dataesr/scientific-paragraphs-categorization huggingface dataset:
Birds were captured at their nest while incubating eggs or guarding chicks. Albatrosses were equipped with either a 6 g Lotek LTD 2400 (Midway) or 3. 6 g Lotek LAT2500 (Tern) geolocation archival data logger (Lotek Wireless, St. John's, Newfoundland). These devices measure changes in ambient light levels to estimate sunrise, sunset, and day length, and by evaluating these metrics against an internal clock, are able to estimate latitude and longitude  (DeLong et al. , 1992; Hill and Braun, 2001; Lisovski et al. , 2012). Loggers were mounted on plastic identification bands placed around each bird's tarsus using ultra-violet resistant cable ties and quick-setting epoxy. The total auxiliary package weighed <2% of bird body mass, the recommended maximum device weight of 3%  (Phillips et al. , 2003).
Estimating bird locations from light-level geolocation data Global location sensing logger data were processed and analyzed in R v. 3. 6. 2 (R Core Development Team, 2019). Given the high variation in location estimates from light-based geolocation, especially around seasonal equinox periods  (Wilson et al. , 1992; Welch and Eveson, 1999; Phillips R. et al. , 2004; Shaffer et al. , 2005; Hinke et al. , 2015) , locations were re-estimated using the logged sunrise and sunset times using the R package "probGLS"  (Merkel et al. , 2016)  which shows low spatial error (approx. 33% less error) relative to other GLS movement analysis packages  (Halpin et al. , 2021). This iterative forward step selection framework estimates positions from sunrise and sunset times using threshold techniques and generates a cloud of possible estimates for each location (n = 800, iterated 60 times forming weighted particles).
These estimated positions were weighted by daily median SST recorded by the geolocators and compared to temporally matched remotely sensed high resolution SST data from NOAA. 1  Estimated locations were further evaluated using a speed filter for periods when an individual was in flight or resting on the sea surface from the wet/dry sensor data. Wet/ dry immersion data were available for all years except for 2009 at Tern. For these data, the logger was assumed to be dry for the entire period, potentially allowing for more dispersed distributions of animals tracked in this year. For both species, travel speeds lower than 10 km h -1 were assumed to be stationary  (Antolos et al. , 2017)  compared to mean travel speeds in flight of 35-40 km h -1 , though maximum travel speeds can be much higher than these mean values  (Hyrenbach et al.
Correlated random walks are simulated tracks that originate from the same starting locations as the true data and then allow for random walk trajectories from that origin, with the trajectories being determined by selected distributions for distance and turning angle. Here, we simulated subsequent points by randomly drawing a distance and angle independently from distributions of travel distances and turning angles fit using the initial data  (Hazen et al. , 2017 (Hazen et al. , , 2021)). The package adehabitatLT was used to determine turning angles and distances between track points present in the data. Distributions were fit by examining Cullen and Frey graphs to select candidate distributions and subsequently testing goodness of fit  (Cullen et al. , 1999). A log-normal distribution was the best fit for each category of species and colony, aligning with previous findings in animal movement studies  (Limpert et al.
Comparisons of overlap in post-breeding distributions We produced Kernel Density Estimates (KDEs) of postbreeding distributions to calculate overlap metrics between species and colonies. Post-breeding position estimates were projected using a Lambert Cylindrical Equal Area projection  (Wood et al. , 2000) , and KDEs were generated using the adehabitatHR package  (Calenge, 2006)  to determine utilization distributions following methods described by  Kappes et al. (2015). We analyzed KDEs at a 300-km resolution following the findings of a double-tagging study by  Halpin et al. (2021)   which showed that the error associated with GLS loggers was approximately 300 km. A fixed kernel approach was used, which incorporated a bivariate normal kernel  (Worton, 1989)  and several estimates for the bandwidth h were explored, including the (1) reference bandwidth for standard bivariate normal distributions, (2) least-squares cross-validation (LSCV) derived bandwidth, (3) exact mean integrated square error bandwidth, and (4) bandwidth derived using a direct plug-in selector.
Henry et al. (2021)  found that SST, wind, and chlorophyll-a concentrations were the most important habitat predictors for Laysan albatross, and therefore we used these three variables to compare environmental characteristics of Laysan and blackfooted albatross post-breeding habitat. We used monthly composites of Aqua MODIS Chlorophyll-a ('Chl-a'; mg m -3 ), with a 4-km spatial resolution, wind speed (m s -1 , hourly) from the MERRA-2 reanalysis product with a 0. 125° spatial resolution, and SST recorded in situ on GLS loggers (°C) from the post-breeding period. We used the monthly composite for Chl-a as higher temporal resolutions did not provide sufficient spatial coverage due to cloud cover. Wind speed was calculated from u and v components of the 10-m height surface model, and the hourly data from the MERRA-2 reanalysis was averaged into daily composites to match the temporal resolution of processed GLS data.
53 days). Laysan albatross from Tern showed the highest variability in post-breeding trip length, principally due to high variability in departure dates. Across 5 years of observations, 13 Laysan albatross had failed breeding attempts (Midway = 5, Tern = 8) and five black-footed albatross had failed breeding attempts (Midway = 2, Tern = 3). Notably, half of failed breeding attempts for Laysan at Tern occurred in 2011. We identified one skip-breeder across our sample size of 143 individuals (a Laysan albatross at Midway which skipped the 2010-2011 breeding season). Both Laysan and black-footed albatross generally showed broad post-breeding distributions (Figure  1 ) and significant at-sea segregation of both foraging and focal areas (Table  4 ). The focal area for Laysan albatross, when data from both the Tern and Midway colonies were aggregated, was distributed within the Subarctic Gyre in the Oyashio Current and bordered the northern front of the North Pacific Transition Zone (NPTZ).
Segregation between foraging areas of Laysan albatross breeding at Midway and Tern was low (Figures  2A, C ; UDOI = 0. 779), while segregation of focal areas was high (UDOI = 0. 057), and significant segregation was not detected in either case (Table  4 ). Foraging areas of Laysan albatross breeding at Tern and Midway both included broad regions of the central and western North Pacific. However, subtle differences in Laysan focal habitat use occurred between the two colonies, despite the presence of considerable overlap in both foraging and focal areas when data from throughout the post-breeding period were examined. High densities of Laysan albatross breeding at Midway were observed on the northwestern Pacific eastward of the Kuril Islands, whereas densities of Laysan albatross breeding at Tern were highest toward the central Aleutian Islands and central areas of the Bering Sea.
Acknowledgments We thank  Ellie Heywood  for her input and help with spatial analyses in R, and for assistance with coding. We would like to acknowledge and thank  Scott Seganti ,  Sarah Youngren ,  Ruth Brown ,  Dan Rapp ,  Caitlin Kroeger , and  Abram Fleishman  for their field work contributions at  Tern Island  and  Sarah Gutowsky  for her field work at Midway Atoll. 10. 3389/fevo. 2022. 1028317Frontiers in Ecology and Evolution 18 frontiersin. org.
Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: BirdLife International (http://www. seabirdtracking. org), under dataset ID numbers  1927ID numbers   , 1928ID numbers   , 1929ID numbers   , and 1930. Naval Research (N00014-00-1-0880 and N00014-03-1-0651). All protocols employed in this study were approved by the Institutional Animal Care and Use Committees at UCSC.
Performance assessment in deep geological nuclear waste repository systems necessitates an extended knowledge of the pore water chemical conditions prevailing in host-rock formations. In the last two decades, important progress has been made in the experimental characterization and thermodynamic modeling of pore water speciation, but the influence of experimental artifacts and uncertainties of thermodynamic input parameters are seldom evaluated. In this respect, we conducted an uncertainty propagation study in a reference geochemical model describing the pore water chemistry of the Callovian-Oxfordian clay formation. Nineteen model input parameters were perturbed, including those associated to experimental characterization (leached anions, exchanged cations, cation exchange selectivity coefficients) and those associated to generic thermodynamic databases (solubilities).
While considerable effort with this experimental and modeling coupled approach enabled to produce predictive models for claystone pore water chemical composition that are consistent with experimental characterization, little attention has been directed to evaluate the model output uncertainties induced by input parameters uncertainties. Indeed, although the treatment of uncertainties in the performance assessment of geologic high-level radioactive waste repositories is recognized as an important topic for more than three decades 6 , most of the studies focus on uncertainties related to retention processes 7. Using Monte Carlo methods, the effects of database parameter uncertainty have been evidenced on geochemical equilibrium calculations but limited to very simple (as stated by the authors) modeling scenarios 8.
Framework Pore water composition model. The estimation of pore water chemical composition in the COx claystone relies on a geochemical model, of which complete description can be found in  4. The model is briefly presented and made available in the form of a PHREEQC  18  input file and its associated database (THERMOCHIMIE v9b  19  ) in the supplementary information file. The complete list of pore water chemical composition model input parameters are: Cl -and SO  2- 4  total concentration obtained from core sample leaching measurements; measured sodium Na + , potassium K + , calcium Ca 2+ , magnesium Mg 2+ , and strontium Sr 2+ exchangeable concentrations; related Na + /K + , Na + /Ca 2+ , Na + /Mg 2+ , Na + /Sr 2+ cation exchange selectivity coefficients; and solubilities of Celestite, Calcite, Dolomite, Goethite, Quartz, Pyrite, Ripidolite, and Illite (corresponding to illite _ Imt-2 of the database).
Acknowledgements The work of P. S. , C. C. and F. C. has been supported by the European project  DONUT. C. T. would like to acknowledge the funding support by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d' Avenir" Program LabEx VOLTAIRE ,  10-LABX-0100. The authors would like to thank the two anonymous reviewers for taking the time necessary to assess the manuscript and for their thoughtful comments and constructive suggestions.
g. , Bayesian Model Averaging (BMA), pseudo Bayesian Model Averaging, and model stacking]  (Fragoso et al. , 2018; Yao et al. , 2018). As a result, multimodel combination and averaging approaches have been gathering interest for a variety of natural hazard applications  (Marzocchi et al. , 2012; Marzocchi et al. , 2015; Yan and Moradkhani 2014; Song et al. , 2018). However, to date, there has not been any consideration of how such approaches may be applied to frequency-magnitude estimation for volcanoes. In this work, we evaluate the uncertainty within long-term frequency-magnitude relationships for 176 volcanoes in Southeast Asia, where volcanoes were chosen by their presence in Smithsonian Institution's Global Volcanism Program (GVP) Volcanoes of the World database (version 4. 8. 5) (Global Volcanism Program, 2013)  and provide a detailed analysis of 24 of these volcanoes with a high Population Exposure Index (PEI) value of 7 (equivalent to a fatality-weighted population exposure of 300,000 people)  (Brown et al.
Conceptual Overview We derive frequency-magnitude relationships for 176 volcanoes in Southeast Asia, but we only use the 24 volcanoes with PEI = 7 as case studies for detailed examination in this manuscript. This limits the diversity of volcano types under consideration, but we wanted to focus on high threat volcanoes as case studies in this paper. The entire dataset of n = 176 are made available as Supplementary Material. The conceptual process used in this work to derive frequency-magnitude relationships is outlined in Figure  1. We first filter the global eruption catalogue to only include eruptions within the record considered complete. The reason for this is to ensure that the eruption record is not biased towards either small or large eruptions. For example, if we considered the entire record within the global eruption catalogue, we would undoubtedly underestimate the eruption probability due to missing eruption records, with smaller eruptions overwhelmingly under-represented; conversely a too short record would not capture the larger eruptions with lower frequency.
We apply two different published analogue classification systems to establish priors for our frequency-magnitude relationships. One approach uses the average across analogue classes globally  (Jenkins et al. , 2012) , whilst the other does this for Southeast Asia and Japan only  (Whelley et al. , 2015). These averaged frequency-magnitude relationships form the basis of our prior models, which are then updated by the volcano-specific record where possible. If the individual volcano has one or less records with an assigned VEI within the eruption record considered complete, no Bayesian update is conducted as there is insufficient information to update our prior model (16 volcanoes in our subset, 102 volcanoes across the entire dataset). If the volcano under investigation has more than one eruption with an assigned VEI within the record considered complete (8 volcanoes in our subset, 74 volcanoes in the entire dataset), that volcano's eruption record is used to update the prior model and produce a frequency-magnitude estimate, which is detailed in Section 2.
Assigning Volcano Analogue Sets To assign a prior model to each volcano, we use the average activity of similar volcanoes as defined by analogue classification systems. Through the process of our systematic literature review we identified just four studies that have used formal analogue classification systems when estimating frequency-magnitude relationships in Southeast Asia  (Jenkins et al. , 2012; Jenkins et al. ,2018; Whelley et al. , 2015; Bear-Crozier et al. , 2016). Two broad approaches emerged from this, both resulting in five analogue categories. One approach uses the "Primary Volcano Type" field within the GVP database to classify analogue volcano sets (henceforth referred to as A1)  (Jenkins et al. , 2012; Jenkins et al. ,2018; Bear-Crozier et al. , 2016). The other approach uses a combination of geomorphological analysis (using digital elevation models, radar scenes, and satellite imagery) and an examination of recent volcanic activity from six eruption databases (see  Whelley et al.
The prior distribution is then updated using the volcano-specific eruption record of the volcano under consideration. We outline the approach taken to characterise the annualised probability of an eruption and the relative probability of each VEI in Sections 2. 2. 3. 1 and 2. 2. 3. 2, respectively. We use the Global Volcanism Program Volcanoes of the World (GVPVotW) database version 4. 8. 5 (downloaded 27 January 2020) in this study  (Global Volcanism Program, 2013). All Bayesian analysis is conducted using the PyMC3 version 3. 8 probabilistic programming package for python  (Salvatier et al. , 2016). PyMC3 uses the No-U-Turn Sampler (NUTS), which is a self-tuning variant of the Hamiltonian Monte Carlo algorithm  (Hoffman and Gelman, 2014; Salvatier et al. , 2016). The full code to our analysis including an annotated example within a Jupyter Notebook for Merapi volcano is available at: https://github.
We did this by counting the number of unknown VEI eruptions in the entire record and dividing by the number of years between the earliest occurring unknown VEI eruption in the volcano's record and 2019 (the year of the GVP database used in this study). These three estimates were then summed into a single value for the long-term average rate of eruptions per year. Then, this number is multiplied by 10,000 to indicate the assumed number of volcano-specific eruptions over a 10,000-year time-frame, under the assumption of stationarity over time [average rate of future activity assumed to be equal to average of past activity:  Bebbington (2013) ]. The posterior distribution is then proportional to the product of the prior and likelihood function, which is a Beta distribution Eq. 7. Samples are drawn using PyMC3, with 20,000 samples drawn using two chains with tuning of 10,000 and a target acceptance rate of 0.
8. We chose to pool VEI 0-3 into a single VEI ≤ 3 category to avoid the issues associated with the default assignment of VEI 2 for some eruptions within the GVP database when VEI was unknown  (Siebert et al. , 2015). The Dirichlet-Multinomial model much like the Beta-Binomial is convenient to use as it enables stability between the prior and the posterior. p θ VEIx |α i Γ k i 1 α i k i 1 Γ(α i ) k i 1 θ αi-1 VEIi (8) Where α i is the vector: (α VEI ≤ 3 , α VEI 4 , α VEI 5 , α VEI 6 , α VEI 7 ). This vector is obtained by counting the relative proportion of eruptions at each VEI class for each analogue class within the record considered complete. The level of confidence one has in the prior can be adjusted by changing the magnitude of the pseudo counts by multiplying by a concentration parameter that controls overdispersion of the data (a larger value puts more importance on the prior model)  (Marzocchi et al.
p θ VEIx |α i k , v j k ∝ Dirichlet α VEI1 + v j1 ,. , α VEI k + v j k (10) Stacking combines each base model into a single metamodel that minimises the divergence between the metamodel and the observed data  (Yao et al. , 2018). The stacking within the PyMC3 python package uses a logarithmic scoring system to evaluate how well a given prior probability distribution performs when compared with the observed data. The model is fitted n times, with one data point left out each time. This is approximated by using the LOO statistic within PyMC3, which estimates the expected log point-wise predictive density using Pareto-smoothed importance sampling LOO cross validation  (Vehtari et al. , 2017; Vehtari et al. ,2019). Bayesian model averaging weights models by their marginal likelihood. However, it is challenging to compute the marginal likelihood, which is why PyMC3 applies an alternative approach (called Pseudo BMA) using the WAIC or the LOO to estimate weights by assessing the relative probability of each model.
Note, we do not include the analogue classification system within this sensitivity analysis, as it is addressed separately within our Bayesian updating results (Section 3. 2. 2). As outlined in the above section, PyMC3 contains three different model averaging approaches that can be applied. To test the sensitivity of the resulting frequency-magnitude relationships, we repeat the analysis using each of the three approaches, whilst keeping all other conditions constant. The underlying data have the potential to influence both the priors and the likelihoods used in this analysis. There are two elements of this that we test for sensitivity: choice of change point, and choice of eruption certainty (as classified within the GVP catalogue). We vary the change point by using the 5th, 50th, and 95th percentile change points reported in  Mead and Magill (2014)  to test the sensitivity of our frequency-magnitude estimates to these parameters.
Analogue classification used by  Jenkins et al. (2012) ,  Whelley et al. (2015)  in square brackets: LC A1 , Large cone, LD, Lava Dome, SC, Small cone, C, Caldera, DC, Distributed cones and fields; LC A2 , Large caldera, SPS, Semi-plugged stratocone, OVS, Open-vent stratocone. Volcanoes with no recorded eruption in the entire Holocene GVP eruption record are denoted by *. Note: Merbabu A2 classification was changed from OVS  (Whelley et al. , 2015)  to SPS (this study) as it does not meet the criteria outlined in  Whelley et al. (2015). Frequency-Magnitude Volcanoes Southeast Asia received less attention to compile their eruptive histories, or 3) some combination of both. The 5th-95th credible interval bands using the A1 models are generally much larger than those of the A2 models. This is possibly an indication of heightened dispersion of volcano behaviour when using a global dataset (i.
e. , A1 model) compared to a more regionalised dataset (i. e. , A2 model). Indeed, using a regionally constrained dataset (i. e. , only the volcanoes used in the A2 model) for the A1 model leads to less dispersion across each analogue class as some of the volcanoes at the extreme high and low frequencies are removed (Figure  12 ). However, it also becomes difficult to use only regional level data in the A1 classification system because of limited number of volcanoes in some of the analogue classes (e. g. , small cones). Volcanoes that are classified as "small cones" have a particularly large uncertainty for the A1 model that spans several orders of magnitude, potentially indicating that assigning frequency-magnitude in this manner may not be advantageous. Interestingly, for the A2 model, an inflection point occurs at VEI 5 for volcanoes classified as "Large Calderas", indicating that this class of volcano demonstrates enhanced frequency of eruptions at VEI 7 compared to VEI 5 (Figure  8 ), or that the preservation, recording and/or study of the differently sized eruptions is not equal.
Agreeability Between Published Frequency-Magnitude Estimations for Southeast Asian Volcanoes The discrepancy between published frequency-magnitude estimates presents a challenge for hazard and risk assessments as they may make resource allocation more challenging and are likely to considerably influence risk metrics such as expected losses (e. g. , life or financial) per unit time, and stochastic catalogues. Most of the published frequency-magnitude probabilities for Southeast Asian volcanoes are presented as point estimates, which means that the uncertainty associated with each is unknown. Frequency-magnitude estimates may be influenced by different aspects of the methodologies such as the underlying data (e. g. , combining multiple databases vs. using a solitary database), how data completeness is dealt with, and how analogues are defined.
Uncertainty in Frequency-Magnitude Estimates for Southeast Asian Volcanoes and Implications for Volcanic Hazard and Risk Assessments In this paper, we have provided a generalised and systematic approach that facilitates stochastic sampling of frequencymagnitude estimates that will enhance the robustness of future hazard and risk assessments for these volcanoes. The use of freely available datasets means the approach taken in this paper may also be applicable in other volcanically active regions where considerable gaps in the volcano record exist. A feature of this is that as new information becomes available (e. g. , updates to the eruption catalogue), the estimates can be easily updated to ensure that the newest information is captured within volcanic hazard and risk assessments. Over half of our study volcanoes in the region did not have sufficient eruption records to allow for Bayesian updating, including volcanoes that have a very high population exposure index (PEI 7), for example Malabar, Tampomas, and Penanggungan.