dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction of the liquid miscibility gap temperature To train Model 3, the 60 descriptors listed in Table  3  are used. When only the data on the 33 binaries in which a liquid miscibility gap is eventually stable are used, performance metrics obtained for different algorithms are summarized in Table  4 , and a scatter plot between the CALPHAD and the predicted miscibility gap temperatures is shown in Fig. 5(a). The predictions are rather inaccurate, with an MAE of 460 K for the RF model. A possible explanation is that the dataset is too small, since it contains 30 elements but only 33 binaries. To investigate this hypothesis, a RF model is built from a larger dataset, which now also includes the data from the 171 entirely metastable miscibility gaps. A lower overall MAE of 356 K is obtained for this new model.
However, for the 33 binaries with a stable miscibility gap, a higher MAE of 580 K is now obtained. It appears from Fig. 5 (b) that higher miscibility gap temperatures are systematically predicted to be lower than those calculated from CALPHAD assessments. It is concluded that the main  limitation of miscibility gap temperature prediction is not the quantity of data, but the quality of data. Experimental data on liquid miscibility gaps are often lacking, especially at high temperatures. As a result, the calculated miscibility gap temperatures in our dataset are often unreliable. For instance, in the Mg-Mn system, a monotectic reaction has been measured at 1471 K  [83] , but no solubility data are available. Two CALPHAD assessments  [83, 84]  have been proposed based on this limited information, and a difference of 1300 K is found between them for the critical temperature at which the miscibility gap closes.
Prediction of the liquidus in 1563 binary systems By combining our three ML models, we predict the liquidus in 1563 binary systems not included in our dataset, many of which are unknown. The prediction results can be found in an openaccess repository  [50]. As discussed in the last paragraph of the introduction, Model 2 is not valid in systems where the elements are always miscible in the stable or metastable liquid phase. Therefore, Model 3 was only used in systems where the probability of having a stable liquid miscibility gap is predicted by Model 1 to be greater or equal to 0. 25. The prediction of two unknown liquidus is shown in Fig. 6  as an example. The Cd-Ce binary is a sys-tem of interest for the reprocessing of nuclear fuels  [85, 86] , and because icosahedral quasicrystals can be formed between Cd and rare earths  [87].
Conclusion In this study, we developed a general framework to predict binary liquidus. The liquidus prediction is broken down into three prediction tasks, which enables to differentiate between miscibility gaps and congruent melting phases while also improving the prediction accuracy. Knowledge of the liquidus is essential for the processing and design of all kinds of materials. With our framework, reasonable estimates of the liquidus and indications of the presence of eutectics and congruent melting phases can be obtained in domains where no data are available. This can be valuable in the early stages of materials development. This is also valuable for developing CAL-PHAD databases, which play an important role in computational materials design. This work also demonstrates how CALPHAD assessments can be used to create unique ML datasets for predicting phase equilibria.
However, to create high-quality datasets, the assessments should only be used within their range of validity and if they are based on sufficient information. This was not checked for in the present study. As a result, while our datasets contain high-quality data validated by experiments, as in the Au-Tb, Cu-Y or Al-Ga cases, it also contains unreliable data, as in the B-Ga case. This was shown to impact the accuracy of the ML models. We are working on improving our datasets, both in terms of quality and quantity, to further increase the performance of our models. Another perspective is to find better features for our models. In this work, the enthalpy of mixing predicted by Miedema's model  [55]  is found to be an important feature for liquidus prediction. Miedema's model is commonly used in machine learning studies focusing on phase equilibria, such as Refs.
1  and Table  1 ). All these samples were collected during the reproductive phase (aquatic phase) and therefore correspond to viable adult individuals. Notably, our survey also includes a selection of 78 syntopic individuals that were previously analyzed using microsatellites by  Johanet et al. (2011) , including those that showed the most substantial rates of introgression. Single-nucleotide polymorphism (SNP). ─ DNA extractions were done on 96 well plates with the DNeasy blood and tissues kit  (QUIAGEN). DNA extracts were diluted to obtain a target concentration of 10-15ng/µL. To identify a set of candidate diagnostic SNPs, we used a total of 1380 nuclear ORF contigs/alignments from transcriptomic data for L. vulgaris and L. montandoni  (Stuglik & Babik 2016). Each of these 1380 alignments encompassed the same 13 individuals: six L.
Among the 1380 alignments examined, only 127 presented sequences meeting the above mentioned specifications and were used to develop 127 multiplexable candidate probes that were preliminarily tested on 20 L. vulgaris and 20 L. 2  for the sequences of these probes). The selected alignments were used to design a 39-plex array to genotype the 1035 samples. The genotyping was subcontracted to the Genome Transcriptome facility at the Center for Functional Genomics in Bordeaux (CGFB), France. Population structure. ─ Sample allocation and admixture level based on the 39 SNPs dataset were assessed with STRUCTURE v2. 2  (Pritchard et al. 2000 , Falush et al 2003)  under models assuming two populations (K=2). STRUCTURE has been shown to be less sensitive to the proportion of hybrids included in the sample, while NEWHYBRIDS (another widely used population genetic programs to address questions related to genetic structure, admixture, and hybridization, Anderson & Thompson 2002) seems to perform slightly better when individuals from both backcross and F 1 hybrid classes are present in the sample  (Vähä & Primmer, 2006).
STRUCTURE assigns individuals probabilistically to clusters based on their multilocus genotype. We estimated posterior distributions based on 3 million MCMC generations of which 50% were discarded as burnin. We used a model that considers the possibility of mixed population ancestry and of correlated allele frequencies among populations due to migration or shared ancestry  (Falush et al 2003) , with an alpha value of 1/k (0. 5)  (Wang 2017). For comparison, the same analyses were repeated on the microsatellite dataset generated by  Johanet et al. (2011, six loci: Tv3ca9, Th09, Tv3Ca19, Th14, Th27, Thca14) , to compare membership coefficients of the 78 specimens for which both SNP and microsatellite data were generated. We used the same options for the microsatellite dataset as with the SNPs.
Results. Samples with three or more missing SNP loci (70 samples) were discarded from the dataset, leading to a final biallelic SNP data-set for 965 usable individual newts, with only 5. 3% of missing data (4,256 undetermined SNP genotypes out of a total of 80,730). From the 39 selected SNPs, two showed shared alleles at significant frequencies: SNP 10797 and SNP 10913. For both loci, the "vulgaris" allele was found in Lissotriton helveticus in low frequencies that were similar between syntopic (3. 3% for SNP 10797 and 13. 2% for SNP 10913) and allopatric populations (4. 0% and 20. 0%, respectively); these two loci are thus not fully diagnostic. In spite of this, the SNPs allowed specific discrimination of every sample, including newly collected as well as previously studied by  Johanet et al (2011) , and no significant sign of introgression between species was detected, i.
e. , species-specific alleles of one species were not found in the other. Only two samples collected in two syntopic localities in France revealed an F1-hybrid genotype: one (sample T9-12-LH8f-2, from Roissy-en-Brie) is heterozygous (vh) for 37 SNP loci (homozygous for the two remaining SNP loci, one of vv type and one hh type) and a second sample (T36-14-24-1, from Ecordal) is heterozygous for all the 38 SNP loci available (genotype at one locus missing). No other signs of hybridization or introgression were detected (Fig. 2A  and Table 1 ). In contrast with the SNP-based results, the sample allocation and admixture level presently inferred from the microsatellite dataset previously generated by Johannet et al (2011) suggest a substantial admixture of genes from the other species (i. e. , ≥ 0.
On the contrary, microsatellites have been designed and selected independently of their level of divergence between species and should thus not overrepresent genomic regions that resist introgression. This hypothesis could be valid if the microsatellite results were able to detect long-term (historical) interspecific gene flow, as this would indeed be reduced in genomic regions involving barrier loci. However it does not apply to the detection of recent admixture (F1 hybrids and backcrosses), in which case the SNP results are expected to be more robust than the microsatellite results due to a larger number of loci (and unambiguous allelic assignment) in the former. Thus, this hypothesis does not seem able to explain the low frequency of F1s and the total absence of F2 or backcrosses in the SNP dataset, in contradiction with the previous study based on microsatellites.
2017 , Szatmári et al, 2020). Assessment of hybridization usually rests, as in the present case, on statistical estimation of the likelihood to observe a given multilocus genotype in a population (assignment methods such as STRUCTURE or NEWHYBRIDS) or comparisons of multivariate axes scores  (Johannet at al. 2011, present study). These assignment methods can produce unreliable results when a small number of microsatellites are used or when there is too much missing data  (Grünwald et al 2017 , Hodel et al. 2017 , Yi & Latch 2022). Analyses of datasets simulated under scenarios with different levels of genetic divergence and varying number of loci have for instance shown that two popular model-based Bayesian methods for detecting hybridization (i. e. STRUCTURE and NEWHYBRIDS) require a significantly higher number of loci than commonly applied in microsatellite-based studies  (Vähä & Primmer 2006).
2017 , Šarhanová, et al. 2018). Additionally, the study by  Johanet et al. (2011)  was affected by a significant amount of microsatellite missing data (22. 2% for the original dataset). Within the 1924 samples involved, 41. 8% were missing at least one third of the data (i. e. , 2 or 3 loci out of a total of six) and 11. 4% were missing half of the data (i. e. , 3 out of 6). When focusing on those samples reanalyzed with SNPs, for which microsatellites suggested substantial admixture of genes from the other species (i. e. , ≥ 0. 1, n=11), examination of the raw data revealed that all of them had at least one missing locus, with an overall mean amount of missing data reaching 34. 8%. Taken together, these considerations prompt us to conclude that the most parsimonious explanation for the discrepancy between  Johanet et al.
(2011)  on the one hand, and Arntzen et al. (  1998 ) and the present study on the other hand, is the poor performance of NEWHYBRIDS (microsatellite analysis by  Johanet et al. 2011)  and STRUCTURE (reanalysis of the same dataset in the present study) to reliably assess ancestry on the basis of an inadequate dataset, characterized both by a small number of hypervariable microsatellite loci and a substantial amount of missing data. Over the past decades, microsatellite genotyping has been the approach favored by the research community to look for hybridization between non-model species. The present study suggests that many of these works may have suffered from biases like those encountered by  Johanet et al (2011) , and may have overestimated the frequency of this phenomenon, as was the case here.
Acknowledgments. We are grateful to Erwan Guichoux (CGFB) and Patricia Sorouille (CEFE) for their technical support. where one of the two hybrids was detected, are illustrated (cf. Table  1  for overall results). (B) Two alternative Bayesian clustering analyses based on the same set of samples from various localities illustrating the differences between microsatellite and SNP genotyping: the structure inferred from the microsatellite dataset suggests significant introgressive hybridization between both species (above, raw data from  Johanet et al. 2011) , whereas on the contrary, the structure inferred from the newly generated SNP dataset does not show any signs of admixture between species (below).
, 2014) , which limits the capability of sampling the precipitation over the full atmospheric column  (Palerme et al. , 2017). Ground-based radar installations, on the other hand, offer a privileged viewpoint on the lower parts of the atmosphere, being instead limited in the spatial coverage of their measurements. The complexity and cost associated with the deployment and maintenance of these instruments in polar conditions resulted in a low number of such installations on the Antarctic continent, especially inland and far from scientific bases. For these reasons, the availability of multiple datasets of ground-based instruments collected in the complex terrain surrounding the Belgian base Princess Elisabeth Antarctica (PEA) during the PEA Orographic Precipitation Experiment (POPE) is a unique opportunity to study precipitation over the region, especially in proximity to the ground, where alternative data source would be unavailable.
The POPE campaign took place between November 2019 and February 2020, and the information on the instruments deployed and the datasets collected is presented in  Ferrone and Berne (2023a). The main novelty of this campaign is the deployment of multiple profilers in remote sites across the region of the Sør Rondane Mountains, directly south of PEA. The base is located in the Queen Maud Land, a sector of East Antarctica bounded between 20 • W and 45 • W of longitude. This area has been the subject of multiple studies highlighting the major role of few considerable precipitation events in the total accumulation  (Braaten, 2000; Schlosser et al. , 2010; Fujita et al. , 2011)  and the consequent effect on the surface mass balance  (Boening et al. , 2012). In this region, the snow accumulation has been increasing in the past century, mitigating the contribution of the ice sheet to sea level rise  (Medley et al.
MRR-PRO The measurements collected by three MRR-PRO deployed along a transect across the Sør Rondane Mountains, reaching a maximum distance of 30 km from PEA, are central to the present study. The locations of the three sites and relevant topographic features are shown in Figure  1. The three profilers and their deployment sites will be denoted by the serial number of each radar, similarly to the article that presents the POPE campaign  (Ferrone and Berne, 2023a). While the latter comprehensively presents the instruments and the datasets, here we will summarize the information relevant to the current analysis: the MRR-PRO 23 has been installed at 1543 m above mean sea level (a. m. s. l. ), the lowest altitude in the transect, in a valley oriented along the North-South axis, in which the Gunnestadbreen glacier lies.
The site is at the eastern edge of a mountain complex, which can be considered upstream of it in the typical north-easterly flow associated with significant snowfall at PEA. the MRR-PRO 06 was placed in a high valley directly North of the mountain Widerøefjellet, at 2000 m a. m. s. l. , approximately 6. 5 km from the previous site. the MRR-PRO 22 has been deployed at the onset of the Antarctic plateau, at 2360 m a. m. s. l. , south (and therefore downstream) of the mountains. It is the highest of the three locations, and its distance from the MRR-PRO 23 and 06 is respectively equal to 17 km and 13 km. The duration of all datasets has been restricted to the period in which all three instruments were deployed and collecting measurements: from 15 December 2019, at 00:00 UTC, to 17 January 2020, at 12:00 UTC.
Limits on the vertical extent of the profiles The first of the radar variables central to this analysis is the equivalent attenuated reflectivity factor, Z ea. Due to measurement inconsistencies in the first 300 m above the instruments, we follow  Ferrone et al. (2022)  and exclude the first 300 m from the analysis of radar profiles. A similar value for the lowest reliable range gates has been used in previous studies using the  MRR-2 (Durán-Alarcón et al. , 2019). For the MRR-2, this limit was dictated by issues in the first couple of range gates, each covering 100 m of the profile. In our MRR-PRO datasets, the behavior may be caused by near-field effects, or the typical drop in power visible in the raw Doppler spectra at the lowest range gates, an intrinsic characteristic of the measurements from this radar.
Another limitation of this MRR-PRO dataset is the existence of artifacts in the measurements. A particular type of these non-meteorological returns is interference lines, characterized by significant peaks in the raw Doppler spectra at a fixed height above the radar, visible in both clear-sky and precipitation conditions. Given their near-constant presence, even in absence of external obstacles, we hypothesize that their origin is internal to the radar. Their characteristics are further discussed in Ferrone et al. (  2022 ). An interference line, not completely removed by the ERUO algorithm, is visible in the MRR-PRO 06 dataset, at approximately 4. 5 km above the first range gate. Since  Ferrone et al. (2022)  shows that none of the three radars recorded meteorological signals above the first 3 km of the profile, a maximum height limit of 3 km above the first range gate of each MRR-PRO has been imposed on the three datasets.
Reflectivity offset correction A comparison of the three MRR-PRO datasets reveals the existence of a significant difference between the Z ea values collected by the MRR-PRO 22 and those from the other two radars in the transect. The likely cause for this difference is a miscalibration of the MRR-PRO 22. Due to the time constraints of the field campaign, we could not directly test the agreement between the three MRR-PRO before or after the campaign at PEA. This lack of comparable measurements limits our ability to precisely correct this significant offset between the radars. Therefore, we decided to follow a different approach, estimating the offset from the sensitivity curve of the three MRR-PRO. By following this method, we assume that the sensitivity of the three radars should be approximately the same across the profiles since their electronic is the same.
Any systematic deviation between the profiles becomes our estimate of the offset. Following  Ferrone and Berne (2023a) , an estimate of 10 dB has been used for the value of the offset, which has been added to the Z ea values collected by the MRR-PRO 22. The main reason why this offset has been computed is to allow the usage of the same threshold on Z ea for postprocessing the three datasets. The uncertainty on the proposed value was deemed too large for comparing derived quantities, such as estimates of precipitation rates, in the context of the current study. Therefore https://doi. org/10. 5194/egusphere-2023-490 Preprint. Discussion started: 16 June 2023 c Author(s) 2023. CC BY 4. 0 License. the analysis presented in the following sections focuses on the occurrence of precipitation at each location, rather than on the accumulation or snowfall rate.
Removal of leftover non-meteorological artifacts Despite the ERUO postprocessing, a few leftover non-meteorological artifacts still affect some of the radar measurements, especially in the MRR-PRO 06 dataset. It should be noted that the amount of interference lines and spurious signals in the raw spectra varies between different MRR-PRO systems. The ERUO postprocessing does not take an aggressive approach to noise removal, prioritizing the preservation of any sensitivity improvement. A cleaner set of variables can be achieved at the cost of losing some of the faintest or most isolated meteorological signals. The current analysis, however, requires a number of false precipitation signals as low as possible, to avoid artificial increases in the detection of virga or surface precipitation over the three sites.
Therefore, the ERUO products underwent a second noiseremoval stage, with stricter conditions than the default ERUO ones. This further refinement starts by imposing two thresholds: the first on the signal-to-noise ratio (SN R), SN R > -10 dB, and the second on the reflectivity factor, Z ea > -10 dBZ. The particularly low value of the minimum accepted SN R value is dictated by how this variable is computed in our dataset. SN R is derived from the ratio between the integral of the signal and the noise floor. The latter is usually high in the MRR-PRO data and, while the peaks associated with the meteorological signal are often clearly distinguishable, their prominence above the noise level can be smaller than the noise level itself. In general, the spurious peaks associated with the leftover non-meteorological signal in the measurements are characterized by low values in both SN R and Z ea , often at the limits of the detection capabilities.
To choose the exact value of the two thresholds, we used the information provided by the pyranometer co-located with the MRR-PRO 06. This instrument measured the downwelling solar irradiance, giving us a way to distinguish between clear-sky and cloudy conditions above the site. Selecting non-null measurements from the MRR-PRO 06 dataset in clear-sky conditions, we were able to identify the typical range of values for the leftover noise in the postprocessed radar data. The chosen thresholds exclude approximately 91% of this noise. From a visual inspection of their time series, it appears that the chosen threshold removes most of the noise also in cloudy conditions. The MRR-PRO 22 and MRR-PRO 23 datasets are less affected, and the amount of spurious signals visible in these datasets is lower than in the MRR-PRO 06 case after the thresholds are applied to both of them.
(2019). Given the limited duration of our datasets, window sizes above 60 min have been tested but not used in the subsequent analysis due to the low number of time steps remaining after the averaging. An inspection of the resulting time series reveals the existence of a few instances in which non-meteorological noise is still visible in the averaged variables, despite the removal described in the previous subsection. If enough noise artifacts appear at the same range gate in the span of a single temporal window, the averaged noise will appear in the final variables. Fortunately, it is an unlikely occurrence, and noise in the final variables appears as spurious non-null values isolated between each other. The first step implemented to remove these artifacts is the exclusion of signals with a duration shorter than 5 min, at each range gate, from the averaging.
This condition takes advantage of the typical temporal continuity of meteorological signals. To exploit the continuity in time and space after the averaging, an approach similar to the one described by  Ferrone et al. (2022)  for the post-processing has been followed. The averaged measurements can be considered analogous to a 2-dimensional matrix, with time on one axis and the vertical distance above the radar on the other. In this matrix, the number of contiguous, non-null measurements can be counted using the image processing capabilities of the Scipy python library  (Virtanen et al. , 2020). We imposed a minimum threshold equal to 3 to this number, excluding in this way isolated artifacts that show no continuity with significant precipitation systems. This procedure may remove some measurements immediately before or after precipitation, which may lack temporal and spatial continuity with the core of the meteorological signal.