dataesr/scientific-paragraphs-categorization huggingface dataset:
Accordingly, column headings are assigned to the corresponding model variables, from the subsequent detection of the exposure routes and elimination processes considered in the experimental design. MOSAIC bioacc then simultaneously estimates all the model parameters under a Bayesian inference framework. In the end, the posterior probability distributions of bioaccumulation metrics are calcu-lated from the kinetic rate estimates. Based on our experience, for a proper use of MOSAIC bioacc , we recommend at least six internal concentration measurements during the accumulation phase to ensure the underlying algorithm to converge, whatever the chosen time points, the total duration of the experiment having no matter. Full analysis reports are available from the TK database itself for each data set, as well as references from which data have been extracted.
Meta-analysis of the TK database Even if not required within regulatory documents, associate the uncertainty to a bioaccumulation metric is of crucial importance  (Wassenaar et al. 2020 ). Among the 211 data sets, a total of 137 corresponds to an exposure through water , meaning that MOSAIC bioacc analyses 137 BCF probability distributions. Based on the median and the 95% uncertainty interval of these BCF estimates (Figure  2 ), it appears that aquatic invertebrates have the highest values, when predominantly exposed to pesticides or metals, with Gammarus the most represented genus among BCF estimates greater than 5000 classifying the corresponding chemical substances as "vB". The SI (see the. html file) provides an additional figure with all the 211 estimated bioaccumulation metrics, without distinguishing BCF from BSAF and BMF estimates.
In fact, from Figure  3 , it can even be stated that this substance has 59. 6% to be non bioaccumulative, and correspondingly 40. 4% to be, because the quantile associated with the ERA threshold at 1000 is equal to 59. 6%. Over the 211 data sets available in the TK database, the Q75 criterion classified 28. 9% of the chemical substances as "B" (n = 61) against 17. 5% for the Q50 criterion (n = 37). Figure  4  shows another example with genus Enchytraeus exposed to silver nanoparticles  (Topuz and van Gestel 2015) , illustrating the possible misclassification of the bioaccumulation capacity of the chemical substance according to the Q97. 5 criterion. Indeed, silver nanoparticles are considered as non bioaccumulative (BCF < 1000) by both the Q50 (the ERA criterion) and the alternative Q75 criteria, whereas the Q97.
3% chance of being non-bioaccumulative, while the decision Fig. 4  Violin plot of the posterior probability distribution of the bioaccumulation metrics for Enchytraeus exposed to silver nanoparticles  (Topuz and van Gestel 2015). The left-side scale gives bioaccumulation metric values, while the right-side scale gives the corresponding quantiles of these values. The orange lines symbolize the threshold values at 1000 and 2000 according to regulatory ERA, while the green lines stand for the 50 th (Q50 criterion), 75 th (Q75 criterion) and the 97. 5 th (Q97. 5 criterion) quantiles of the distribution, respectively (Table  1 ). threshold is located far in the tail of the distribution, that is within a range of very few probable values. Considering all the 211 data sets available in the TK database, we performed the classification for all the chemical substances based on each of the three Q50, Q75 and Q97.
For this purpose, the MOSAIC bioacc web tool could easily be updated with an additional tab automatically delivering both Q50 and Q75 criteria together with the corresponding classification. See SI (. html file) for an example of what this might look like. In the same way, the TK database could be amended to also provide this classification in an all-in-one facility for ERA. Over the whole database, using the suggested Q75 criterion, we finally classified 5. 2% (n = 11), 9. 5% (n = 20) and 14. 2% (n = 30) of the chemical substances as " B", "B" and "vB", respectively. This confirms 71% of the chemical substances classified as non bioaccumulative. Focusing on the precision of all bioaccumulation metrics, we got a high variability among species and chemical substances (see SI,. html file). Only considering the Q75 criterion, 37.
4% (n = 79) of the chemical substances were associated with a CV < 0. 5, with the high est CV among the "nB"-classified chemical substances (38. 9%, n = 82/150). This underlines that for a chemical substances as bioaccumulative, whatever the class, the corresponding bioaccumulation metric was precisely estimated in most of the cases. Collecting additional TK data sets is the core foundation of our TK database  (Ratier and Charles, 2022). Indeed, it has been thought and designed to be dynamically incremented so that researchers are increasingly willing to share their data, either publicly via dedicated repositories, or privately directly to us to be made them available in TK database by referring to the corresponding scientific publication. In turn, they would benefit from automatic reports with MOSAIC bioacc , for example to revisit previous works or to perform experiments and analyses differently in the future.
Conclusion Based on the meta-analysis of the TK database , encompassing today 211 data sets, this paper establishes how crucial it can be to consider the uncertainty in the calculation of bioaccumulation metrics when classifying chemical substances into specific bioaccumulative categories. We fitted TK models under a Bayesian framework, that we specifically chose for its ease of use in delivering of bioaccumulation metrics as posterior probability distributions. This paper thus gathers together statistically-founded results towards the recommendation for a new criterion to sustain this classification. It could indeed be suggested to use the 75% (our preferred indicator) or the 97. 5% quantiles of the bioaccumulation metric distributions in order to most appropriately guide the classification of chemical substances into the four regulatory ERA categories: non-, low-, medium-, or very-bioaccumulative.
shows several tree maps performed with the treemap R-package (Tennekes 2017) allowing to visualise the proportion of each chemical category (Figure 1-a) and each genus (Figure 1-b) among the 211 data sets. Pesticides and hydrocarbons are the most represented chemical substances within the TK database, what can be explained by the predominance of data sets on freshwater invertebrates and fish. Gammarus and Daphnia are the most represented genus, probably due to less ethic exigence with them, while fish studies are rarer. Gammarus and Daphnia are also the genus for which the most biotransformation data are available.
Funding: The authors are thankful to  ANSES  for providing the financial support for the development of the MOSAIC bioacc web tool (CNRS contract number  208483 ). This work is part of the  ANR  project  AP-PROve  ( ANR-18-CE34-0013 ) for an integrated approach to propose proteomics for biomonitoring: accumulation, fate and multi-markers (https://anr. fr/Projet-ANR-18- CE34-0013 ). A large part of the work benefited from the  French GDR "Aquatic Ecotoxicology " framework which aims at fostering stimulating scientific discussions and collaborations for more integrative approaches. At last, this work was made with the financial support of the  Graduate School   H2O ' Lyon  ( ANR-17-EURE-0018 ) and " Université de Lyon" (UdL) ,  as part of the program  " Investissements d'Avenir" run by  " Agence Nationale de la Recherche" (ANR).
We obtained high Pearson r values and low RMSE values at both the regional (r ≥ 0. 93, RMSE ≤ 0. 16, rRMSE ≤0. 37) and pixel levels (median r across pixels ≥ 0. 80, median RMSE ≤ 0. 38, median rRMSE ≤ 0. 64), suggesting high accuracy for the data merging procedure. The merged radar signal was then validated with a continuous ERS-2 data set available between 1995 and 2011. ERS-2 stopped working in full mode after 2001 but observations are occasionally available for a subset of the pixels until 2011. Because the period of 1995-2011 fully overlaps with the working period of QSCAT (1999QSCAT ( -2009)), comparing the merged radar signal against the ERS-2 data in 1995-2011 is the most direct validation available. We found concordant monthly dynamics between the merged radar signals and the ERS-2 signals during 1995-2011, with Pearson r value ranging from 0.
79 to 0. 98 across regions. These results evidenced that our merged radar data have a consistent C-band signal dynamic. The CScat data set (https://doi. org/10. 6084/m9. figshare. 20407857, Tao et al. 2022a) is expected to advance our understanding of the long-term changes in, e. g. , global vegetation and soil moisture. The data set will be updated on a regular basis.
ERS-2 was then launched by ESA in April 1995 as a follow-up to ERS-1. However, starting from early 2001 until the end of mission in 2011, ERS-2 has been operating without gyroscopes, which largely reduced its spatial coverage  (Carabajal and Harding, 2006). Consequently, the distribution of global coverage ERS-2 images to the user community was discontinued for the period 2001-2011. Both sensors operate on a sun-synchronous near-circular polar orbit, passing the Equator at 10:30 am in descending mode. The incidence angle of ERS-1 and ERS-2 ranges from 16 to 50 degrees. ERS-1/-2 images were acquired in vertical (V-) polarization mode, and were usually gridded at 25 or 12. 5 km resolutions  (Frison and Mougin, 1996). The SeaWinds scatterometer (13. 4 GHz, Ku-band) onboard QSCAT was launched by NASA in 1999 and collected data in full mode until November 2009.
Data pre-processing We first aggregated QSCAT and ASCAT images of the BYU version at the resolution of ERS images, namely 8. 9 km/pixel. Ascending path QSCAT and ASCAT images were used. The ascending path time of QSCAT acquisition (6:00 am) is before sun rise, and the ascending path time of ASCAT (21:30 pm) is well after sun set, both reflect nighttime land surface conditions. The ERS images of the BYU version are generated by combining images of all paths to ensure the highest possible spatial and temporal coverages, we therefore used the all-path ERS-1/-2 images. V-Polarization QSCAT images were merged with V-Polarization ERS and ASCAT images. H-Polarization QSCAT images were also tried but very similar merged signals were obtained. The BYU data center provides images synthesized from acquisitions made over periods of 17, three, and four consecutive days for ERS, QSCAT, and ASCAT respectively.
Validation of the data merging approach We also conducted a stricter evaluation of the data merging approach. From January 2001 to 2011, the ERS-2 satellite experienced a series of failures that affected its data continuity and spatial coverage. However, observations were occasionally available for a subset of global pixels  (Crapolicchio et al. , 2012). This period overlaps with the QSCAT operating period, thus it can be used to test whether the corrected Ku-band (QSCAT) signal shows consistent dynamic with the C-band signal. Recently, the European Space Agency (ESA) has released the ERS-2 data set for the period of 2001-2011  (Crapolicchio et al. , 2012). We used this version of ERS-2 data (hereafter referred to as ESA ERS-2) to validate our data merging approach. Excluding Australia, Southern Africa, and the Bering Sea, ten out of the 13 global regions were covered by the ESA ERS-2 data set during 2001 and 2011.
For each of these ten regions, we calculated monthly radar backscatter coefficients at 40-degree incidence angle from the ESA ERS-2 data set, for comparison with our merged radar data set. To normalize the incidence angle, a linear regression was fitted between all incidence angles and the radar backscatter coefficients, and the R squared value and RMSE value of the regression were reported. The backscatter coefficient at 40-degree incidence angle was then predicted by the regression. To ensure data quality, the predicted backscatter coefficient was not used if the RMSE was higher than 0. 5. Since the ESA ERS-2 data have a resolution of 25 km, we aggregated our merged radar signals to that resolution. For each month during 2001 and 2011, pixels with available ESA ERS-2 observations were located, and their ERS-2 signals were averaged across pixels.
Merged radar signals and quality assessments The merged radar signal, averaged across pixels within a region, is presented in Fig. 5. Pearson r, RMSE and rRMSE between the C-band and the corrected Ku-band signals in the overlapping years  (1999-2001 and 2007-2009)  were used to assess the quality of the merged radar signal. All 13 regions had a r value larger than 0. 93, with a maximum of 0. 99. We also obtained low RMSE values (from 0. 03 to 0. 16), even in regions with a large seasonal amplitude in radar signal, such as Siberia where the seasonal amplitude is around 3 dB but the RMSE is only 0. 16. This result was further confirmed by the low rRMSE values obtained in all regions, which ranged from 0. 09 to 0. 37. 315 We further assessed the data merging quality at the pixel level. Before correcting the monthly signal differences, the Pearson r values between the C-band and the scaled Ku-band signals showed a long-tailed distribution in all regions (Fig.
Independent validation of the merged radar signal The quality of the merged radar signals was also validated directly against the ESA ERS-2 data (see Section 2. 5). The number of ESA ERS-2 pixels available for a comparison differed across regions. Furthermore, the pixel number decreased largely around 2003 in many regions (Fig. S4 ). Despite the variations in pixel number, we found similar monthly dynamics between the merged radar signals and the ESA ERS-2 signals in all regions. Using the Pearson r value as an index of similarity, all regions had a Pearson r value higher than 0. 79, with a maximum of 0. 98. Seven regions had a r value higher than 0. 90 (ranging from 0. 90 to 0. 98) (Fig. 9 ). This validation evidences that the CScat data are unlikely to be biased due to the cross-period merging method.
Signal quality and merging mechanism After rescaling the radar time series from different sensors, monthly signal differences were corrected, by modelling them from climatic variables (namely precipitation, skin temperature, and snow depth). The quality of the merged radar signals was assessed against the ESA ERS-2 data set. Highly similar monthly time series were obtained, suggesting high accuracy for the merging procedure. Why did rainfall, skin temperature, and snow depth successfully predict the signal differences? The main reason is that the Ku-band signal has a lower penetrating ability in comparison to the C-band signal because of its shorter wavelength. In regions with a strong rainfall such as the tropics, Ku-band signals are more impacted by raindrops and the intercepted water on forest top-canopy, thus showing different seasonal patterns with C-band signals (Fig.
Limitations and future works We used the reanalysis ERA5-Land monthly climatic data to model the signal differences. As a result, whether signal differences can be accurately modelled partly depends on the accuracy of the ERA5-Land climatic data. Future work will test the effectiveness of other climatic data sets for modelling the signal differences. The accurate mapping of some climatic variables, such as snow depth, are challenging  (Orsolini et al. , 2019; Clifford, 2010; Pulliainen et al. , 2020). This is critical in the high-latitude regions such as northern Alaska, where snow depth is the most important variable predicting the signal differences. The estimation of rainfall is also challenging in regions with sparse climate stations such as the tropics. An increasing amount of climate data sets has been made publicly available, including the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2,  Gelaro et al.
In addition, aerosol could be a contributing factor to the signal differences, especially in deserts such as the Sahara Desert where the rRMSE values in the final CScat data set remained relatively high (Fig. S3 ). The Sentinel 5P mission provides near real-time, high-resolution aerosol products starting from the year of 2018  (Ingmann et al. , 2012). Analysis will soon be conducted to assess whether NDVI and aerosol layers can further improve the data merging quality. Another potentially useful data set to be included into our data merging framework is the Oceansat-2 scatterometer (OSCAT). OSCAT also provides Ku-band backscatters akin to QSCAT, but operating in a different period (between 2009 and 2014)  (Bhowmick et al. , 2013). QSCAT operated in full mode between 1999 and 2009, and overlapped with ASCAT during three years  (2007) (2008) (2009).
Acknowledgements We thank the  BYU data center  and  Dr. David Long  for providing the enhanced-resolution radar images. This work was supported by the  National Natural Science Foundation of China  (grant no. 31988102 ); the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  Grant  XDA26010303 ; French grants (CEBA, ref. ANR-10-LABX-25-01 ; TULIP, ref. ANR-10-LABX-0041 ;  ANAEE-France :  ANR-11-INBS-0001 );  CNES  postdoctoral fellowship to S. T. ; and  ESA CCI -BIOMASS. https://doi. org/10. 5194/essd-2022-264 Preprint. Discussion started: 15 August 2022 c Author(s) 2022. CC BY 4. 0  License.
Data availability The CScat data set can be downloaded at https://doi. org/10. 6084/m9. figshare. 20407857. Zhang, H. , Zhang, F. , Che, T. , Yan, W. , and Ye, M. : Investigating the ability of multiple reanalysis datasets to simulate snow depth variability over mainland China from 1981 to 2018, J. Clim. , 34, 9957-9972, 2021. https://doi. org/10. 5194/essd-2022-264 Preprint. Discussion started: 15 August 2022 c Author(s) 2022. CC BY 4. 0 License.
2020). The apollinaire 2  package is designed to provide a readyto-use, consistent, and flexible open-source MCMC peakbagging framework for solar and stellar time series. It has already been used in several publications  (Hill et al. 2021; Breton et al. 2022; Huber et al. 2022; Mathur et al. 2022; Smith et al. 2022 ). The code is fully written in Python 3. Available solar time series being unequaled in quality and length, their specificities were fully taken into account during the development phase of the module. The difficulties of stellar peakbagging were also exhaustively considered in order to provide an automated process for p-mode parameter extraction. Therefore, apollinaire is able to perform fits globally, by order ( = {0, 1, 2, 3}), by pair ( = {0, 2} or {1, 3}), or for a single mode, and to extract parameters such as mode splittings, stellar inclination angles, and the choice of symmetric or asymmetric Lorentzian profiles (see e.
g. Nigam & Kosovichev 1998; Korzennik 2005). Similar automated frameworks with the Interactive Data Language (IDL) or Python interface have already been developed in the past few years (see e. g. Fast and AutoMated pEak bagging with DIAMONDS (FAMED) from  Corsaro et al. 2020 , or PBJam from  Nielsen et al. 2021). With the upcoming PLATO mission at the end of the decade, the existence of a wide diversity of peakbagging open source modules will be a considerable asset for the asteroseismic community, as it will allow non-expert peakbaggers to easily access ready-to-use frameworks for p-mode parameters extraction. The layout of this paper is as follows. Section 2 presents the principles of parameter fitting in a Bayesian framework and extensively describes the set of models that are implemented in apollinaire.
Description of the framework The apollinaire package is designed to perform MCMC samplings for each step of the peakbagging procedure: background fit, asymptotic parameters fit to determine the global modes pattern, and extraction of individual mode parameters. Those three operations are performed one after another when using the stellar_framework function but can also be performed independently. The global flowchart of an analysis performed with the stellar_framework function is represented in Fig. 3. erent parameters. At the same time, the prior function reflects the level of knowledge that the Bayesian fitters already possesses concerning the model for which they want to sample the posterior distribution. From thi order to Discussi eral poss ging lite.
3. 5. When a MCMC is sampled, the code automatically extracts summary statistics from it. For a given parameter, it returns the median of the marginalised sampled distribution, y. In order to obtain the uncertainties σ -and σ + over y, 16th and 84th percentiles y 16 and y 84 are also extracted. In the case of a Gaussian distribution, σ -= σ + = σ with σ being the standard deviation of the distribution. Some apollinaire output files (see Appendix D and online documentation for more details) only provide a σ sym over y: σ sym = max (y -y 16 , y 84 -y). (18) In this case, if it is the natural logarithm of the parameters that has been sampled, median, 16th, and 84th percentiles are transformed again before computing and returning σ sym.
The chains are stored as Hierarchical Data Format version 5 (hdf5) files. The code provides functions to read these files for the user that would need to perform a more thorough analysis on the chains than the extraction of summary statistics described in Sect. 3. 1. Using these files, it is for example straightforward to obtain the marginalised distribution for each parameter or the covariance matrix between two parameters. Corner plots visually summarising the sampled distributions can be saved as pdf or png files if filenames are specified. They represent both the marginalised distribution for each parameter and the covariance distribution for each pair of parameters. Parameters fitted by the perform_mle_ background, perform_mle_pattern, explore_ distribution_background, and explore_distribution_ pattern functions are returned as numpy arrays.
Background fit Besides the PSD, the only additional inputs needed by apollinaire to automatically compute initial guesses and priors for the background fit are the stellar effective temperature, T eff , the asymptotic large spacing ∆ν, and the frequency at maximum power ν max. If no previous estimations of ∆ν and ν max are available, it is possible to provide the code with the stellar mass, M, and radius, R, in order to compute an estimate of ∆ν and ν max through the scaling laws  (Kjeldsen & Bedding 1995)  ∆ν ≈ ∆ν M M 1/2 R R 3/2 ν max ≈ R R 2 M M T eff, T eff 1/2 , ( 19 ) where ∆ν , M , R , and T eff, are the reference solar values for the asymptotic large spacing, mass, radius, and effective temperature, respectively. These values are set to 135 µHz, 1 M , 1 R , and 5770 K in apollinaire.
The limit spectrum fitted on the data is the sum of the background term given by Eq. (  6 ) and a Gaussian p-mode envelope term: S B (ν) = B(ν) + H max exp        - ν -ν max W gauss with H max being the maximal height of the p-mode Gaussian envelope and W gauss its width or standard deviation. When only one Harvey model is considered, the initial γ value is fixed to 2; it is fixed to 4 when two Harvey models are considered. This parameter can be fixed  (Kallinger et al. 2014)  or set to vary. In order to fit more than two Harvey models, the user should manually provide the guesses. Otherwise, input profile guesses are automatically generated. If the spectrum has been acquired with photometric observations, the values provided by Table  2  of  Kallinger et al. (2014)  are used. If the code has to deal with a PSD obtained from a solar radial-velocity time series, initial guess values have also been implemented using GOLF as a reference.
In this situation, the results obtained with the summary statistics alone should also be considered with caution as the inclusion of low-S/N modes can strongly influence the multi-modality of the sampled distribution. An échelle diagram may also be particularly useful in order to check that the code correctly identified the = 0 and the = 1 mode. When this is not the case, the failure in the sampling is generally related to an improper prior for. In this situation, the solution is to manually impose new initial values and prior bounds for.