dataesr/scientific-paragraphs-categorization huggingface dataset:
The major difficulties for validating space-borne lidar oceanic products are the lidar footprint size (70 m for CALIOP) and the revisit time (16 days) which poses a significant challenge for having a significant number of match-ups. Alternative validation protocols need to be developed specifically for space-borne lidar sensors to ensure the accuracy and reliability of their data products. The much smaller footprint of lidar sensors in comparison to ocean color sensors swaths of 1,000+ km makes it challenging to collocate in situ data. Moreover, the repeat cycle of lidar sensors, as CALIOP, is much less frequent than ocean color sensors, making it difficult to select an appropriate time and space window for validation of lidar data due to scarcity of coincident in situ data. Recent studies have adopted various spatio-temporal scales for validating CALIOP data with in-situ BGC-Argo b bp  (Lacour et al.
, 2020; Bisson et al. , 2021). However, there is a lack of research aimed at providing clear guidelines to the scientific community regarding the optimal spatio-temporal scales for validating satellite lidar sensors. In this study, the objective is to establish a standardized methodology with a clear and objective criterion for validating CALIOP oceanic products, b bp (532), but also other current and future space-borne lidar oceanic products, given the limited availability of datasets that makes direct validation challenging. The proposed methodology uses an objective statistical score to determine the optimal time-distance window. The CALIOP b bp (532) archive from 2010 to 2017 and globally was compared to the BGC-Argo b bp measurements through a match-up exercise, where the CALIOP footprint was co-located with the BGC-Argo measurement with criteria on the time (Δt) and distance (Δd) differences between both observations.
Data 2. 1 CALIOP CALIOP measures the back-scatter lidar signal at 532 nm and 1,064 nm wavelengths and is a nadir-pointing lidar  (Winker et al. , 2010). The measurements in CALIOP comprises of the co-polarized and cross-polarized components of the vertically integrated backscatter with a vertical resolution of 22. 5 m in ocean waters  (Behrenfeld et al. , 2013; Bisson et al. , 2021). The day-and nighttime lidar derived b bp products published by  Behrenfeld et al. , 2019  as available online (http://orca. science. oregonstate. edu/lidar_public_v2. php) were used in this study. Similar to  Bisson et al. , 2021, modification  to this dataset was performed by using a conversion factor (β(π)/b bp ) of 0. 32 instead of 0. 16. That is, the dataset was multiplied by a factor of 0. 5. The CALIOP data used in this study are for the period 2010 to 2017 and includes both dayand night-time data.
BGC-Argo The BGC-Argo b bp profiles at 700 nm were downloaded from the biogeochemical-argo. com website on 25-12-2021  (Claustre et al. , 2020). Synthetic delay mode data were used which are quality controlled and depth adjusted data. There were about 41,420 data points with b bp data for the period between 2010 and 2017 at separate locations. Outliers were removed using 1. 5 times the interquartile range method. For a comparable dataset between the CALIOP and Argo, the data had to be averaged within the mixed layer depth. This was achieved by finding the depth where the density is more than 0. 03 kg•m -3 with respect to the density at the depth 10 m. The global median mixed layer depth from the BGC-Argo dataset is 18 m and has an inter-quartile range of 3. 9 m  (Bisson et al. , 2021). However, only depths less than 50 m were considered, and the global median value was used at stations where this method of finding mixed layer depth (MLD) did not work due to a smaller number of samples in the profile.
The values of the calculated BGC-Argo values were reported to be not significantly changed if the first light attenuation layer was chosen instead of mixed layer depth as in  Bisson et al. , 2021. Figure  1  shows the locations of the BGC-Argo data points having a match-up with the CALIOP dataset, within 16-day and 50-km spatio-temporal range. As the wavelength at which b bp is provided (at 700 nm for BGC-Argo and at 532 nm for CALIOP), it is necessary to transform BGC-Argo b bp (700) into b bp (532) for direct comparison. This was done with the following equation: b bp 532 ( ) b bp 700 ( ) × 532 700 -γ (1) Where Y represents the spectral slope of the backscattering spectra.
MODIS-Aqua MODIS-Aqua level-3 remote sensing reflectance (R rs ) data at 443 nm and 555 nm with the 8-day average and 9-km resolution were downloaded from the NASA ocean color website. The spectral slope of the backscattering coefficient (Eq. 1) was determined through the computation from the R rs at 443 nm and 555 nm, using the following formula  (Lee et al. , 2002) : γ 2. 2 1 -1. 2e -0. 9 Rrs 443 ( ) / Rrs 555 ( ) ( ) (2) This was required for converting the b bp (700) from the BGC-Argo data set to b bp (532) for comparison with the CALIOPestimated b bp (532). Similarly, the annual MODIS Aqua derived SST values were also obtained from the NASA website. The K d values from MODIS Aqua with 8-day average and 9 km resolution were also downloaded to be used in the depth averaging of BGC-Argo b bp as in  Lacour et al.
Description of the validation protocol by Bisson Bisson et al. , 2021 used a decorrelation approach to choose the time-distance windows for the matchups between global BGC-Argo and CALIOP datasets for the period 2015-2017. Only daytime dataset was considered for the analysis. b bp (700) from the BGC-Argo dataset was converted to b bp (532) to match with the wavelength of CALIOP using a spectral slope calculated from inversion of MODIS-Aqua R rs using the generalized IOP model  (Werdell et al. , 2013). A 3-point moving average median was performed on the BGC-Argo profiles of b bp and removal of outliers were performed (more than 1. 5 inter-quartile range). The CALIOP dataset was used as processed by Behrenfeld et al. , 2019 except for using a conversion factor of 0. 32 instead of 0. 16 for the ratio of β(π) to b bp.
Description of the validation protocol by Lacour Lacour et al. , 2020, validated CALIOP observed b bp data with BGC-Argo data in the North Atlantic for the year 2014. They defined three configurations of spatio-temporal windows: 9-km/ 16 days, 1 °/16 days, and 2 °/1 month. The analysis included both dayand night-time data. BGC-Argo data were denoised through the removal of data points along the profile designated as "bad" or "probably bad. " A fixed backscattering spectral slope of 0. 78 was used to convert BGC-Argo b bp (700) to b bp (532). The vertical integration of the b bp profiles from the BGC-Argo to match the CALIOP data was performed through the application of the following equation. 490 )) measured by the floats, and then determined the mean slope over the initial 50 m of each profile.
Introduction We proposed a universal protocol validation for comparison between in-situ measurements and space-borne lidar oceanic products. To do that, we used the BGC-Argo observations as it provides a global distribution of b bp (532) since 2010. It is a valuable dataset for validation of ocean color satellite products  (Bisson et al. , 2019). However, the comparison between the BGC-Argo and CALIOP b bp is not straightforward for several reasons: measurement of b bp (700) vs. CALIOP b bp (532); profiles of b bp (700) vs. depth-integrated CALIOP b bp (532). We investigated the sensitivity of our analysis to these factors.
SST threshold for the time-distance window Bisson et al. (  2021 ) has suggested to use the annual global average SST as a threshold for choosing the distance window between the BGC-Argo floats and the CALIOP footprint. Our analysis proposes that this SST threshold be not be used for several reasons. First, it imposes an additional condition on the match-ups protocol, requiring an extra parameter to validate CALIOP datasets, which can impede ease of operation. Second, our results indicate that our proposed time-distance window generated superior scores in comparison to the 50-km/24-h case employed in  Bisson et al. (2021) , for the SST>15 °C scenario. Third, even though the 15-km/24-h combination in the SST<15 °C case produced comparable scores, our analysis showed that our proposed time-distance window outperformed the proposed protocol by  Bisson et al.
Acknowledgments The authors would like to thank  Kelsey Bisson  for fruitful discussion and for sharing her match-ups dataset and  Leo Lacour  for sharing his codes and fruitful discussions. BGC-Argo data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it. We would like to thank the  Ocean Productivity Group at the Oregon State University  for providing the CALIOP dataset.
To illustrate and compare these methods, we will apply each approach to examine the association of the 24HAC with a test of global cognition in a cohort of older adults who were part of the Adult Changes in Thought Activity Monitoring (ACT-AM) sub-study. In the literature, comparisons between ISM and CoDA have been made previously in particular settings  (Biddle et al. , 2018; Dumuid et al. , 2018a) , but a comprehensive comparison of these three approaches has not been previously considered. We emphasize the practical application and interpretation of these three models in relation to the cognitive outcome. We also provide sample code in the R software  (R Core Team, 2021)  on GitHub, 1 as well as sample syntax for the Latent GOLD LPA software, so that the presented analyses can be readily adapted to other settings.
We defined each full day as out-of-bed time to the next day's out-of-bed time. Based on best practices for free-living activity measurement, a minimum of 4 days with 10 or more hours of waking wear time, as defined by the presence of valid device data during participant self-reported waking periods, was required to be included in analyses  (Donaldson et al. , 2016; Migueles et al. , 2017). We used proprietary PAL Technologies software to extract eventlevel files. Events files were then processed by collapsing consecutive activities of the same activity type using a batch processing package activpalProcessing in the R software  (Lyden, 2016). Self-reported time in bed, which the device captures as sitting/lying time, was removed from calculation of waking activity metrics. For simplicity, we refer to time in bed as "sleep.
Statistical methods and analysis of the 24HAC In this section, we describe three analytical approaches: ISM, CoDA, and LPA. For each approach, we describe the method and illustrate its application with the analysis of the 24HAC and its crosssectional relation to the CASI-IRT score in the ACT cohort. We will then summarize key features of each approach and provide guidance on selecting the most appropriate approach based on features in the data and the scientific question of interest.
The nonlinear ISM analysis can be done in R with package "mgcv" Sample R code for this analysis is provided on GitHub. 2  Lastly, through combinations of the regression coefficients, the ISM can also be used to estimate the expected difference in mean outcome between two populations that have different 24-HAC profiles (i. e. , different mean time spent on each behavior), but the same values for all other model covariates, which may also be of scientific interest.
ISM illustration: Associations between activity behaviors and CASI-IRT in the ACT cohort Four linear ISMs adjusted for age, sex, years of education, race/ethnicity, BMI, depressive symptom scores, and self-rated health conditions were fit to the ACT data (excluding 34 (3. 3%) with missing covariates), with each of the four activities omitted from the model one at a time. The associations of 30-min time reallocations between any two types of activity are summarized in Table  2. The ISM model with Step dropped suggested that reallocating 30 min/day from sitting, standing, or sleeping to stepping was associated with 0. 027 [-0. 011, 0. 064], 0. 033 [-0. 011, 0. 078], and 0. 027 [-0. 014, 0. 067] SD units higher mean (95% confidence interval (CI)) CASI-IRT score, respectively. Symmetric results can be observed for any two activities that would be exchanged.
Compositional data analysis CoDA is another widely used analytic approach to handle 24HAC data and its associations with health outcomes  (Chastin et al. , 2015; Biddle et al. , 2018; Dumuid et al. , 2018b; McGregor et al. , 2021). Janssen et al. , 2020 conducted a systematic review of CoDA studies examining associations of 24HAC with diverse health outcomes in adults. For CoDA, the fundamental unit of observation is the multivariate vector of the proportions or percentages of the 24 h that are spent in each type of activity. In the ACT study, we will consider the 24HAC composed of the sleep, sit, stand and step behaviors. One scientific question of interest in the 24HAC paradigm is how different profiles of 24HAC can affect a health outcome. One advantage of CoDA is that it provides a natural way to compare health outcomes between two compositions, including substitution of one behavior for another.
There was a tendency for subjects spending less time in sitting and more time in stepping to 3 https://github. com/yinxiangwu/24HAC_illustrations have higher CASI-IRT scores, which will be further explored in the multivariable regression below. Due to the large sample size, relatively small differences in the compositions were statistically significant at the level of 0. 05, using the James multivariate analysis of variance test on the ilr-transformed 24HAC. Table  3  summarized regression coefficient estimates for the four CoDA pivot coordinates, each of which quantifies the effect of increasing time in one behavior by a factor while simultaneously decreasing time in the other behaviors by another factor. None of these time reallocations were statistically significantly associated with changes in the CASI-IRT scores; however, a higher proportion of time in stepping was weakly associated with higher CASI-IRT scores.
As mentioned before, a key feature of 24HAC data is the co-dependence between activity behaviors. This co-dependence prevents us from applying LPA to all 24HAC variables because it will lead to a degenerate (rank-deficient) covariance matrix. Two possible solutions to consider are (1) apply LPA to the same ilr-transformed variables used in CoDA (see Section 3. 2 more details of ilrtransformation; see  Gupta et al. , 2020; von Rosen et al. , 2020)  or (2) drop one activity behavior variable from the LPA (see  Jago et al. , 2018). Although the first approach retains the full composition, one potential drawback is that the ilr-transformation, involving log-ratios, could generate skewed profile indicators unsuitable for LPA. For example, if one behavior often had a relatively small proportion of the composition, this could create a large ratio for the ilr-coordinates with this behavior in the denominator.
, 2004; Vermunt, 2010; Asparouhov and Muthén, 2014) , which adjust for the uncertainty in the latent profile assignment. These methods assume conditional independence of covariates and profile indicators given the latent variable. For recent advances and discussion of more complex models, such as assuming covariates have direct effects on observed profile indicators, multilevel or latent transition models, or more than one latent class variables, refer to  Bakk and Kuha (2021) ,  Nylund-Gibson et al. (2019) ,  Bray and Dziak (2018) , and  Vermunt and Magidson (2021b). In our data analysis, LPA was performed in the Latent GOLD 6. 0 software  (Vermunt and Magidson, 2021a). The improved BCH method, which has been advocated as a preferred method for continuous external outcomes  (Bakk and Kuha, 2021) , was used to relate latent profiles with the outcome CASI-IRT score adjusted for age, sex, race/ethnicity, BMI, education level, depressive symptoms, and self-rated health.
Latent GOLD syntax for this analysis is provided in Supplementary Appendix A2. 2. We compared these results to those of the biased approach of ignoring the uncertainty in the class assignment. As a sensitivity analysis, we repeated this analysis with the ML-based instead of the BCH adjustment for the uncertainty in the latent-profile assignment using the same software. We also performed analyses of using each of the covariates as a predictor for latent profiles with the ML-based method, which can be done using the Latent GOLD "Step-3" module. p-values for all associations were reported based on the Wald-type test using robust standard error estimates [using robust standard error estimates is necessary as shown in  Vermunt (2010)  and  Bakk et al. (2014)  and is the default in Latent GOLD 6.
LPA can be also performed in R with the package tidyLPA  (Rosenberg et al. , 2019)  and Mplus  (Muthén and Muthén, 2017). A tutorial of using this R package and its comparison to Mplus can be found in Wardenaar  (2021). Model fit statistics (Supplementary Table  S3 ) were used as initial screening methods to select candidate models and then combined with each model's interpretability, minimal profile size, and finally the likelihood ratio-based tests to determine the final number of profiles. Supplementary Table  S3  presents model fit statistics for models with 2-6 number of latent profiles. AIC, BIC, consistent AIC, samplesize adjusted BIC, integrated complete likelihood BIC favor the model with 6, 3, 3, 4, and 2 latent classes, respectively. The solution with 6    S4 ), we can see that the four-class model additionally identifies a group with lower than average sleeping time, as well as higher activity (standing and stepping) compared to profiles 3 and 4 with similar or lower sitting time, which could correspond to a clinically interesting 24HAC profile in this ACT cohort.
Research questions and assumptions The main research question that both ISM and CoDA address regarding 24HAC data is the associated effect of time reallocation on the study outcome. Typically, ISM is focused on substituting time spent in one activity for another, whereas CoDA more naturally considers differences between two compositions. Both ISM and CoDA estimate the substitution effect in a standard regression framework and the usual assumptions would apply, e. g. , for our ACT study example, usual linear model assumptions apply. ISM is generally conducted on the original time scale (e. g. , hours of activity); whereas, CoDA models the proportion of time spent in each activity. Hence, CoDA is scale invariant, which implicitly means that the time information of 24HAC is irrelevant in CoDA, e.
g. , the amount of time spent on each behavior, which is an additional assumption that researchers should evaluate. In contrast to ISM and CoDA, LPA is a more exploratory method used to identify distinct latent subgroups with respect to activity profiles based on observed 24HAC data. Another aim of LPA is to explore the correlates of identified profiles and the associations of the profiles with outcomes. LPA assumes the observed data are sampled from a population composed of distinct subpopulations with heterogeneous distributions of activity behaviors of a day and models the data using a mixture of multivariate normal distributions. Hence, LPA results can be sensitive to the distributions of activity behaviors, while for ISM and CoDA, this assumption is not necessary if the outcome model is correctly specified.
Multivariable regression analysis Both ISM and CoDA estimate the effects of time reallocations through standard multivariable regression models; however, they handle the collinearity between activity behaviors differently. ISM is formulated by including the total activity and all but one of the activity variables -the activity you will explore reallocating. In contrast, CoDA considers 24HAC as a composition and applies the isometric log-ratio (ilr) transformation to transform the compositional data into a set of continuous variables in a lower dimensional space that standard statistical approaches can work with. Although the procedure and the interpretation of the 24HAC model coefficients are more complicated, regarding 24HAC as a composition facilitates the comparison of health outcomes between any two compositions.
Alternatively, values could be imputed by statistical imputation tools  (Martín-Fernández et al. , 2003; Palarea-Albaladejo et al. , 2007; Palarea-Albaladejo and Martín-Fernández, 2008). Rasmussen et al. (2020)  compare different approaches for handling zeros in compositional physical activity data and found that the simplistic approach of replacing zeros with a small fixed value led to more distortion of the underlying composition than other imputation approaches  (Martín-Fernández et al. , 2003; Palarea-Albaladejo et al. , 2007; Palarea-Albaladejo and Martín-Fernández, 2008). If the occurrence of zero values is expected for one or more behaviors, CoDA may not be an appropriate approach. For example, if MVPA was considered as a component of the 24HAC for a given cohort with limited physical function, many individuals could have zero min/day in MVPA.
Several previous papers  (Ward et al. , 2005; Matthews et al. , 2012; Donaldson et al. , 2016; Migueles et al. , 2017)  have explored the minimum number of accelerometer wear days required to accurately capture the daily variability of physical activity and sedentary behavior patterns. These suggest that a minimum of 4 days of wear is adequate to capture 90% or more of the variability of physical activity or sedentary behavior patterns for adults  (Donaldson et al. , 2016). Accounting for the many variable factors in accelerometry measurement (consecutive vs. non-consecutive wear, weekday vs. weekend variability, seasonal patterns, etc. ), however, it is possible that 4 days, or even 7 days, of data are not sufficient for capturing the usual short-term 24HAC activity pattern or long-term patterns.
Automatic detection and classification of animal sounds has many applications in biodiversity monitoring and animal behaviour. In the past twenty years, the volume of digitised wildlife sound available has massively increased, and automatic classification through deep learning now shows strong results. However, bioacoustics is not a single task but a vast range of small-scale tasks (such as individual ID, call type, emotional indication) with wide variety in data characteristics, and most bioacoustic tasks do not come with strongly-labelled training data. The standard paradigm of supervised learning, focussed on a single largescale dataset and/or a generic pre-trained algorithm, is insufficient. In this work we recast bioacoustic sound event detection within the AI framework of few-shot learning.
We adapt this framework to sound event detection, such that a system can be given the annotated start/end times of as few as 5 events, and can then detect events in long-duration audio-even when the sound category Preprint submitted to journal was not known at the time of algorithm training. We introduce a collection of open datasets designed to strongly test a system's ability to perform fewshot sound event detections, and we present the results of a public contest to address the task. Our analysis shows that prototypical networks are a very common used strategy and they perform well when enhanced with adaptations for general characteristics of animal sounds. However, systems with high time resolution capabilities perform the best in this challenge. We demonstrate that widely-varying sound event durations are an important factor in performance, as well as non-stationarity, i.