dataesr/scientific-paragraphs-categorization huggingface dataset:
Imagery has become a key tool for assessing deep-sea megafaunal biodiversity, historically based on physical sampling using fishing gears. Image datasets provide quantitative and repeatable estimates, small-scale spatial patterns and habitat descriptions. However, taxon identification from images is challenging and often relies on morphotypes without considering a taxonomic framework. Taxon identification is particularly challenging in regions where the fauna is poorly known and/or highly diverse. Furthermore, the efficiency of imagery and physical sampling may vary among habitat types. Here, we compared biodiversity metrics (alpha and gamma diversity, composition) based on physical sampling (dredging and trawling) and towed-camera still images (1) along the upper continental slope of Papua New Guinea (sedimented slope with wood-falls, a canyon and cold seeps), and (2) on the outer slopes of the volcanic islands of Mayotte, dominated by hard bottoms.
However, biodiversity data on the Comoros archipelago are scarce, with only one dedicated expedition in this area  (BENTHEDI, Thomassin, 1977)  and with sampling gears deployed down to 3,700 m. Details of the MNHN and BENTHEDI expeditions can be found on BaseExp database (Muséum national d'Histoire naturelle, 2019) 1. Images were acquired during the BioMaGlo expedition  (Corbari et al. , 2017) , on board the R/V Antea on 21 January 2017 in the Mayotte-Gloriosos area. Three slope orientations were explored by camera: (1) the northwestern slopes characterized by a plateau at 600 m depth and covering 100 km 2 then surrounded by deeper crater-like or volcanoes network features; (2) the southwestern slopes with a deeper and larger plateau at 750 m covering 250 km 2 and (3) the eastern slopes, extending continuously down to 1,000 m depth and characterized by shallower volcanic cones  (Audru et al.
Images were acquired at 10 s intervals (PNG) and 30 s intervals (Mayotte) with an HD Camera (NIXON D700, focal length 18 mm, resolution 4,256 × 2,832 pixels) and geo-referenced using the ship positioning system processed with Adelie tools (French Oceanographic Fleet) developed at IFREMER and implemented using ArcGiS software V10. 3. The detection of organisms either from the observation in images or from capture by the fishing gears, reflects the respective efficiency of the cameras and the gears rather than the exact occurrence of the organisms at a given place. Indeed, some organisms may have been missed because the images are not overlapping. Similarly the probability of capture by the fishing gears may vary according to the topography and/or the nature of the substrate.
Identifications From Images A total of 7,674 images were analyzed and annotated (i. e. , organism delineation in images and labeling of taxonomic ranks) using the web platform BIIGLE 2. 0 (Benthic Image Indexing and Graphical Labelling Environment)  (Langenkämper et al. , 2017). BIIGLE 2. 0 was chosen because it provides effective methods (1) allowing collaborative and interactive work with taxonomists who can actively contribute to the identification of organisms from images and (2) allowing easy comparisons and revisions of annotations with the LARGO tool. The platform allows the export of a database with observation records of each faunal annotation. We then summed up these observations to obtain an abundance matrix for each georeferenced image. Megafaunal identification from images consisted of five steps divided into three main processes that involve different levels of expertise: non-expert annotation, objective identification and contextual identification, the last two performed in collaboration with taxonomists (Figure  3  and corresponding details on the working steps in Supplementary Material 1).
Final Dataset Processing After exporting the matrix of specimen observations from each georeferenced image of the BIIGLE platform, hierarchical taxonomic labels were homogenized between physical sample and image datasets according to the taxonomic hierarchy provided by the Worlds Register of Marine Species (WoRMS) database. Contrary to physical sampling, images can provide abundance data. Thus, to compare biodiversity patterns between images and physical samples along dives and in the surrounding area, we transformed the abundance data from image analysis into presence/absence data for each image. For easier naming convention, we will refer to the term Pisces for Osteichthyes/Chondrichthyes groups. Diversity metrics were used: alpha diversity corresponding to the intra-transect diversity that was analyzed at the different levels of identification reached in the images, which were generally not the species level.
Statistical Analyses All analyses were performed using the R environment (V3. 6. 3) (R Core Team, 2020). Taxonomic richness comparison between camera transects and co-located sampling was assessed at different taxonomic levels using sample-based rarefaction curves with 999 random permutations on presence/absence data using the vegan package (speceaccum function)  (Oksanen et al. , 2019). Differences in assemblage composition between camera transects and co-located sampling were assessed using principal component analysis (PCA) on Hellinger-transformed presence/absence data with the ade4 package (dudi. pca function)  (Dray and Dufour, 2007). This transformation allows the species presence/absence dataset to be represented in a Euclidean space  (Legendre and Gallagher, 2001). For crustaceans, PCA and Venn analyses were performed at the genus rank only, to consider comparative taxonomic names between image and sampling datasets (i.
e. , at a specific rank of comparison, morphospecies names in the image dataset do not match the species name in the sampling dataset). TABLE 2 | Summary of image acquisition and physical sampling efforts with sites information for the dive, sampling along dive (cl-dive) and in the surrounding area (cl-around) along volcanic island slopes of Mayotte. Similarities and dissimilarities in taxonomic composition between images and physical samples and according to the image acquisition and physical sampling efforts (camera transects, colocated dredge/trawl transects and surrounding area transects) were represented in Venn diagram using the gplots package (venn function)  (Warnes et al. , 2020).
Along the volcanic island slopes, we identified a higher number of echinoderm taxa either from specimens observed in images or from specimens collected by the sampling gears. For asteroids and echinoids identification from images, we reached the genus rank for most individuals (Table  3C ). For comparing the level of diversity estimated from images and from physical samples, in sedimented slopes and volcanic island slopes datasets, we selected the order and family ranks for fishes, and the genus and species/morphospecies ranks for crustaceans. We made the comparisons at the genus rank in hardbottom environments for asteroids and echinoids, because we generally reached these levels of identification both for specimens observed in images and for specimens collected by the sampling gears. The inventory for each targeted taxon and taxonomic level, in the images and physical samples, are listed in Supplementary Material 4 and raw databases used for analyses are available in Supplementary Materials 5 (PNG) and 6 (Mayotte).
We considered four groups of Caridea specimens observed in images as belonging to different genera and thus delimited them into four different photo-taxa attributed to a reliable genus rank. We identified these four genera primarily based on observable morphological characters from images. In the physical samples (data from the BioMaGlo expeditions as well as from previous expeditions), we identified species attributed to these four genera that thus constitute a pool of species potentially present in the analyzed images (Table  4 ). For each of these four genera, we compared the morphological characters observable on the phototaxa by using the characters of potential species. From this comparison, we were able to determine the individuals to the species or morphospecies ranks, whereas for some photo-taxa, identification did not go beyond the genus rank.
However, this approach requires DNAbarcoding reference databases which are yet far from completed for the deep-sea fauna. Crustaceans are better sampled by fishing gears if comparing the metrics at the genus rank. However, when considering the identification of morphospecies in images and the identification of species for physical samples, we obtained similar metrics. The metrics estimated at the species level for images might have overestimated the real richness. Indeed, several morphospecies can represent a single species with intraspecific polymorphism. Such issues can be solved only when DNA barcoding analysis is conducted on the collected specimens. Nevertheless, underestimation of diversity is also possible, because one morphospecies can potentially gather several species  (Williams et al.
, 2015). This could explain the lower richness observed in images compared to physical samples in this study (190 species identified from sleds, vs 57 photo-taxa) which contrasts with our results. The use of taxonomic levels, or thresholds, defined for each taxonomic group, could be a more cautious and robust way to assess the biodiversity from image datasets, even if these levels are heterogeneous. Images alone give a partial estimation of the diversity notably due to identification limitations, particularly for the mobile species that appeared blurry in images (e. g. , Malacostraca indeterminabilis dominated in the canyon site) or for smallsized taxa (e. g. , genus Echinocyamus for echinoids, genus Lepas for crustaceans). In soft bottoms, endogenous fauna cannot be detected in images  (Rice et al.
, 2010). Unanswerable couplets can be particularly limiting in the case of image analyses (characters not visible from image), leading us to focus on the development of a multiple-access key such that the user can choose any character according its availability (observable from images) or familiarity (doubt regarding some characters), and also recommended by  Howell et al. (2019)  in the case of imagebased identification. This type of key is available for echinoid identification at the genus rank. It can be easily updated and quickly provides the information sought to end-users on a web platform. It also facilitates the documentation of environmental settings and of main abiotic and biotic elements of habitats along with their implementation in datasets (e. g. , taxon depth range, substrate type, associated living communities, etc.
Such indications should help the delimitation of morphospecies in future studies. However, the naming convention for certain morphospecies in this study will certainly not be the same in another study. There is a need for future studies to develop identification keys in other oceanic regions, systematically accompanied by a morphospecies catalog-if morphospecies are delimited-and by listing the corresponding, potentially analogous known species in the area. The identification keys and the morphospecies catalog should both relied on the standardized database of marine taxa image catalog developed by  Howell et al. (2019)  in the Atlantic region, and using a Darwin Core format.
This methodology has led to the development of identification keys adapted for imagery data, for target taxonomic groups (echinoids, asteroids and shrimps). Improvement of image resolution and different camera angles could offer interesting perspective to improve megafauna' identification from images, and for instance, to develop identification keys for fish. The combination of image acquisition with targeted sampling is also a crucial way for the calibration of the identification. Finally, these identification keys were developed from the Mozambique Channel seamount fauna dataset and could be extended to other areas of the Indo-West Pacific region, through further international collaborative effort.
ACKNOWLEDGMENTS We would like to thank the officers and crews of RV's ANTEA and ALIS as well as the SCAMPI towed-camera team for their contribution and assistance with data acquisition. We are grateful to the chief scientists of the  Madeep, BioMaGlo, BioPapua , and  Papua Niugini  cruises as well as the entire scientific team. Moreover, we would like to thank  J. Tourolle  for the helping and assisting in the analysis of GIS data, EJ. Pernet for the processing of the fauna samples,  L. Keszler  for the primary annotation of the BioMaGlo campaign images. We are grateful to  T. Nattkemper  and  D. Langenkämper  for allowing the loading of the image data and their annotation with Biigle 2. 0 and for their technical support. We are particularly grateful to all the taxonomists who have contributed to the identification of the fauna in the images and in the collections:  N.
Electronic copy available at: https://ssrn. com/abstract=3519040 Electronic copy available at: https://ssrn. com/abstract=3357683 Working Paper -November 2019 The practice at WSP has been to select the replacement device randomly, due to the large volume of transactions in its reverse logistics system. As such, the remaining OEM warranty on the replacement device has no relationship to the remaining time on the customer warranty. Since some devices can fail multiple times, randomly assigning replacement devices to customers can lead to a significant number of out-of-OEM-warranty returns. For the devices that we examine in this paper, WSP's data indicate that, between 15% and 25% of returned devices were out-of-OEM-warranty returns under WSP's current practice. The hundreds of out-of-OEM-warranty returns that WSP receives per day are a significant source of costs and operational overhead.
In WSP's case, we examine the Oldest-Out-First (OOF) assignment policy. • Heuristic policies: we assign devices based on a policy that blends elements of the youngestout-first policy and the oldest-out-first policy. We consider the Sampling Policy, where devices in inventory are sampled, sorted, and then matched to demand. We display in Table  1  a summary of the performance of these policies on real data from WSP, in comparison to WSP's current practice for five device models. The performance metric is the average uncovered time of the customer-device pairs that are sent as replacements to customers that filed a warranty claim. If a customer has less time left in its customer warranty than the device has left in its OEM warranty, then the uncovered time is zero. Conversely, if the customer has more time left in its customer warranty than the device has left in the OEM warranty, then the uncovered time is the time difference between the end of the customer warranty and the end of the OEM warranty.
Numerical Experiments We now compare and contrast the assignment policies introduced in the previous section through numerical experiments and we investigate the sensitivity of the assignment policies to changes in seed-stock. In addition, this section concludes by simulating the performance of these policies with real data from our partner WSP. For the simulations in this section, we built a discrete-time simulator using the Julia programming language  (Lubin and Dunning 2015). This simulator allows the warranty claims, devices and their termination date to be individually realized. We will use the average uncovered time per replacement device shipped as a metric for measuring the performance of the different assignment policies. That is, we set the assignment cost c t (i, j) = (jmax(i, t)) + for when a device with OEM-warranty termination date i is assigned to a customer with termination date j.
Simulation based on data from WSP We now compare the performance of the assignment policies when applied to sales and failure data for five of WSP's best-selling models, which we refer to as model A to model E. For every device of each model that is sold we have the device's sales date and failure date (if the device failed at all). We depict the number of sold and failed devices for each model in In practice, over 10% of the devices sold for each model failed under warranty. For these tests, we assume an 80% probability that a failed device is refurbished and that seed-stock corresponds to 1% of sales. We set the OEM lead time to three weeks. We assume that the OEM warranty for each device starts on its sales date, as does the customer warranty; and we assume that both warranties are for twelve months.
Similar to our previous simulations, we assume that prices of new and refurbished devices depreciate at a rate of 50% per year over the entire simulation horizon and we use the certaintyequivalent approximation to calculate the selldown levels  (Calmon and Graves 2017). We assume Electronic copy available at: https://ssrn. com/abstract=3519040 Electronic copy available at: https://ssrn. com/abstract=3357683 Table  3  Expected number of out-of-OEM-warranty repairs as a percent of the out-of-OEM-warranty repairs under the random assignment policy a daily time period so that there are failures and matching on a daily basis. The simulation time horizon for each model is 972 days. In Table  1  we display the average uncovered time for each assignment policy for each model. Since we have data on which device was sent to which customer, we can estimate the average uncovered time for WSP's current policy 7.
Then, when a device with OEM warranty termination date i is assigned to a where Pr(device fails at time k) is estimated from the device's empirical failure age distribution. Based on these assumptions, we use the warranty data to estimate the total expected out-of-OEMwarranty repair cost for each device. We assume that a new phone costs $750 and that WSP sources a new phone for a wholesale price of about $650 for a 15% gross margin. Therefore, the initial out-of-OEM-warranty repair cost is $217 per device and we assume that this cost depreciates at a rate of 50% per year 10. We report the expected out-of-OEM-warranty repair cost per device sold for each model in Table  4. By weighting by the number of devices sold for each model we obtain the average expected cost per device sold for each policy; for the random assignment policy, the average is $0.
Even though the matter was not widely acknowledged, informal recycling plays a vital role in waste treatment in many developing countries. In Vietnam, the management of waste collection and treatment faces many difficulties, as there is no statistical data or description of the informal sector in waste collection and recycling to clarify their role, their scale, and their capacity. This paper presents the preliminary data processed by informal waste collectors and aggregators in six Vietnamese cities, namely, Vinh, Nha Trang, Buon Ma Thuot, Da Nang, Ho Chi Minh, and Hanoi. The surveys were conducted from January 2017 to December 2019 by the JEAI Recycurbs Viet project [1] and then from August 2019 to December 2020 in the framework of the COMPOSE project (2019-2021)  [2]. Geographical and sociology data of aggregators will demonstrate the overall background of the presence, spatial allocation, and the establishment process of aggregators in the surveyed cities to find the patterns of establishment and operations of these aggregators.
Description of data collection The data of aggregators in Hanoi were collected many times in the survey for three years from January 2017 to December 2019 and updated in November 2020  [4]. Also, the data of five cities, Vinh, Da Nang, Nha Trang, Buon Ma Thuot, and Ho Chi Minh City, were performed twice per site during 5-10 days of investigation from August 2019 to December 2020  [5] [6] [7] [8] [9]. Two types of data were collected: observational and survey data. The geographical data were first generated using Google My Maps and then input to QGIS 3. 12. To develop the reference database, the aggregators' locations were achieved with a GPS, and the distribution of aggregators was mapped per city. For the survey information, respondents answered questions concerning the aggregator's activities (establishment year, operating hours, number of employees in the aggregator; classifications of recyclable waste purchased; aggregator owner's information (name, age, reason of business's line).
Relationship between number aggregators and city's area, inhabitants and amount of domestic waste generated The amount of domestic waste generated in each city was collected from local official reports and converted to tonnes per day for comparison among cities. The number of aggregators is shown in relation to the area, population and the amount of domestic waste generated in each locality ( Fig. 4  ). The data illustrates that, in general, the number of aggregators is proportional to the scale of areas per city, as well as to volumes of daily waste release and the population. Vinh is the city with the smallest population and the least number of aggregators. While Nha Trang and Buon Ma Thuot are similar in total area and population, the number of aggregators is similar in the two cities. The chart indicates that cities-Category 1 (Vinh, Nha Trang, Buon Ma Thuot)-have      similarities in total area, population, and number of aggregators.
These surveyed conductors are organized into groups of two to three people per group; each group is in charge of surveying from 1 to 3 wards/commune in 1 day. The investigators go along the road axis to search and locate the aggregators. At each aggregator found, the investigators asked the owners to direct them to nearby aggregators in the ward/commune. So, data was collected in many times, ward by ward, commune by commune and district by district until covering all over the city.
Data processing Quantitative and qualitative analyses were done on an aggregated Excel database. From the coordinates and the marked location, the research team created a map to locate positioning collection. The qualitative data analysis was also done through the system mapping tool of aggregators by using QGIS software 3. 12 ( Fig. 8  ), an open-source geographic information management software. The first geographical location data was created for all the aggregators using Google My Maps during data collection. The geo-coordinates were then imported to QGIS to generate a map of aggregators for each city. OpenStreetMap base map was used as the map's background for each city. This one is shown separately for urban and rural areas. The determination of urban and rural boundaries was decided based on the resolution of the National Assembly Standing Committee No.
Acknowledgments Our research team would like to express our gratitude to the  COMPOSE  project, which is funded by  French MEAE  and conducted by  IRD  and the  French Embassy in Vietnam , to complete this study. Data on Hanoi City's recyclable waste collection network was collected/estimated under the framework of the  JEAI Recycurbs Viet  project funded by  IRD  for the period 2017-2019 and updated in 2020 using the author's financial resources and be added synchronized data within the framework of the  COMPOSE  project. Data on aggregator networks in six cities of Vinh,  Da Nang ,  Nha Trang ,  Buon Ma Thuot , and  Ho Chi Minh  were collected under the framework of the  FPSI COMPOSE project  implemented in 2020.
Associated travel history When available on GISAID as part of the uploaded metadata, we made use of this associated metadata information and contacted the submitting labs to determine precise travel dates. For all other cases, we retrieved individual travel histories by contacting the submitting labs -who then in turn contacted either the originating lab or the patient's general practitioner -for any travel records they may have available. This resulted in travel itineraries for 10 patients, with 7 of these also containing detailed dates for the recorded travel. When a returning traveller visited multiple countries on the return trip, we included all visited countries as possible locations of infection by using an ambiguity code in the phylogeographic analysis  (Lemey et al. , 2020). The travel history information collected can be found in Supplementary Table  S1.