dataesr/scientific-paragraphs-categorization huggingface dataset:
While a careful design of the training dataset can help reduce the effect of correlated environmental gradients, it can never eliminate them completely  (Juggins, 2013). To infer the analog quality as an indicator of non-analog situations, we calculated the minimum dissimilarity (squaredchord distance) between modern pollen assemblages and fossil pollen assemblages with probability thresholds of 1 % (indicating very good analogs), 2. 5 % (good analogs), and 5 % (poor analogs), using the minDC function from the analogue R package (version 0. 17-6;  Simpson et al. , 2021). A statistical significance test  (Telford and Birks, 2011 ) was applied using the randomTF function in the palaeoSig R package  (version 2. 0-3; Telford, 2019). In this test, the proportion of variance in the fossil pollen data explained by the reconstructed environmental variable is estimated from redundancy analysis (RDA) and tested against a null distribution generated by replacing the modern training dataset with randomly generated surrogate fields.
The surrogate fields were simulated to have realistic spatial autocorrelation by fitting variograms to the WorldClim 2 temperature and precipitation data; 1000-member ensembles were simulated for each variable. A reconstruction is considered statistically significant if the reconstructed variable explains more of the variance than 95 % of the random reconstructions  (Telford and Birks, 2011). The reconstructed climate variables were tested as introducing the environmental variable as a single variable in a run, in addition to partialling out the explained variance in the pollen data by the respective other variables. We used Plantaginaceae (mostly representing the Plantago lanceolata type in Europe) and Rumex type to assess human influence as an indicator of intense herding  (Behre, 1988).
In addition, we calculated the correlation between the WA-PLS reconstruction of T July , T ann , and P ann and the pollen percentages of Plantaginaceae and Rumex for 9000, 3000 and 1000 cal years BP to assess potential biases in the dataset. 3 Dataset description LegacyClimate 1. 0: input data, reconstructions, and reconstruction model statistics LegacyClimate 1. 0 provides pollen-based reconstructions and sample-specific reconstruction errors in T ann , T July , and P ann for 2594 fossil pollen records (i. e. , a total of 146 067 single pollen samples) from three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Furthermore, we provide the method-specific model metadata and quality measures for each record and each climate variable (Table  1 ). To ease data handling, the dataset files are separated into western North America, eastern North America, Europe, and Asia.
Dataset assessment 4. 1 Spatial and temporal coverage of LegacyClimate 1. 0 In total, we provide reconstructions for 2594 fossil pollen records. Among them, 670 records are located in eastern North America, 361 records in western North America, 1075 records in Europe, and 488 records in Asia (Fig. 1 ). Some records are included that come from marine cores which were taken from the continental shelf. They contain information from source areas from the nearby continents (e. g. , fluvially transported material). If users want to focus on terrestrial-only records, then those marine sites could be filtered out by the archive type provided in the metadata. Climate reconstructions for one fossil record in the western North American Dataset on Hawaii (dataset ID 17832; Keālia Pond) could not be performed, as there were no modern training data available within a 2000 km area.
Modern relationships between pollen and climate assessed by constrained ordination Results from CCA applied to modern datasets reveal that T July -constrained ordinations have high λ 1 /λ 2 ratios, indicating a strong relationship between this climate variable and modern pollen assemblages in eastern North America, while low ratios can be found in central Asia. The spatial pattern of λ 1 /λ 2 of ordinations constrained by T ann is similar overall to those of T July , but the ratios are slightly higher for T ann than for T July. Reconstructions for P ann show low ratios in Europe and eastern North America. Areas with high ratios are concentrated in Alaska and East Asia (Fig. 3 ).
Assessment of consistency among reconstruction methods Reconstructions with MAT are, in general, in good agreement with those derived from the WA-PLS. Comparing MAT with WA-PLS, 37. 3 % (T July ), 38. 9 % (T ann ), and 30. 4 % (P ann ) of all records have a positive correlation of r 0. 6. Strong positive correlations (r >= 0. 9) can mainly be identified in eastern North America, while a weak correlation can be found for large areas in central North America and most of Europe (Fig. 11 ). WA-PLS_tailored used a reduced modern training dataset (illustrated for an example in Appendix Fig. 2 ). The tailoring successfully reduced the covariation in temperature and precipitation in the modern dataset, as indicated by the distribution of the correlation coefficient in Fig. 12. Nevertheless, the obtained reconstructions are largely consistent between WA-PLS and WA-PLS tailored, with a correlation of r 0.
Data availability The compilation of reconstructed T July , T ann , and P ann is open access and available from PANGAEA (https://doi. org/10. 1594/PANGAEA. 930512; see the "Other version" section;  Herzschuh et al. , 2023a). The dataset files are stored in machine-readable data format (. CSV), which are already separated into western North America, eastern North America, Europe, and Asia for easy access and use.
Pollen from azonal riverine vegetation might be over-represented in fluvially impacted pollen records. Our dataset is based on taxonomically harmonized modern and fossil pollen datasets using a restricted number of taxa. Such an approach guarantees that all records are handled consistently. Although losing taxonomic information when merging taxa together into a higher taxonomic level, it also increases the possibility of matching climate analogs in the modern and the fossil datasets. However, one needs to keep in mind that species with different ecological requirements may be merged together into one genus or family, for example, Pinus species that are restricted to tropical or subtropical areas in China or ones that grow in boreal forests  (Cao et al. , 2013; Tian et al. , 2017). Along with the pollen assemblages, data repositories also provide chronological information for fossil records.
The quality of such chronologies varies strongly with respect to dating methods, calibration, and numerical algorithms for determining an age-depth relationship  (Blois et al. , 2011; Trachsel and Telford, 2017). Having accurate and precise chronologies is thus of pivotal importance for reconstructing past climate in order to identify spatiotemporal patterns and in order to help evaluate climate model outputs. The advantage of the fossil pollen dataset used for the reconstruc-tion presented here (i. e. , LegacyPollen 1. 0;  Herzschuh et al. , 2022b)  is that it has harmonized chronologies (LegacyAge 1. 0), along with information about uncertainties, in addition to related metadata and scripts that allow a customized reestablishment of the chronologies  (Li et al. , 2022). Accordingly, we were able to provide sample-specific age uncertainties along with reconstruction uncertainties.
Modern pollen and climate data sources and LegacyClimate 1. 0 quality We a priori selected T July , T ann , and P ann as target variables for our reconstructions. However, we provide λ 1 /λ 2 (i. e. , explained variance of the climate variable in the modern pollen dataset relative to the variance explained by the unconstrained first axis; ter Braak, 1988), a commonly used proxy for the assessment of reconstructions. The higher the λ 1 /λ 2 in the spatial modern dataset, the higher the chance that this target climate variable has also impacted vegetation over time and is thus reflected in the variation in the fossil pollen dataset. As a rule of thumb, a ratio of 1 is considered to indicate reliable reconstructions  (Juggins, 2013) , though useful reconstructions may also be obtained from datasets with lower values.
As expected, maps of RMSEPs reveal similar spatial patterns as the results of constrained ordination. Our results indicate that calibration sets from Europe in particular have low ratios and a high RMSEP for all climate variables (despite having a high number of modern samples), likely related to the human impact on the modern and fossil data. Some areas that are known for their sensitivity to precipitation, e. g. , East Asia, show low RMSEPs as expected for P ann on the one hand but show a low sensitivity to T ann and T July on the other. For our study we used the, to our knowledge, largest modern dataset ever used in a pollen-based climate reconstruction. For fossil pollen records in areas with an insufficient coverage of modern surface pollen samples (e. g. , central Asia or western Siberia), it might be difficult to create a calibration dataset that maps the required variety of environmental and climatic gradients and therefore find enough modern analogs for reconstructions with a classification approach such as MAT.
This is indicated by the high RMSEPs as percentages of gradient length in these areas. Our routine uses the modern pollen data from within a radius of 2000 km around the site of the fossil record. The information provided in the reconstruction metadata, including number of modern pollen samples and ranges of reconstructed variables, allows an assessment of the modern dataset used for reconstruction. Our assessments of the modern dataset (e. g. , using CCA), the transfer function (e. g. , RMSEP), and the reconstruction (e. g. , the significance test) also revealed the potential biases in the pollenbased reconstruction and pointed to limitations. Further validation and assessments of the results and more comprehensive uncertainty analyses, e. g. , by applying forward modeling approaches  (Izumi and Bartlein, 2016; Parnell et al.
Reconstruction method and LegacyClimate 1. 0 quality Overall, the three reconstruction approaches, MAT, WA-PLS, and WA-PLS_tailored, yield rather similar results (i. e. , indicated by the overall high correlation between the reconstructions of the different methods; Fig. 11 ). Accordingly, the major trends at global or continental scales are similar, even if the actual amplitude of change may vary locally. As each method has its own strengths and weaknesses, there is not one set of reconstructions that is absolutely superior. One advantage of our multi-method reconstruction dataset is that users can identify the methods that are likely to perform best in a selected region and/or specific reconstructions. MAT is often recommended for large-scale studies, but it is highly sensitive to the quality of analogs  (Chevalier et al.
, 2014; Self et al. , 2015; Andrén et al. , 2015; Hébert et al. , 2022). All reconstruction methods used in this study heavily rely on extensive collections of modern assemblage data covering diverse climatic and environmental gradients and are applicable on a broad spatial scale. As discussed, all the methods may struggle with some intrinsic characteristics of pollen data and of pollen compilations, including complex species responses, sensitivity to spatial autocorrelation, and limited analogs that may produce poor results in so-called "quantification deserts"  (Chevalier, 2019) , where fossil pollen is hardly preserved or nearby modern surface pollen samples are missing  (Chevalier et al. , 2020). However, we designed our datasets so that more methods can be included in our reconstruction scripts (https://doi.
org/10. 5281/zenodo. 7887565;  Herzschuh et al. , 2023b) , such as CREST, an approach that combines presence-only occurrence data from species distribu-  tion databases instead of modern pollen samples to estimate the responses of pollen taxa to the climate variable to reconstruct to a climate variable  (Chevalier et al. , 2014; Chevalier, 2022). CREST is, therefore, more independent from the availability of modern pollen samples. Employing inversemodeling through iterative forward modeling (IMIFM;  Izumi and Bartlein, 2016)  might also be possible in such regions. Its use would be particularly interesting to reconstruct the LGM samples because IMIFM is the only technique that can explicitly take the effect of CO 2 on plants into consideration  (Chevalier et al. , 2020). The inclusion of CREST and/or IMIFM in such large-scale studies would complement our multi-model reconstruction ensemble by exploring a larger fraction of the "method uncertainty" space in greater detail (e.
g. ,  Brewer et al. , 2008). Kucera et al. (2005)  propose several metrics for a multi-technique approach to assess the uncertainty space, namely correlations between the residuals (observed minus reconstructed values) and between pairs of techniques are used to investigate the similarity in the reconstructions among different techniques. The correlation between the residuals in seasonal reconstructions (e. g. , summer and winter temperatures; summer and annual temperatures) can be used to investigate the degree of independence of different seasonal reconstructions. Error rate estimates (RMSEP) determined by the cross-validation of the calibration datasets and the leave-one-out method can be used to compare the calibration of individual transfer function techniques, though it should be considered that error estimates may vary with the choice of the cross-validation procedure  (Kucera et al.
Potential use of LegacyClimate 1. 0 Our LegacyPollen 1. 0 fossil pollen synthesis  (Herzschuh et al. , 2022b)  contains records from all over the Northern Hemi-sphere extratropics. We used this synthesis to produce our LegacyClimate 1. 0 reconstruction dataset, which thus can be used to infer spatiotemporal patterns in climate reconstructions that are not limited to a local or regional scale. Although several hemispheric or global reconstruction studies exist, they have been largely restricted to temperature or have included relatively few records  (Marcott et al. , 2013; Marsicek et al. , 2018; Routson et al. , 2019; Kaufman et al. , 2020a, b). Our dataset is therefore a valuable addition. It may be used in a multi-proxy approach, to synthesize marine and terrestrial records in order to assess temperature development during the Holocene, and can help to highlight possible interdependencies between oceans and land masses.
The interconnection between temperature and precipitation  (Trenberth, 2011)  and its spatiotemporal variation across the Northern Hemisphere is therefore an important aspect of evaluating climate models  (Wu et al. , 2013; Hao et al. , 2019; Herzschuh et al. , 2022a). A broad-scale quantitative reconstruction of temperature and precipitation would therefore be of great value for evaluating transient climate model experiments such as TraCE-21K  (He, 2010). Wegener Institute for providing information on pollen records and data uploads. We also thank Cathy Jenks for copy-editing a previous version of this work. Financial support. This research has been supported by the Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie (grant no. 01LP1510C), the European Research Council, H2020 (GlacialLegacy; grant no.
Acknowledgements. We would like to express our gratitude to all the palynologists and geologists, who, either directly or indirectly by providing their work to the Neotoma Paleoecology Database, contributed pollen data and chronologies to the dataset. The work of the data contributors, the data stewards, and the Neotoma community is gratefully acknowledged. We thank  Andrej Andreev ,  Mareike Wieczorek , and  Birgit Heim  from the  Alfred.
predicted, RMSEP, and no. of WA-PLS com- ponents) LegacyClimate 1. 0: quality measures Canonical correlation analysis (CCA) of the modern training dataset Minimum dissimilarities between modern pollen assemblages and fossil pollen assem- blages for each record sample for MAT Statistical significance (see Telford and Birks, 2011) for each site for MAT, WA-PLS, and WA- PLS_tailored.
Acknowledgements. This work was granted access to HPC resources at TGCC under the allocation  A0100412428  and  A0120412428  made by  GENCI  via the DARI procedure. We acknowledge the  CINECA award  under the  ISCRA initiative , for the availability of high performance computing resources and support for the project IsC93. We acknowledge the support of  CNES  for the BepiColombo mission. Part of this work was inspired by discussions within International Team 525: " Modelling Mercury's Dynamic Magnetosphere in Anticipation of BepiColombo " at the  International Space Science Institute, Bern, Switzerland. We acknowledge support by  ESA  within the  PhD project  " Global modelling of Mercury 's outer environment to prepare BepiColombo". J. D. gratefully acknowledges support from  NASA's Solar System Exploration Research Virtual Institute (SSERVI) :  Institute for Modeling Plasmas, Atmosphere, and Cosmic Dust (IMPACT) , the  NASA High-End Computing (HEC) Program  through the  NASA Advanced Supercomputing (NAS) Division at Ames Research Center , and  NASA 's  Rosetta Data Analysis Program , Grant No.
Acknowledgments We thank  Nicolas Michalski  and  Boris Gourévitch  for lending us the CMOS camera and  Brice Bathellier  for the virus sample. This work was supported by a  Human Frontier Science   Program Career Development Award  ( CDA00009/2017-3 ), by the  CNRS   Momentum program  and by the  Fondation pour l'Audition  ( FPA IDA01 ). We acknowledge the support of the  Fondation pour l'Audition  to the  Institut de l'Audition.
Sample Collection The Dirección General de Aguas de Chile (DGA for its acronym in Spanish) has been monitoring a group of lakes in Chile since 1986. Lake Llanquihue is within the selected group because it is the second-largest lake in the Chilean territory and because of its economic-social-cultural importance. The monitoring campaigns carried out in all seasons of the year consisted of sampling and in situ measurements of parameters at eight stations located in the lake (Ll1-Ll8, see Figure  1 ). The collection of field data on physicochemical and biological parameters, as well as water samples for algae and pigment identification, is an essential part of water quality monitoring. The data collected will provide valuable information for understanding the health of this water body and identifying potential environmental threats, such as the possible occurrence of algal blooms.
The orthorectified and corrected images of the terrain of Collection 2 Level 1 were selected considering, the closeness to the sampling date and availability (see Table  S1 ). Considering a previous visual inspection through the Quality Assessment band (QA) and the Region of Interest (ROI), the images were atmospherically corrected in the ACO-LITE software (version 20211124. 0) from https://github. com/acolite and accessed on 10 February 2023. ACOLITE is a generic processor that was developed specifically for marine, coastal, and inland waters, and brings together the atmospheric correction algorithms and software developed at RBINS for processing of images satellites applied to aquatic remote sensing  [18, 42, 43]. ACOLITE uses a default atmospheric correction based on Dark Spectrum Fitting (DSF)  [44] [45] [46]  and Exponential Extrapolation (EXP)  [43, 47, 48]  algorithms.
From the resulting bands representing the surface-level reflectance (ρs) for L-8, the values were extracted in a matrix of 3 × 3 pixels per sampling point, according to  [49]. Pixel values were extracted using ArcGIS software (ESRI's v. 10. 8. 2). Only data from cloud-free areas were used to have high-quality data and to avoid affecting the accuracy of the chlorophyll concentration estimate. These values were obtained from five multispectral bands: blue (B), green (G), red (R), near-infrared (NIR), and shortwave infrared (SWIR). In addition, the values of the Normalized Difference Vegetation Index (NDVI) and Floating Algal Index (FAI) were used. Both indexes are algorithms included in ACOLITE as part of the recovery of parameters derived from reflectance  [50, 51]. The limits of the lake were acquired from the DGA, and only the water body was considered for the analysis (DGA, 2023)  [52].
Raw data were obtained for each station (Figure  1 ). Subsequently, data were resampled at monthly intervals. Autocorrelation (ACF) and Partial Autocorrelation (PACF) functions were computed. Additionally, the Dickey Fuller test  [67]  was employed to examine whether the chlorophyll-a time series exhibited the characteristics of white noise. Subsequently, a seasonal order was applied, and, if necessary, a 1-step differentiation was performed. The optimal hyperparameters were determined through calibration using the pmdarima library (https://github. com/ alkaline-ml/pmdarima, (accessed on 20 February 2023)), and the range of values for calibration was established based on the ACF and PACF results. Finally, the most suitable model was selected based on Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC).
Raw data were obtained for each station (Figure  1 ). Subsequently, data were resampled at monthly intervals. Autocorrelation (ACF) and Partial Autocorrelation (PACF) functions were computed. Additionally, the Dickey Fuller test  [67]  was employed to examine whether the chlorophyll-a time series exhibited the characteristics of white noise. Subsequently, a seasonal order was applied, and, if necessary, a 1-step differentiation was performed. The optimal hyperparameters were determined through calibration using the pmdarima library (https://github. com/alkaline-ml/pmdarima, (accessed on 20 February 2023)), and the range of values for calibration was established based on the ACF and PACF results. Finally, the most suitable model was selected based on Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC).
Long Short-Term Memory (LSTM) Subsequently, Long short-term memory (LSTM) was used, which is a variant of a Recurrent neural network (RNN) proposed by Hochreiter and Schmidhuber in 1997  [68]. This algorithm solves the long-term dependency problem in RNNs by introducing memory (C) and an appropriate gate structure. The LSTM cell (Figure  3 ) has four gates: input (i), forget ( f ), control (c) and output gates (o). The input gate determines the information that can be inserted and transferred to the cell: i t = σ(W i •[h t-1 , x t ] + b i ) (2) ing the training process, tanh is used to scale values in the range of -1 to 1, and b represents the bias in each step. During the training process, a lag of 9 is constructed from the input variables. An LSTM layer with a variable number of cells ranging from 30 to 50 was used based on the size and complexity of the dataset.
The optimizer and loss metric used were Adam and mean squared error, respectively. This configuration has been widely used in previous studies on time-series predictions  [68]. The complete training structure is described in Figure  6. To test the three models described above, cases A, B, and C are defined as follows: Case A (Measurement Data): In the first case, we included the real variables measured in the monitoring campaigns for the four seasons of the year and in the eight stations of the lake. Case B (Measurement and Meteorological Data): In addition to the actual variables, we included meteorological data as conditioning variables that can influence the autochthonous processes of the lake. Case C (Measurement, Meteorological Data and Satellite Data): In this case, we include bands and indices from the L-8 satellite image processing.