dataesr/scientific-paragraphs-categorization huggingface dataset:
Our model rapidly detects images with snow, but also those with sunshine or hail. Unlike previous models, ours is shown to perform well on images from novel camera placements and even a distant site in Svalbard. Above all, we present a dataset and methods to train future models that will be even more transferable. Future work should focus on the assembly of larger training datasets, representing a greater diversity of backgrounds, conditions, and wildlife camera domains. There is potential for deep learning models to extract hydrological information from images, for example rainfall or even rare cryogenic processes  (Grab et al. , 2021). Furthermore, models could be trained to extract meteorological by-catch from phenocams for vegetation  (Brown et al. , 2016)  or camera traps for large animals  (Hofmeester et al.
, 2020). A particularly exciting prospect would be to extract latent meteorological information from existing wildlife camera network datasets, containing millions of labelled organisms  (Norouzzadeh et al. , 2018). This could provide new insights into the occurrence and phenology of a huge variety of animal and plant species worldwide. Foundation (grant no. 20BD21_193809), the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning and the German Research Foundation.
Acknowledgements This research was funded through the  2019-2020 BiodivERsA joint  call for research proposals, under the  BiodivClim ERA-Net COFUND programme , with the funding organizations  Innovation Fund Denmark  (grant no. 0156-00022B ), the  Department of Science and Innovation Republic of South Africa  (grant no. DSI/CON 0000/2021 ), the  Research Council of Norway , the  Swiss National Science.
, 2023). Our analyses focused on four habitats that have extensive long-term monitoring data: rocky intertidal, kelp forest, shallow reef, and deep reef (Figure  1A ; Figure  S1 , Supplementary Appendix Figures). Across the four habitats included in our analyses, three organismal groups were sampled: macroalgae (rocky intertidal and kelp forest), conspicuous mobile and sessile invertebrates (rocky intertidal and kelp forest), and fishes (kelp forest, shallow reef, and deep reef). We focused our community structure analyses on MPAs located along the temperate  1A ) because this area was the most comprehensively sampled region of the MPA network and had sufficient pre-marine heatwave data to evaluate baseline community structure (Figure  S1 , Supplementary Appendix Figures). To evaluate MPA features as potential drivers of ecological resilience, we included MPAs across all of California for two of the habitats (rocky intertidal and kelp forest), where more extensive spatial and temporal coverage exists (see section 2.
6). F o r R e v i e w O n l y Central Coast of California (Figure Two general types of data were used in our analyses: species counts (kelp forest, shallow reef, deep reef), and the proportional cover of invertebrates and macroalgae (rocky intertidal and kelp forest invertebrates and algae). While biomass is often used as a common measurable response across taxa  (Duffy et al. , 2017) , it is not suitable for our analyses because of the large number of taxa for which it is difficult to accurately measure biomass (e. g. , macroalgae and invertebrates). Therefore, we elected to use a taxonomic abundance-based approach to compare changes in community structure. All analyses were conducted using published data for each habitat  (Brooks et al. , 2022; Cieri et al. , 2022; Malone et al. , 2022; MARINe et al.
To test these hypotheses, a single PERMANOVA was performed for each habitat and site type (inside or outside a MPA) using pairwise comparisons between marine heatwave periods (before vs. during, before vs. after) using the pairwiseAdonis wrapper function  (Martinez Arbizu, 2019)  in the vegan package  (Oksanen et al. , 2022)  in R (R Core Team, 2021).
g. , high resistance is indicated by a smaller change in multivariate distance-based centroids). Recovery was evaluated by calculating the multivariate distance between the before (2007-2013) and after period (2017-2020) centroids. We calculated the change in multivariate distance between centroids using the betadisper function in the vegan package  (Oksanen et al. , 2022). The betadisper function returns the principal coordinates of centroids, which we used to calculate multivariate distance after ensuring positive-definite eigenvalues. Finally, statistical significance was tested using a PERMANOVA on community structure resistance (before vs. during) and recovery (before vs. after). To explore whether the timing of community shifts coincided with temporal changes in oceanographic variables, we used a Granger causality test on Bray-Curtis dissimilarity.
Species associated with community changes To evaluate which species were associated with community change, we used two approaches. First, we used a Similarity Percentage Analysis (SIMPER) to decompose community structure using Bray-Curtis dissimilarity and to examine the percent contribution of each species to the before versus after marine heatwave communities. However, because SIMPER is known to confound the mean between group variation and dispersion  (Warton et al. , 2012) , we cross-validated the SIMPER output using the mvabund package  (Wang et al. , 2022)  in R. The mvabund package uses fitted generalized linear models to account for nonlinear mean-to-variance relationships of each species. Both of these analyses were conducted on the absolute abundance of species (rather than relative abundance) because they are not sensitive to common or rare taxa.
Baseline averages were calculated through the end of 2012 to accommodate a buffer before the onset of the marine heatwave in 2014. For MOCI, we calculated the annual mean  at each site as a standard index. To visualize and pair the environmental data with the long-term biological monitoring data, we calculated the mean anomalies across all calendar months for each year (Figure  1B-F ). Finally, to determine whether observed shifts in community structure were associated with changes in oceanographic conditions, we used the envfit function in the vegan package  (Oksanen et al. , 2022)  to overlay the four environmental variables as vectors on the ordinated community data. The envfit function computes the rankbased correlation coefficient and significance is evaluated through permutation-based resampling.
We used a trait-based fourth-corner model to evaluate effects of environmental variables and species thermal affinities on the relative abundance of all recorded taxa. Species thermal affinities were obtained from literature reported classifications and expert judgment (Supplementary Appendix, Methods). The fourth-corner model relates the interaction between the environment and species traits (thermal affinities) on variation in the abundance of taxa  (Brown et al. , 2014). Briefly, we used the mvabund package to fit simultaneous generalized linear models for counts of all species at each site as a function of the environmental and F o r R e v i e w O n l y oceanographic conditions (AT, SST, SBT, BEUTI, MOCI), the relative representation of thermal affinities, and their interaction.
Drivers of resistance and recovery The ecological and design context of California's network is well documented, allowing us to explore whether certain MPA features conferred community structure resilience to the marine heatwave. These analyses were spatially expanded to include a subset of MPAs covering the entire network from northern to southern California, thereby providing a greater scope of MPA feature diversity (Figure  S3 , Supplementary Appendix Figures). However, only the kelp forest and rocky intertidal habitats contained sufficient pre-heatwave monitoring data with consistent annual surveys in all bioregions of the state to appropriately examine drivers of ecological resilience among MPAs at the network level (Figure  1A ; Figure  S1 , Supplementary Appendix Figures). MPA features included habitat richness and habitat diversity (calculated using Shannon-Wiener on depth-stratified hard and soft bottom, proportion of rock, and the extent of kelp forest canopy, rocky intertidal, sandy beach, coastal marsh, tidal flats, and armored shore), historic fishing pressure, MPA age and size, connectivity, and kelp forest fish biomass response ratios (as a proxy for MPA ecological performance, see Supplementary Appendix Methods).
Statistical analysis Growth analysis (based on SL) was performed in 121 larvae between 5 and 30 dph. Growth rate (mm day -1 ) was calculated from length-at-age and determined and compared using the nonparametric Siegel's repeated medians estimator for slopes regressions and a 95% confidence interval (CI) was calculated by applying the "confint. mblm" function of the "mblm" R package to each of the four regressions. A Kruskal-Wallis-Test was applied to the full sample set (106 individual fish) and treatment differences tested by a post-hoc Dunn test with Bonferroni adjustment. The relationship between SL and OR was regressed with a power function using a non-linear least square method. SL and OR data were then log-transformed and residuals measured as proxies of relative otolith size index (ROSI)  34.
Acknowledgements The authors would like to thank the  Live Prey and Aquaculture departments of IMARPE  and the members of the CUSCO project for their technical assistance during the rearing experiments. We would also like to thank  Dr Rolf Koppelmann ,  Dr Catriona Clemmesen  and  Dr. Bieito Fernández Castro  for scientific discussions that improved this study. Funding for C. O. , M. M. , M. S. and M. A. P. was received by the  German BMBF project CUSCO  (FKZ:  03F0813B ). F. R. was funded by the  Institut de Recherche pour le Développement (LMI-DISCOH  and  JEAI DYSRUP ), A. A. V. from  PpR de Acuicultura of IMARPE  and F. M. from the  BiodivErSA and Belmont Forum  project  SOMBEE  ( BiodivScen ERA-Net COFUND program ,  ANR  contract  n°ANR-18-EBI4-0003-01 ). We acknowledge financial support from the  Open Access Publication Fund of Universität Hamburg.
For consistency with the Core Argo mission, Deep Argo floats operate on a nominal 10-day cycle between the surface and the seafloor (4000-m or 6000-m depth, depending on the float model). For Deep Argo floats profiling on descent and ascent, measurements on ascent should be collected at least in the upper 1000 m to provide near real-time data to ocean forecasting and seasonal prediction communities. Required accuracies for Deep Argo salinity, temperature, and pressure measurements are to approach ±0. 002 PSS-78, ± 0. 001°C, and ±3 dbar respectively  (Roemmich et al. , 2019a) , in line with shipboard measurements from the Global Ocean Ship-Based Hydrographic Investigations Program (GOSHIP;  Katsumata et al. , 2022). It is recommended that technology development continues to reach targeted values.
With salinity accuracy target five times more stringent than Core (0-2000 m) Argo  (Wong et al. , 2020) , Deep Argo can efficiently supplement the high-quality shipboard reference database used for Core Argo quality control and has therefore the capacity to improve the post-calibration of salinity measurements. The accuracy, precision, and vertical resolution of the Deep Argo CTD sensor influence the temperature-salinity relation, and estimates of steric height and vertical stratification as outlined below. Steric height is vital for sea level budget analyses  (Cazenave et al. , 2019)  as well as geostrophic circulation studies  (Zilberman et al. , 2020). Errors the size of Deep Argo target accuracies ( ± 0. 002 PSS-78, ± 0. 001°C, and ±3 dbar) applied to full-depth (0-6000 m) profiles at 20°S in the Brazil Basin result in sea level errors of about ±0.
Value of Deep Argo data The amount of deep-ocean profiles collected from Deep Argo floats in the South Australian, Australian Antarctic, Southwest Pacific, and Brazil basins in less than 7 years approaches that of deep-ocean data accumulated from ships over the past 70 years (Figure  2 ). This new dataset has enabled breakthroughs in our understanding of OHC, ocean circulation, and sea level change. For instance, strong decadal-scale warming rates of the cold and dense Antarctic Bottom Water (AABW) that spreads over much of the abyssal ocean (depth > 4000 m) have been quantified combining Deep Argo data with historical data in the Brazil Basin  (Johnson et al. , 2020) , and Argentine Basin  (Johnson, 2022)  with trend estimates that are up-to-date and several times more certain than those from repeat hydrography alone (Figures  3A, B ).
04 to ±0. 006 W m -2  (Meyssignac et al. , 2019)  and deep-ocean thermal expansion from ±0. 73 to ±0. 1 mm decade -1  (Johnson et al. , 2015; Desbruyères et al. , 2016). As the array expands, Deep Argo will become increasingly useful to improve knowledge of the B A FIGURE 2 The sampling density per 5°latitude by 5°longitude of temperature and salinity profiles deeper than 2000 m. (A) Deep Argo profiles in regional pilot arrays deployed between 2014 and 2023. (B) Historical non-Argo profiles from the World Ocean Database (WOD) 2018 collected between 1950 and 2023  (Garcia et al. , 2019). regional-to-basin-scale structure and subseasonal-to-interannual fluctuations of the large-scale deep-ocean circulation, and advance understanding of the drivers for these changes. In particular, Deep Argo will provide a new means to validate numerical model projections of the impact of meltwater release from Greenland and Antarctica on the deep-ocean overturning circulation  (Swingedouw et al.
Deep Argo floats deployed in these regions may park at depths greater than 1000 m to minimize float dispersion, hence optimizing their value. For instance, programming parking depth to coincide with reduced background flow such as within 500-m above the seafloor in abyssal plains, and above deep boundary currents near topography, has proven an effective way to limit horizontal displacement during parking  (Zilberman et al. , 2020). Floats may also be programmed to park at the core depth of local deep water masses in order to assess regional circulation  (Racapéet al. , 2019). Once a global array is achieved, it is recommended that all Deep Argo floats should park at 1000 m in order to facilitate homogeneous float sampling  (Roemmich et al. , 2019a)  and increase the velocity database from Argo float trajectories to reduce sampling error  (Zilberman et al.
Ongoing evolution of the Deep Argo array Sensors to measure ocean properties additional to temperature, salinity, and pressure are under testing to assess their performance in the field and increase value of the Deep Argo data set. Any additional sensors should be compatible with the Deep Argo float longevity target and should not impede the achievement of a global Deep Argo array. The opportunistic measurement of bathymetry from Deep Argo floats, which does not require supplementary sensor measurements, is under evaluation.
Deep Argo will supplement DO data from repeat shipboard hydrography  (Testor et al. , 2018) , and in return, shipboard observations will help validate and calibrate Deep Argo profiles  (Racapéet al. , 2019). It is expected that Deep Argo floats with DO sensors could play a key role in the expansion of the Global Ocean Oxygen Database and Atlas  (Greǵoire et al, 2021).
Bathymetry Ocean bathymetry has a fundamental impact on the pathway and strength of ocean currents  (Wilson et al. , 2022) , and amplitude of ocean mixing  (Mashayek et al. , 2017) , and can influence heat and freshwater exchange between the ocean and atmosphere  (de Boer et al. , 2022). Variations in the shape of the seafloor can dictate the magnitude and frequency of earthquakes  (Passarelli et al. , 2022)  and the generation and propagation of tsunamis  (Salaree and Okal, 2020). Less than 24% of the seafloor topography from the General Bathymetric Chart of the Ocean (GEBCO) dataset is constrained by shipboard sounding measurements or other direct measurements, and is limited mostly to Exclusive Economic Zones  (Tozer et al. , 2019; Wölfl et al. , 2019; GEBCO Bathymetric Compilation Group, 2022).
Preliminary comparisons between historical Argo and GEBCO bathymetry sourced from echo sounding measurements show encouraging agreement  (van Wijk et al. , 2022). Based on the averaged Deep Argo float grounding rate of 60% over the past 4 years, a 1200-float array could contribute over 25,000 additional deep-ocean bathymetry measurements every year to global bathymetry datasets such as the Nippon Foundation -GEBCO Seabed 2030 Project  (Mayer et al. , 2018).
Acknowledgments Argo data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it. (https://argo. ucsd. edu, https://www. oceanops. org). The Argo Programme is part of the  Global Ocean Observing System. Thanks to  Pauline Weatherall ,  Thierry Schmitt ,  Kevin Mackay ,  Brook Tozer , and  David Sandwell  for providing helpful insight on the GEBCO bathymetric dataset.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors gratefully acknowledge financial support by the following projects and grants: NZ was supported by  NOPP  ( NOAA  grant  NA18OAR0110434 ) and  NSF  ( OCE-2242742 ). NZ, SP, DR and MS received support from  NOAA Global Ocean Monitoring and Observing Program  through Award  NA20OAR4320278. GJ was supported by the  NOAA Global Ocean Monitoring and Observation Program  and  NOAA Research. VT, XA, KB, CC and DD were supported by the  Ifremer PIANO  project and by the  Equipex+ Argo-2030  project that received support from the  French government  within the framework of the " Investissements d'avenir" program integrated in France 2030  and managed by the  Agence Nationale de la Recherche (ANR ; grant  ANR-21-ESRE-0019 ).
vocabularies help improve metadata interpretation and data findability by eliminating spelling variations, synonyms, and other forms of variability. Additionally, controlled vocabularies help facilitate metadata interoperability between different systems, making it easier to exchange and integrate data between different organizations and platforms. Data citation involves referencing a dataset for the purpose of attributing credit and facilitating access  (TGDCSP 2013). It not only enables data users to acknowledge and give credit to the producers of a dataset used in a research study or project, but also allows readers to access and use the dataset for additional research. Data citation plays an important role in promoting scientific reproducibility, and accountability, and facilitating data sharing and reuse.
Profile The collection of discrete water samples from the ocean at specific locations and depths, using sampling bottles (e. g. , Niskin). The samples are then analyzed in a laboratory to determine various oceanographic parameters, such as dissolved inorganic carbon, total alkalinity, dissolved oxygen, and nutrient levels. It can also refer to continuous measurements using autonomous sensors mounted on a CTD rosette. Column headers for profile data 1 Data file example 2  Jiang et al. (2022)  Surface underway Continuous measurements of oceanographic variables at the ocean surface using sensors, often in flow-through systems onboard research vessels or ships of opportunity (SOOP), to obtain real-time information about the ocean's physical and chemical conditions, such as temperature, salinity, and fCO2.
It emphasizes the experimental setup while allowing users to document their own response variables. For biological variables, it is important to state the taxonomy (a taxon or a community), upon which the variable is studied. For example, if the growth rate of a certain species of salmon is studied. The "variable/parameter" is growth rate, and "biological subject" is that species of salmon. One could group/capture organismal data in three forms: taxonomic, functional, and phylogenetic. It is recommended to use the species reference databases from the Catalogue of Life (https://www. catalogueoflife. org/), Integrated Taxonomic Information System (or ITIS, https://www. itis. gov/index. html), World Register of Marine Species (or WoRMS, http://marinespecies. org/), or Paleobiology Database (PBDB, https://paleobiodb.
org/classic/). For life stages, consider using an existing controlled vocabulary like https://vocab. nerc. ac. uk/collection/S11/current/. Model outputs often involve extensive data volumes, reaching gigabytes or even terabytes, making it necessary to address standards for the operational provision of model data (i. e. , making data available for weeks to years), separately from longterm or permanent archiving. The operational provision of model output data typically relies on three integrated standards: network Common Data Form (netCDF) files, the Climate and Forecast (CF) metadata conventions, and the Open source Project for a Network Data Access Protocol (OPeNDAP) libraries for remote data access. NetCDF is an open-source software that was developed and supported by UCAR's Unidata program (http://www.
unidata. ucar. edu/) since 1989. NetCDF enables the creation and dissemination of self-contained data files with metadata, using formats that are independent of any specific machine or system. It has long been a standard for generation of model outputs and climatological data products in the ocean and climate modelling communities. The CF metadata conventions provide guidelines for encoding datasets in netCDF, specifying the reporting of space and time coordinates, units, variable names, and other relevant information  (Hassell et al. , 2017). CF-compliant netCDF files are advantageous due to their self-describing nature, eliminating the need for additional information to interpret their contents. CF is a living and open standard that encourages community participation in proposing enhancements and reporting issues (https://www.
Information specific to OAE studies is indicated in italic font. Refer to Chapter 4. 6 for more context. The netCDF-CF-OPeNDAP standard enables provision of model outputs in accordance with the FAIR principles  (Wilkinson et al. , 2016) , provided a few conditions are met. NetCDF-CF-OPeNDAP datasets can be Findable because machine-readable metadata enable automatic discovery, Accessible because of the standardized communications protocol that is open and universally implementable, Interoperable because of the standardized, machine-readable metadata and data and the ability to subset and aggregate datasets, and Reusable because rich metadata using standardized naming conventions can be provided. The necessary conditions for a netCDF-CF-OPeNDAP dataset to qualify as FAIR are that (a) it is openly available and has a globally unique and persistent identifier (e.
g. , a digital object identifier, or DOI), (b) data and metadata are registered and indexed in a searchable resource, and (c) data are described with rich metadata that include accurate and relevant attributes and remain accessible even if the data are no longer available. Box 1 lists attributes that should be included in netCDF files generated out of a biogeochemical ocean model, including several that are specific to OAE research, for the output to be considered a richly documented dataset. Output from an Earth System model would have slightly different requirements regarding the atmosphere (e. g. , atmospheric forcing would not apply). The discussion thus far has focussed on the operational provision of model outputs, i. e. , comprehensive datasets that may be available for periods of weeks to years.