tons) using a different oceanographic model  (Lebreton et al 2012)  whose output was scaled by the globally measured plastic concentration. Given the methodological differences between these studies, it is encouraging that the resulting estimates are so close. Here we estimate the global standing stock of small floating plastic debris with the most comprehensive dataset, ocean models and ocean plastic input estimates available. We compiled all available plastic data collected with surface-trawling plankton nets (more than 11 000 observations, including those in  Cózar et al 2014 and Eriksen et al 2014) , resolved sampling biases and other variations using a statistical model, and then used the standardized dataset to scale the outputs of three ocean circulation models.