dataesr/scientific-paragraphs-categorization huggingface dataset:
We used life history data (developmental stage at 12 weeks, female flowering stage at 12 weeks, height at 16 weeks, fruit number) collected on plants from the 9 focal populations (N = 524) to build predictive models of individual fitness based on phenotypic data (SI: Predicting fitness). These models accounted for a large proportion of the variation in observed population mean fitness, with a correlation of r = 0. 94 between predicted and observed population mean fitness (SI: Predicting fitness). We used these models to predict fitness of the individuals from the remaining 13 populations (and the individuals from the 9 focal populations for which we did not have final fitness data). Results from regressions of fitness ~ distance using data from either the 9 or all 22 populations were very similar (SI: Predicting fitness; Fig.
S3 ); we present results using the 22 populations because these capture a wider geographic sampling of populations and increase statistical power. Thus, the final dataset consisted of population mean fitnesses calculated from 1) observed individual fitness data for individuals from the nine focal populations for which we recorded fruit counts in the field, and individuals that died, and 2) predicted individual fitness for the remaining individuals where final fruit counts were not recorded. We do note that predicting fitness in this way assumes that the relationship between phenotypes and fitness in the nine focal populations holds for the other 13 populations (SI: Predicting fitness; Fig. S2 ).
The unit of analysis in all models below was population mean fitness (i. e. , mean number of fruits produced by individuals). Analyses were conducted in R (v. 4. 1. 2, R Foundation For Statistical Computing 2021) and all data and code to replicate the analyses are available at FigShare (https://doi. org/10. 6084/m9. figshare. c. 6092868. v2). We used tidyverse packages  (Wickham et al. 2019)  for data manipulation and visualization, along with visreg (Breheny and Burchett 2017) for model visualization.
To make Euclidean distance more directly comparable to directional geographic distance, we decomposed Euclidean distance into the absolute values of latitudinal and longitudinal distances between populations and gardens. Hereafter, geographic distance refers to the combination of latitudinal and longitudinal distance either for absolute or directional measures. To aid in interpretability, we transformed these distances in latitude/longitude to distances in kilometers for visualization. Environmental distance is an alternative to geographic distance. Because we were interested in assessing adaptation to climate, we calculated environmental distance between each garden and source population as climatic distance using monthly mean temperature and monthly total precipitation data from 1986-2016 extracted from PRISM (Oregon State University 2020).
We extracted monthly climate data for the growing months (May -Oct) for each source population from 1986 -2015, and for each garden in 2016 (the experiment year). We then calculated mean monthly temperature and precipitation for each year as the average across the six growing months. For source populations, mean temperature and precipitation were averaged over years 1986 -2015. We then calculated temperature distances as the difference between a source population location's long-term mean temperature and the experiment year (2016) temperature of the common garden. Precipitation difference was calculated in the same fashion. We calculated both absolute and directional climatic distances in the same way we calculated geographic distances. See Table  S4  for correlation matrix of all distance metrics.
Acknowledgements We thank  M. Merello ,  L. Peters , the  Missouri Botanical Garden ,  R. Noyes ,  C. Rentschler  and J. Carlson for assistance with seed collections. Y. W Lee ,  S. DelSerra , and  J. Yoon Kim  assisted with data collection in the field. We also thank the following individuals and associated institutions for use of their field stations:  M. Lotsetter  at the  Rosemount Outreach and Research.
2. Comprehensive overview of recent advances using such approaches. The goal here is to categorize and provide statistics and temporal patterns regarding the different models used, the different types of data exploited, and the various forecasting horizons addressed. The references from this manuscript were identified by conducting a keyword-based search in the following databases: Web of Science (https://www. webofscience. com/wos/ alldb/basic-search, accessed on 25 February 2022), Scopus (https://www. scopus. com/ search/form. uri?display=basic#basic, accessed on 25 February 2022), and Google Scholar (https://scholar. google. com/, accessed on 25 February 2022). The most effective keywords were "spatio-temporal solar forecasting", "PV forecasting", "irradiance forecasting", "solar forecasting", "neighboring", "multiple sites" and a combination of the mentioned keywords.
The authors worked with a prediction horizon of up to 1 h and analyzed how the location influenced the ability of the system to achieve better performance, resulting in increased robustness and flexibility, as the model was capable of handling missing data events where information from one or more sensors is not available. Simeunovic et al. [34]  proposed two graph-based deep learning models to capture the spatio-temporal correlation of PV plants with a horizon of 6 h ahead. Two datasets were used, a real dataset with 304 sites and a generated dataset of 1000 sites, with the proposed models displaying overall better results. These models surpassed single-site approaches for horizons above 4 h, since they used data from NWP models for training (which provide better results for longer horizons).
Physical Methods The physical methods considered in this review work are mainly advective models where a spatially resolved description of sky cloudiness is advected in space and time according to a cloud motion vector (CMV), which is often under the assumption that clouds keep a constant shape, optical thickness and motion during the forecast horizon under consideration. While CMV-based forecasting traditionally explore satellite  [6, 7]  and sky-camera  [8, 9]  imagery, this review work only considered publications that implement this method using spatially distributed ground data-the potential for such datasets to infer CMV has already been discussed in  [116, 117]. Table  5  lists the related publications, which are identified in this manuscript. Most works assume the persistence of the CMV for the duration of the considered forecast horizon  [15, 118, 119].
g. ,  [14, 40, 85] ). Gutierrez et al. [103]  subsample a set of 50 weather stations down to 10 based on a circumference with a 55 km diameter-the reduction in stations and even the definition of the diameter of the circumference being driven by the limited processing capacity of the database used. In [89], the authors aim to reduce the dimensionality of the data using a two-step approach. First, a selection of neighboring sites is made based on a distance threshold to the location of interest. Then, the chosen neighboring sites are further filtered based on Akaike information criterion (AIC). Other works explore forecasting models, which intrinsically perform feature selection, such as the LASSO  [14, 17, 30, 78]. In this line, Yang et al. [14]  deployed a LASSO model to process up to 256 inputs (16 lags from 16 ground GHI sensors) and test the impact of varying the number of neighboring sites and lags considered; the use of wind information to select which neighbors to include in the model is also explored.
It is expected that in a world where PV data and higher-resolution satellite information are becoming ever more available, more complex, hybrid solutions combining information from multiple data sources are expected to increase in number. It is relevant to note that the cases where data sources with different spatio-temporal resolution and coverage are used, the models are able to address a broader range of horizons and spatial scales without compromising performance. It is considered that a strong spatio-temporal application requires the creation of very large-scale datasets, far bigger than that considered in current studies, as a model with high generalization capacity requires large amounts of data from varied and representative areas for the training procedure. We found works with datasets that explored a small number of stations (below 5) or works modeling information from a time window of only 25 days-almost half of the works presented used less than 20 locations, which were values hardly representative for the correct training of a model.
Nonetheless, we also found works using satellite data and PV systems that presented a higher density in the network of points used, as discussed in Section 4. 2. We believe it is necessary to ensure the traceability of the data employed and promote the use of datasets provided by public agencies that passed quality controls that guarantee the completeness, consistency, accuracy, and veracity of the information in order to allow predictions closer to the ground truth. Another strong line of improvement would be that the authors openly publish the datasets used in their research and the corresponding quality control considered to enable better comparisons between the performance of the models. We noticed that a considerable part of the works identified address intra-hour forecasting by exploiting high-resolution ground-based data to obtain models with a greater forecasting capability of short-term variability of solar radiation and PV generation.