dataesr/scientific-paragraphs-categorization huggingface dataset:
59 %). To assess the impact of parameter inference uncertainties on trajectory computation, the posterior parameter distribution is sampled and new trajectories are computed, replacing the ground-truth parameters by the sampled ones. The swimmer ground truth trajectories are accurately recovered: the sampled trajectories tightly frame the original swimmer path as illustrated on a randomly chosen trajectory   (Fig. 4a ). We note that an identical random seed has been taken for these simulations, including the ground truth trajectory, in order to turn off the stochastic uncertainties and only focus on the propagation of inference errors during simulations of swimmer trajectories. Finally, we re-assemble a synthetic dataset by replacing the ground-truth parameters by the inferred ones, i. e. the posterior mean.
Qqplot of the fitted model accelerations versus the ground truth accelerations give an excellent accuracy (Fig. 4d ), with all the points lying on the bisector, except slight divergences on the distribution tails. The fitted model trajectories visually reproduce the qualitative characteristics of the original dataset (Fig. 4c ). The trajectory descriptors of section 2. 1 are then computed on both datasets (ground truth and inferred) and compared (Fig. 4b ). The kinematics descriptors, i. e. acceleration and speed distributions, are very accurately recovered with a relative error of 0. 1%, 3. 2%, 5% for respectively the mean, quantiles 0. 05 and 0. 95 of the acceleration (resp. 0. 9%, 2. 5% ,2% for speed). Some small discrepancies can be observed on the distance and displacement distributions, even if the mean and the quantiles 0.
05 and 0. 95 are close. The interactions between the host biofilm and the acceleration and speed distribution are also recovered with high accuracy. We note that part of the observed discrepancies comes from an additional source of variability of the simulation framework: when a swimmer reaches a domain boundary during a simulation, its trajectory is stopped and a new swimmer is randomly introduced elsewhere in the biofilm (see Materials and Methods for more details). This simulation strategy seems to be responsible of the over-representation of short trajectories in the inferred dataset, compared to the ground truth (Fig. 4b  upper panel, distance and displacement distributions).
Analysis of the confocal microscopy dataset We now solve the inference problem (  5 )-  (7)  on the confocal microscopy dataset to identify population-wide swimming model parameters. The inference process is assessed by comparing the descriptors obtained on trajectories predicted by the fitted model (Fig. 5a ) with descriptors of real trajectories (Fig. 2 ). The mean values of acceleration and speeds are accurately predicted for the three species (Figs. 5a, panels A and V , dashed lines). Relative positions of distance, displacement and visited area mean values are also correctly simulated (Figs. 2  and 5a , upper panel). B. sphaericus presents the lowest predicted accelerations and speeds while B. pumilus has the widest speed and acceleration distributions and B. cereus shows the highest accelerations, consistently with the data.
2  and 5a , lower panel). B. pumilus is the species that crosses the highest biofilm densities in the fitted model simulations, showing the highest speeds in this crowded areas, and that visits the most frequently areas with high density gradients, consistently with the data. As in the confocal images, the simulated B. sphaericus and B. cereus favor smoother zones of the biofilm with lower biofilm densities. The B. cereus fitted model correctly render the highest acceleration variance observed in the data for low biofilm gradients, while B. sphaericus speed and acceleration variance is the lowest for all ranges of biofilm densities and gradients, both in the data and in the fitted model predictions. The drop of speeds and accelerations for increasing biofilm densities and gradients is well predicted for B.
sphaericus accelerations, while B. cereus is more influenced by the random term. 1 ) with the inferred parameters and the trajectory descriptors introduced in section 2. 1 are computed and can be compared to the data descriptors in Fig. 2. Acceleration, speed, distance and displacement distributions are displayed in the upper panel, with quantiles 0. 05, 0. 5 and 0. 95 (plain lines) and mean (dashed line). The mean values observed in the image data are also displayed for comparison (black dashed line). Interactions between the host biofilm and, respectively, acceleration and speed distributions are displayed in the lower panel with isolines enclosing 5, 50 and 95% of the points, centered in the densest zones. (b) Inferred parameter posterior distributions after analysis of the confocal swimmer images, and posterior mean (dashed line).
Images were collected during 30 s (see 1 for sampling period). Bacterial swimmers navigate within a three-dimensional biofilm matrix and confocal microscope refreshment time is not small enough to allow 3D+T images. To limit 3D trajectories, a focal plane near the well bordure has been selected, where the well wall physically constrains the swimmer trajectories in one direction, which select longer trajectories in the 2D plane that can be tracked in time. Therefore, experimental data are composed of two-dimensional trajectories captured between the swimmer arrival and departure times in the focal plane, and the associated 2D+T biofilm density images that change over time due to swimmer action.
Post-processing of image data See Fig. 1  for a sketch of the datastream from microscope raw images to model inputs and Fig. A. 1 for data visualization at each step of the post-processing pipeline. Swimmer tracking has been applied on the red channel of the raw temporal stacks with IMARIS software (Oxford Instruments) using the tracking function after automated spots detection to get position time-series for each swimmer. Time-series with less than 8 time steps were filtered out. Then, swimmer speed and acceleration time-series were computed from their position by finite-difference approximations and trajectory descriptors were extracted. The RGB biofilm density temporal images were converted into grayscale and rescalled between 0 and 1 (linear scalling). Post-processed data are available at https://forgemia.
The parameter space [0, 1] 5 was uniformly sampled with n = 1, 000 points using the Fourier Amplitude Sensitivity Test (FAST) sampler of the SALib library i. e. the function SALib. sample. fast sampler. sample [10, 36]. We note that the interval [0, 1] covers a large parameter domain for some parameters, in particular β which remains small after inference. For this parameter, the sensitivity analysis will show potential impact on the output, that may be ineffective in the parameter range of the inferred model. For each point in the parameter space, a forward simulation is conducted on a population of swimmers on a representative biofilm extracted from the dataset (first batch of the B. pumilus dataset). Trajectory descriptors are then extracted and taken as observable of the sensitivity anaylsis that requires both the parameters sampling and the associated descriptors.
Inference validation on synthetic data Ground truth data construction Ground truth synthetic data (see section Assessment of the inference with synthetic data) were computed by solving eq. (  9 ) with γ = 10 s -1 , v 0 = 5 m. s -1 , v 1 = 1 m. s -1 , β = 10 m. s -2 , = 40 m. s -2 and biofilm maps taken from the first batch of the B. pumilus dataset. The number of swimmers was fixed to N = 50 and the number of time steps was taken identical to the experimental data i. e. N t = 224. Resulting mean speeds and accelerations were A ref = 68. 29 m. s -2 , V ref = 7. 47 m. s -1 and were used to rescale the data before inference together with the ground truth parameters (Table  2 ). In total, the acceleration dataset contains 9,523 samples for each spatial direction. Comparing ground truth data with the fitted model After inference, a new dataset is obtained by solving eq.
Inference validation on experimental data Comparing microscopy data with the fitted model The same procedure is repeated on the microscopy data: after inference, a new dataset is obtained by solving eq. (  9 ) with the fitted parameter, taking the same initial conditions for speeds and positions. Trajectories are stopped after the same number of time step as in the corresponding trajectory of the ground truth experimental dataset. Measuring the deterministic reconstruction The deterministic coefficient of determination R 2 det was computed to measure how much the dataset is explained by the deterministic part of the model. Setting A s,det i = f A (γ, v 0 , v 1 , β|yb, yV, yb, yGb, dt): R 2,s det = 1 -i (yA s i -A s,det i ) 2 i (yA s i -ȳ A s ) 2 where ȳ A s is the acceleration mean. R 2,s det is expected to tend towards 1 when the stochastic term η = N (0, ) becomes negligible with respect to A det.
Code availability All the image pre-and post-processing, calculations and statistics have been performed with custom scripts using the standard python libraries numpy  [17] , scipy  [40] , imageio  [21]  and pandas  [31]. The forward swimming problem computation is computed using customed scripts built upon numpy  [17]  and H5py (https://www. h5py. org). Sensitivity analysis has been conducted with the function SALib. analyze. fast. analyze of the SALib library  [10, 36]  (Sobol index), pcorr method of the pingouin library  [39]  (PCC). The Bayesian inference has been conducted using the STAN library  [38]  through its python interface pystan  [35]. All plots have been made with the matplotlib python library  [19]. The whole python code has been made available and accessible at the following git repository https://forgemia.
A. 7 Influence of inference and stochastic terms on the trajectory descriptors We wonder if the uncertainty sources involved in the inference process and in the stochastic term of the random walk have a decisive impact on the trajectory descriptors. To address this question, a first dataset is assembled by integrating in time Eq. (  2 ) for given parameters (see table  2 ), initial conditions and host biofilm. Then, this dataset is used as inputs of the inference method to infer the initial parameters (ground truth). Another dataset is produced by replacing the initial parameters by the inferred parameters. We note that we take the same seed for the random number generator than for the initial dataset, so that the only uncertainty that has been introduced until this step comes from the inference procedure.
Finally, we produce a last dataset by solving the model with the same inferred parameters as in the second dataset, but changing the seed of the random number generator. Hence, this last dataset involves uncertainties coming from the stochastic terms and from the inference process. This variation results in modifying the sampling of the stochastic terms and leads to strong modifications of the trajectories, like in We can see that the trajectory descriptor distributions are very similar across the different dataset, except for the total distance and the displacement where discrepancies can be noted. However, these differences are relatively small compared to the mean and the width of the distributions. We can also observe that the interactions with the underlying biofilm is very well conserved, even when the sampling of the stochastic term is very different.
Data collection We developed an online survey to investigate ECR experiences and perceptions of engaging in SPIs. In this study, we considered different types of SPIs at various levels of social organisation representing different roles and positions researchers adopt: from global platforms such as IPBES to the less formal? interactions between researchers and policy-makers, such as developing collaborative projects or discussions with local government officials (as communicated to survey respondents during survey deployment -see Supplementary materials p. 3). As previously articulated, there are several different definitions for ECRs. For the purpose of this study, we used the following definition: 'a person with five or fewer years of research experience after their highest completed degree (MSc or PhD), researching biodiversity and/or ES'.
A revised version was developed and was then uploaded to the KoBo Toolbox platform (https://www. kobotoolbox. org/). The online survey was open for data collection from 16 January until 1 April 2020. Respondents were recruited through mailing lists of international networks for ECRs researching biodiversity and ES, such as Young Ecosystem Services Specialists (YESS) network, IPBES Fellows and the Future Earth ECR Network of Networks. Respondents were also encouraged to share the survey with their peers and professional networks. These activities were supplemented with recruitment posts to social media channels (Twitter and Facebook) by the research team, which has been shown to be helpful in leveraging contacts in snowball sampling  (Kirchherr and Charles 2018). A call for survey participants was also featured in the Ecosystem Services Partnership (ESP) newsletter.
For example, boundary organizations and stakeholder groups like Open Network of IPBES Stakeholders (ONet) could host short online workshops for ECRs to complement the IPBES fellows programme. Young educators could embrace IPBES assessments, Sustainable Development Goals Reports (or similar sciencepolicy works) as teaching tools to build interdisciplinarity capacities and science-policy training. A key area of identified future work is in extending this survey to provide a more representative global view. This could be achieved through, for example, 1) the translation of the survey into a range of other formats to ensure accessibility in languages aside from English and optimisation for ECRs using a range of assistive technologies including dictation, text to speech, etc. , and/or 2) promoting wider distribution through a range of global networks and a longer data collection period.
Future work could focus on understanding the relationships between the participation of ECRs in SPIs, academic disciplinary background, and the degree to which ECRs participate in inter-and trans-disciplinary work. It could also seek to understand in more detail the impacts of various economic, practical, demographic factors determining participation in different global contexts (e. g. income, age, gender, etc. ). Case studybased work exploring motivations, barriers, opportunities in those who already engage in SPI work, such as current and former IPBES Fellows or YESS delegations to IPBES, could be a route to increasing the depth and nuance of these insights. Furthermore, comparison of our findings with those from other initiatives (for example, the IPBES Stakeholder survey data that is to be published on Zenodo) could generate useful insights and serve as inspiration for future investigations.
Acknowledgements The authors would like to thank survey participants for their time and input. We also appreciate all efforts in helping us distribute the survey, especially by the leadership of YESS, IPBES fellows,  ESP  networks (especially Iskra Konovska). This paper benefited from the insightful comments of two anonymous reviewers and from discussions around the paper topic as part of the IPBES-8 stakeholder days, for which we extend our thanks to IPBES, ONet and all participants. The authors would like to thank the  Snow Leopard Trust ,  UNDP  and  GEF  for providing funding that partially covered costs of authors attending the 7th IPBES Plenary in 2019, where this research has been developed.
Using model-observation studies to quantify iron cycle mechanisms Combining global ocean biogeochemical modelling experiments with GEOTRACES datasets have played a key role in identifying significant processes shaping the ocean iron cycle and their wider impacts. However, their results may be compromised when the model resolution is insufficient for the system of interest. Our results show that only model experiments at the 10s of km horizontal resolution can accurately represent the dispersal of DFe from the TAG site on the mid-Atlantic ridge, far exceeding the typical resolution of global ocean biogeochemical models (100-500km). This is due to the complex topography of the axial valley setting of the TAG site that is not resolved in global ocean biogeochemical models. Where ridge topography is less variable, for instance at faster ridge spreading sites with shallower axial valleys or troughs, e.
Acknowledgements This work is part of the United Kingdom GEOTRACES section GA13 which was supported by the  Natural Environment Research Council (NERC)  Grants  NE/N009525/1  to AT and  NE/N010396/1  to MCL. We thank the  Captain  and crew of the  RRS James Cook  and everyone that contributed to the GA13 sampling effort on board. JG acknowledges support from the  French National Agency for Research (ANR)  through the project  DEEPER  ( ANR-19-CE01-0002-01 ), and  PRACE  and  GENCI  for awarding access to HPC resources  Joliot-Curie Rome  and  SKL  from  GENCI-TGCC  (grants  2020-A0090112051 ,  2019gch0401 , and  PRACE  project  2018194735 ) and  HPC facilities DATARMOR of "Pôle de Calcul Intensif pour la Mer" at Ifremer Brest France. The authors thank  Gildas Cambon  and  Sébastien Theetten  for their contribution in the development of the GIGATL3 simulation.
Data availability statement: CROCO ocean model is available at https://www. croco-ocean. org. Information about the GIGATL6 simulation can be found at https://doi. org/10. 5281/zenodo. 4948523 The Lagrangian software Pyticles is available at https://github. com/Mesharou/Pyticles and has been archived on Zenodo at https://doi. org/10. 5281/zenodo. 4973786. The data from the GA13 transect is available (to view and download) as part of the GEOTRACES intermediate data product 2021 which can be accessed online via https://www. geotraces. org/geotraces-intermediate-data-product-2021/ 7. References 19448007, ja, Downloaded from https://agupubs. onlinelibrary. wiley. com/doi/10. 1029/2022GL100615 by University of California -Los Ange, Wiley Online Library on [09/11/2022]. See the Terms and Conditions (https://onlinelibrary.
The Antarctic marine environment, although rich in life, is predicted to experience rapid and significant effects from climate change. Despite a revolution in the approaches used to document biodiversity, less than one percent of Antarctic marine invertebrates are represented by DNA barcodes and we are at risk of losing biodiversity before discovery. The ease of sequencing mitochondrial DNA barcodes has promoted this relatively 'universal' species identification system across most metazoan phyla and barcode datasets are currently readily used for exploring questions of species-level taxonomy. Here we present the most well-sampled phylogeny of the direct-developing, Southern Ocean nudibranch mollusc, Doris kerguelenensis to date. This study sampled over 1000 new Doris kerguelenensis specimens spanning the Southern Ocean and sequenced the mitochondrial COI gene.
2010). Parts of the Antarctic Peninsula, including the west Antarctic Peninsula for example, are particularly vulnerable to these human-induced ecological disasters, as these are already experiencing the greatest increases in mean annual atmospheric temperatures on Earth  (Chapman and Walsh 2007; Clarke et al. 2007; Ingels et al. 2012; Torre et al. 2017). Failure to address these global threats will not only result in the degradation of Antarctic marine ecosystems but will potentially lead to enormous losses of global biodiversity. Technical advances in molecular phylogenetics over the past several decades have resulted in the development of many more sensitive rapid tools for detecting new species  (Féral 2002; Goetze 2003; Baird et al. 2011). The ease of sequencing mitochondrial DNA barcodes has promoted a relatively 'universal' species identification system across most metazoan phyla and now barcode datasets are more readily used for exploring questions of species-level taxonomy  (Hebert et al.
2003; Puillandre et al. 2012; Taberlet et al. 2012; Eberle et al. 2020). In current phylogenetic studies the types of molecular information extracted for rapid assessments of animal diversity have evolved from interpreting single-locus (e. g. Hebert et al. 2003)  to whole-genome datasets (e. g. Jensen et al. 2021). This expansion of available sequences, facilitated by the automation of sequencing and decrease in sequencing costs, has accelerated phylogenetic studies and whole-genome-based research (e. g. Johnson et al. 2008; Layton et al. 2018; Cai et al. 2019). This is particularly significant when barcoding cryptic and pseudocryptic species (organisms that are first discerned by non-morphological methods, e. g. Brasier et al. 2016; Matsuda and Gosliner 2018; Tyagi et al. 2019). When divergent interspecific traits are not morphologically obvious, traditional taxonomic methods fail to detect speciation events and biodiversity will remain under-reported  (Knowlton 1993; Baird et al.
Tissue subsamples were taken from specimens preserved in 96-100% ethanol or frozen. Sequenced specimens were housed in the Western Australian Museum (WAM), Scripps Institution of Oceanography, Benthic Invertebrates Collection (SIO-BIC), Yale Peabody Museum (YPM), California Academy of  Sciences (CAS), National Museum of Natural History, Smithsonian Institution (USNM), University of Barcelona and the Baker Laboratory, University of South Florida. Data for all specimens sequenced in this study are available in Supplementary Table  S1  including GenBank accession numbers for outgroup taxa. In addition to the ingroup specimens, a range of outgroup sequences were selected from GenBank  (Wollscheid-Lengeling et al. 2001; Shields 2009; Pola and Gosliner 2010; Jung et al. 2014; Palomar et al. 2014; Hulett et al.
8 μL of forward primer, 0. 8 μL of reverse primer, 0. 2 μL of Platinum Taq polymerase (Invitrogen), with 1. 5 μL of DNA template added. The thermocycling protocol for COI using primers LCO1490/HCO2198 followed the procedure: 5 min. at 95°C, 7 cycles 30 s at 95°C, 30 s at 45°C, 1 min. at 72°C followed by 35 cycles of 30 s at 95°C, 30 s at 50°C, 1 min. at 72°C with a final extension time of 10 min. with a temperature of 72°C. The thermocycling protocol using primers jgLCO1490/jgHCO2198 was as follows: 3 min. at 95°C, 8 cycles of 30 s at 95°C, 30 s at 50°C, 45 s at 72°C followed by 32 cycles of 30 s at 95°C, 30 s at 48°C and 45 s at 72°C with a final extension time of 5 min at 72°C. Amplicons were screened on E-gels (Invitrogen) and the positive reactions, determined through Bio-Rad Image Laboratory software, were outsourced to the Australian Genome Research Falkland Islands FI 1 1 1 Bouvet Island BI 1 1 1 Mount Siple SIP 1 1 1 Kerguelen Plateau KP 2 1 18 Casey Station CS 1 1 2 Ordered by total number of species per region.
Pairwise distances (uncorrected p-distances) or Tamura and Nei (1993) (TN93) corrected genetic distances were both calculated from the mtDNA sequences using the 'dist. dna' function from the R. cran (ver. 4. 2. 0, R Foundation for Statistical Computing, Vienna, Austria, see https://cran. r-project. org/) package Ape (ver. 5. 6, see https://cran. r-project. org/web/ packages/ape/index. html; Paradis and Schliep 2019) with either 'raw' or 'TN93' selected as the evolutionary model (Supplementary Fig. S2 ). The gamma shape α and base frequencies were also parameters specified for the distance calculations. Intra-and interspecific (PSH clades) p-distances and TN93 corrected genetic distances were also calculated in MEGA X (ver. 10. 2. 6, see https://www. megasoftware. net/; Kumar et al. 2018 ) (Supplementary Table  S2 ).