dataesr/scientific-paragraphs-categorization huggingface dataset:
In 1966, the station was moved roughly 2 km to the east and to an elevation of 18 m. The portion of the homogenized record from 1980 onward is from Pitt Meadows, BC located roughly 14 km east of the previous location and at an elevation of 5 m. Using a composite station is non-ideal given the potential influence of local micro-climate effects and the increasing distance from the cooling effects of The Strait of Georgia. Those effects may increase the uncertainty of our analysis, but given the magnitude of the signal and the consistency of results among the datasets presented here (and analysis of other temperature records in BC, not shown), we accept these issues. The AHCCD dataset is updated annually and ends in 2020. Data for 2021 were appended from unhomogenized recent records from Environment and Climate Change Canada.
While the ocean and ice components have a horizontal resolution of only 1 • , the resolution of the atmosphere and land is about 50 km and might therefore provide a better simulation of certain extreme weather events  (Baldwin et al. , 2019). The data used in this study cover the period from 1860 to 2100, and include both the historical and RCP4. 5 experiments driven by transient radiative forcings from CMIP5  (Taylor et al. , 2011). We also examine five ensemble members of the AMIP experiment (1871-2019) from the GFDL-AM2. 5C360 (Yang et al. , 2021; Chan et al. , 2021) , which consists of the atmosphere and land components of the FLOR model but with horizontal resolution doubled to 25 km for a potentially better representation of extreme events. The project (C20C+) was designed specifically for event attribution studies  (Stone et al.
Acknowledgements. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. We thank  Urs Beyerle  for downloading and curating the CMIP6 data at ETH Zurich. F. L. was supported by the  Regional and Global Model Analysis (RGMA)  component of the Earth and  Environmental System Modeling Program  of the  U. S. Department of Energy's Office of Biological & Environmental Research (BER)  via  National Science Foundation   IA 1947282. We would like to express our deep gratitude and respect to Geert Jan van Oldenborgh, co-author of this manuscript, who sadly passed away on 12th October 2021.
Acknowledgments We thank  Alexander Bentkamp ,  Martin Desharnais ,  Robert Lewis ,  Simon Robillard ,  Anders Schlichtkrull ,  Mark Summerfield ,  Dmitriy Traytel ,  Uwe Waldmann , and the anonymous reviewers for their comments and suggestions. Blanchette's research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 713999 ,  Matryoshka). He  has also received funding from the  Netherlands Organization for Scientific Research (NWO)  under the  Vidi program  (project No. 016. Vidi. 189. 037 , Lean Forward ).
5 ). The comparison is even more difficult that major differences exist between the available zircon records and depending on whether the whole dataset or only juvenile zircons are considered. Continents from 3. 5 Ga onward contain only a small proportion of juvenile crust, indicating that reworking of older felsic crust was a ubiquitous process during most of Earth history. At 3. 7 Ga, the compiled sedimentary data suggest that the continents were fully covered by juvenile crust (Fig. 2C ). This result must be taken with great care because of the small number of sedimentary rocks available for this age bin (n = 7). This could be because the production of juvenile crust was more important at the beginning of the Archean than later on or because there were not a lot of continental rocks to rework by that time, or both.
Sm-Nd database for detrital sedimentary rocks The Sm-Nd database used to estimate the average SiO 2 contents of continents and the relative proportion of juvenile crust through time is available in data S1. The compilation includes 2653 data from 104 publications reporting 147 Sm/ 144 Nd and 143 Nd/ 144 Nd ratios for fine-or coarse-grained sedimentary rocks of all ages. The compiled 147 Sm/ 144 Nd ratios were all analyzed by isotope dilution except in modern sediments (<100 Ma) for which they were calculated from Sm and Nd concentrations. Epsilon values were calculated relative to the chondritic uniform reservoir (CHUR) value of Bouvier et al. (42). Three types of Nd model ages are reported in data S1. They were all calculated with a decay constant of 6. 54 × 10 In the following models, the Sm-Nd database was screened for outliers to remove sediment data with extreme or aberrant values such as 0.
05 > 147 Sm/ 144 Nd > 0. 4, 0 > TDM (or TPM) > 4. 56 Ga, and TDM (or TPM) < stratigraphic age ± 0. 1 Ga. TDM values outside the ranges specified above could reflect a reopening of the Sm-Nd isotopic system after sediment deposition. In the preferred model using TDM 4. 5Ga (depleted mantle extrapolated back in time to the CHUR value at 4. 5 Ga), the outlier rules lead to a removal of 26 outliers from the initial database. The width of the bins to be used in the models was determined using Scott's and Freedman-Diaconis' rules, which are two statistically independent rules used to select the best width of the bins to be used in a histogram. Both rules suggest that a 200-Ma binning is the most appropriate way to group the Sm-Nd data given their distribution. The distribution of fine-and coarse-grained sedimentary rocks per bin is shown in fig.
Relationship between SiO2 contents and 147 Sm/ 144 Nd ratios in igneous rocks The first step of this model is to determine the equation of the regression line that expresses the best relationship between the SiO 2 contents and the 147 Sm/ 144 Nd ratios of igneous rocks (Fig. 1B ). For this purpose, I compiled 147 Sm/ 144 Nd ratios and SiO 2 contents of volcanic and plutonic rocks from Archean cratons, convergent margins, and oceanic plateaus from the GEOROC database on 27 January 2020 (N = 3864). The dataset was screened for outliers to remove data 0. 05 > 147 Sm/ 144 Nd > 0. 4 and 45 wt % > SiO 2 > 75 wt %. This step led to the rejection of 636 data out of the initial dataset. The 147 Sm/ 144 Nd ratios were averaged every 1 wt % of SiO 2 and screened for outlier above and below the mean ± 2 SD for each bin.
12 year -1 for 143 Nd. TDM values correspond to model ages calculated relative to the depleted mantle linearly extra polated back in time to the CHUR value of Bouvier et al. (42) at 4. 5 or 3. 8 Ga from the present-day Nd isotopic composition of MORB [ 143 Nd/ 144 Nd (t = 0) = 0. 513073 ± 0. 000191 (2 SD), which gives Ɛ Nd = +8. 6]. The present-day MORB value was estimated by averaging 3297 Nd isotopic compositions of MORB from the PetdB database (compilation of igneous rocks from spreading center, off-axis spreading center, triple junction, aseismic ridge on 3 March 2020) after removing outliers above and below the mean ± 2 SD. TPM values correspond to model ages calculated relative to the primitive mantle [CHUR reservoir; (42)].
Acknowledgments: I acknowledge  R. W. Carlson ,  N. T. Arndt ,  D. Andrault ,  M. Boyet ,  B. Dhuime ,  J. Monteux ,  E. Bruand , and  L. Sauzéat  for comments on an earlier version of the manuscript and  B. Schoene ,  C. B. Keller , and an anonymous reviewer for helpful and insightful reviews. Funding: This work was funded by the  European Research Council (ERC)  under the European Union's  Horizon H2020 research and innovation programme  ( Starting Grant GOforISOBIF , grant agreement no. 852239 ), the  Swiss National Science Foundation (SNSF) -Ambizione  Grant (no. PZ00P2_161218 ), the  French Government Laboratory of Downloaded  from https://www. science. orgon September 23, 2021.
Evolution of 147 Sm/ 144 Nd ratios and SiO 2 contents in rocks through time B Igneous rocks Modern FELSIC All igneous rocks Archean Mean every 1 wt % of SiO 2 ± 2 SE MAFIC 0. 10 0. 12 0. 16 0. 18 0. 20 147 Sm/ 144 Nd Fig. 1all igneous rocks (green) in (B). Value for modern upper continental crust is from Rudnick and Gao (21). (A) Compiled 147 Sm/ 144 Nd ratios of sedimentary rocks (gray points; data S1). Binned and moving means are shown in pink and purple with error bars. For the moving mean, the length of the sliding window is 200 Ma. The two types of means globally show the same variations through time. (B) Variation of SiO 2 contents as a function of 147 Sm/ 144 Nd ratios in Archean (blue), modern (gray), and all age (green) igneous rocks compiled from the GEOROC database. The best-fit regression lines are shown with 95% confidence interval (shaded areas).
2. We analyzed a dataset of 28 field cereal-legume intercropping trials from various climatic and management conditions across Western Europe. PRA was quantified in both mixing and single-species situations. 3. PRA was positively impacted in specific management conditions, leading to a greater increase in yield for a given increase in plant size. Variations in PRA were more beneficial for legumes grown in unfertilized mixture, which explains their use as a key component in actual intercrop systems. The response for cereals was similar but less pronounced in magnitude, and was greater under limiting resource conditions. Focusing on intercropping conditions, hierarchical competition (indicated by biomass difference between intercropped species) appears as a strong driver of the reproductive output of a given species.
0 and 0. 5 for sole crops and 0. 3 -0. 7 for each of the two intercropped species); and iii) the N fertilization, with non-fertilized and N-fertilized situations, the latter ranging from 30-200 kg N. ha -1 (mean (± SD) = 95 ± 44 kg N. ha -1 ) (Table  1 ). To assess reproductive effort and allometry, all experiments measured at least three variables: grain yield (t. ha -1 ) at harvest, total aboveground biomass (t. ha -1 , including grains, flowers, pods and ears) at maturity, and actual plant density (plant. m -  ). Plant density was used to convert per-ha variables into per-capita variables (i. e. g. plant -1 ; Table  1 ). Table  1. Cropping situations pooled in the database by plant family (Poaceae vs. Fabaceae), crop type (sole crop vs. intercrop) and nitrogen (N) fertilization (non-fertilized N0 vs.
For the same reason, differences between sole crops and intercrops for legumes could be assessed only under non-fertilized conditions. We analyzed reproductive allometric relationships between plant grain yield and plant aboveground biomass thanks to standardized major axis analysis [SMA;  Warton et al. (2006) ;  Poorter and Sack (2012) ] using the R smatr package  (Warton et al. , 2012) , through the power relation y = ax b , where y and x are plant yield and aboveground biomass, respectively. This method enables geometrical interpretations that lead to statistical testing procedures to compare slope, offset and shift along the allometric line  (Warton et al. , 2012; Taskinen and Warton, 2013). More specifically, we assessed the effect of plant family and, within each family, the effect of crop type and N fertilization, on the position of individual plants along the main reproductive allometric line.
Then, considering an intercrop mixing two species i and j, the biomass distance index for the focal species i was defined as x j -x i , and respectively for species j. This index ranges from -1 (i. e. focal species is dominant) to +1 (i. e. focal species is dominated). We used a linear model to analyze change in plant yield as a function of the biomass distance index. The allometric relationships led to centered residuals as the differences between the observed yield and the predicted one from associated biomass. A natural question arose about the impact of the conditions of each experiment on these results. Considering an ANOVA or a mixed model would be unsatisfying due to the unbalanced sizes of each experimental group in the dataset. Thus, an alternative approach was proposed to tackle such a question.
The residual values were plotted separately for each subset of data obtained in the same conditions. Moreover, the p-th quantiles for p=2. 5% and p=97. 5% were drawn to bounds 95% of residual values to visualize possible outliers (Supplementary Material S2). The results indicated that no extreme value appeared as remarkable. Some variability is revealed but its order of magnitude remains below the dispersal of the residuals. The role of the experimental factors appears then as neglectable with respect to the residual variations of the allometric relationships. Data were analyzed with R software version 3. 6. 0 with the packages dplyr [data processing; Wickham et al. (  2019 )], ggplot2 [visualization;  Wickham (2016) ] and knitr [reporting;  Xie (2015) ].
3B ), the threshold was 3. 5 g. plant -1 (CI0. 95 = [2. 0-5. 9 g. plant -1 ]), with biomass ranging from 0. 7-9. 9 g. plant -1. For a cereal under N-fertilized conditions (Fig. 3C ), the threshold was 6. 3 g. plant -1 , with biomass ranging from 2. 6-13. 2 g. plant -1. We could not derive CI0. 95 for this situation because the allometric relationships of the two datasets had high collinearity and widely scattered points. In intercropping conditions, for a given species, we analyzed how variation in its yields depends on biomass distance between the two intercropped species. This distance index significantly explained yield variation in all studied species x management cases (R 0. 39 -0. 48, p < 1e-3, Fig. 4 ). N fertilization also influenced allometric relationships (Fig 3D  -F ). For a given plant biomass, intercropped legumes had higher yield without N fertilization than with it (significant offset, Fig.
Acknowledgements The authors thank all the technical staff who helped to acquire this vast dataset, Michael and  Michelle Corson  for their helpful comments and English revision, and the reviewers and editor for their contribution to highly improve this manuscript. This study was supported by the  French National Research Agency  under the Investments for the  Future Program  ( ANR-16-CONV-0004 ) and by the  INRA Environment and Agronomy Division  through the project  IDEA (Intra-and interspecific diversity mixture in agriculture ). CV was supported by the  European Research Council (ERC)   Starting Grant  Project 'ecophysiological and biophysical constraints on domestication in crop plants' (grant  ERC-StG-2014-639706-CONSTRAINTS ).
Reliable satellite estimates of chlorophyll-a concentration (Chl-a) are needed in coastal waters for applications such as eutrophication monitoring. However, because of the optical complexity of coastal waters, retrieving accurate Chl-a is still challenging. Many algorithms exist and give quite different performance for different optical conditions but there is no clear definition of the limits of applicability of each algorithm and no clear basis for deciding which algorithm to apply to any given image pixel (reflectance spectrum). Poor quality satellite Chla data can easily reach end-users. To remedy this and provide a clear decision on when a specific Chl-a algorithm can be used, we propose simple quality control tests, based on MERIS water leaving reflectance (ρ w ) bands, to determine on a pixel-by-pixel basis if any of three popular and complementary algorithms can be used.
The algorithms being tested are: 1. the OC4 blue-green band ratio algorithm which was designed for open ocean waters; 2. the OC5 algorithm which is based on look-up tables and corrects OC4 overestimation in moderately turbid waters and 3. a near infrared-red (NIR-red) band ratio algorithm designed for eutrophic waters. Using a dataset of 348 in situ Chl-a / MERIS matchups, the conditions for reliable performance of each of the selected algorithms are determined. The approach proposed here looks for the best compromise between the minimization of the relative difference between In situ measurements and satellite estimations and the number of pixels processed. Conditions for a reliable application of OC4 and OC5 depend on ρ w 412/ρ w 443 and ρ w 560, used as proxies of coloured dissolved organic matter and suspended particulate matter (SPM), as compared to ρ w 560/ ρ w 490, used as a proxy for Chl-a.
Conditions for reliable application of the NIR-red band ratio algorithm depend on Chl-a and SPM. These conditions are translated into pixel-based quality control (QC) tests with appropriately chosen thresholds. Results show that by removing data which do not pass QC, the performance of the three selected algorithms is significantly improved. After combining these algorithms, 70% of the dataset could be processed with a median absolute percent difference of 30. 5%. The QC tests and algorithm merging methodology were then tested on four MERIS images of European waters. The OC5 algorithm was found to be suitable for most pixels, except in very turbid and eutrophic waters along the coasts where the NIR-red band ratio algorithm helps to fill the gap. Finally, a test was performed on an OLCI-S3A image.
It has been designed to correct the overestimation of the OC4 algorithm in moderately turbid and/or high CDOM waters and is based on a training dataset from the western Mediterranean Sea, the Bay of Biscay and the English Channel. OC5 LUTs are based on a triplet of entries: the maximum ratio of reflectance blue/green used by the OC4 algorithm and the normalized water leaving radiances at 412 and at 560 nm. The OC5 LUTs were originally developed for the Sea-WiFS sensors  (Gohin et al. , 2002)  but additional LUTs adapted to other ocean colour sensors (MODIS AQUA, MERIS and OLCI) are available. The third algorithm evaluated in this study is the  Gons (1999)  algorithm. It is based on the NIR-red band ratio (i. e. reflectance bands 705 nm and 665 nm) which increases with Chl-a. These types of algorithms are applicable in turbid, eutrophic waters where the NIR-red reflectance is significant enough to be insensitive to radiometric noise and where the effect of red Chl-a absorption is sufficient to be measurable.
Among the variety of NIR-red band ratio algorithms  (Dall'Olmo et al. , 2005; Gitelson et al. , 2007; Le et al. , 2009; Moses et al. , 2009; Gilerson et al. , 2010) , we selected the  Gons (1999)  version because of its semianalytical structure which makes it less dependent on validation datasets compared to empirical algorithms. However, this algorithm is based on some assumptions and a final calibration step. In fact, the  Gons (1999)  algorithm assumes a constant back scattering coefficient in the red and NIR, a negligible absorption from CDOM and non-algal particles compared to water absorption at 672 nm, 704 nm and 750 nm and to water and chlorophyll-a absorption at 672 nm. Finally, a constant chlorophyll-a specific absorption coefficient is assumed. Previously, application criteria based on remote sensing reflectances  (Lee and Hu, 2006; Morel and Bélanger, 2006; Park et al.
(2019)  tested 48 Chl-a algorithms on a dataset covering the 13 optical water types and attempted to provide recommendations for each water type. Nevertheless, as the same reflectance spectra may result from a large diversity of water constituent combinations  (Hieronymi et al. , 2017)  with different absorbing and scattering properties, Chl-a retrieval within the same optical water class can sometimes be rather challenging. To process oligotrophic and eutrophic waters simultaneously, alternative switching algorithms using bluegreen and NIR-red band-ratio algorithms have been proposed  (Gons et al. , 2008; Smith et al. , 2018)  but they have been developed/tested for certain regions and water types (i. e. case 1 waters in  Smith et al. , 2018). Given the large variety of coastal and inland waters and Chl-a algorithms presenting different degrees of complexity, the user can easily be confused when it comes to selecting an appropriate Chl-a algorithm.
A given algorithm may be derived from a limited regional or local data set or even from a synthetic database, and it is not easy to know which algorithms works best in the coastal area that one wants to investigate. The aim of this study is to develop and to provide robust reflectancebased QC tests and a suitable algorithm switching approach for coastal waters in order to choose between OC4, OC5 and Gons (1999) algorithms. QC tests should avoid unreliable algorithm performances whereas the switching approach reduces the overall uncertainties while processing most of the pixels. QC tests are developed with a novel methodology based on percent difference of satellite estimations compared to the respective in situ match-up data. An in situ/satellite matchups dataset was produced using water reflectance from the MEdium Resolution Imaging Spectrometer (MERIS;  Rast et al.
, 1999). The MERIS sensor was selected for the analysis because of its long operational period (from March 2002 to May 2012), which over the years has allowed ESA's MERIS validation team to collect a reasonable number of in situ/satellite reflectance match-up data, covering different water types. ESA have recently launched the Sentinel-3 series of follow-up missions for MERIS, each hosting an Ocean and Land Colour Instrument (OLCI,  Nieke et al. , 2012)  with spectral characteristics are very close to MERIS. In addition, the 709 nm band, present on MERIS and OLCI (but not available for MODIS and VIIRS), is particularly useful for Chl-a estimation in turbid, eutrophic waters. The following sections describe the datasets used and the methodology to determine reflectance-based quality control tests for the OC4, OC5 and NIR-Red band ratio algorithms.
Chl-a in situ/satellite matchups A total of 235 in situ Chl-a and MERIS matchups were extracted from the MERMAID database (http://mermaid. acri. fr/home/home. php, Barker et al. , 2008)  with MEGS 8. 1 processing at reduced resolution (RR, spatial resolution: 1040 m × 1160 m). In situ Chl-a was mostly determined with High Performance Liquid Chromatography (HPLC, 95%) otherwise with spectrophotometry (5%). The Chl-a in situ database was complemented with measurements published in the Coast Colour Round Robin (CCRR) dataset  (Nechad et al. , 2015)  providing in situ optical and biogeochemical data from different coastal regions over the world. In the CCRR dataset, Chl-a was measured by lab fluorometry (57%) and HPLC (43%). Fluorometry measurements were performed after water filtration and extraction in solvent, detailed protocols are provided in  Nechad et al.
, 2015. MERIS match-ups were extracted from MEGS8. 1 RR level 2 products downloaded from the ODESA platform (http://www. odesa-info. eu/info/) for the CCRR in situ Chl-a observations. Reduced resolution is provided by the MERMAID platform for matchups extraction and was used by the MERIS validation team  (Barker et al. , 2008). For the MERMAID and CCRR datasets, MERIS matchups were acquired in a time window of ±2. 5 h from the in situ observation. The median of nine pixels around the location of the in situ measurement was used (macropixels). Data was accepted if at least 3/9 valid pixels were available in the respective macropixel, and if no extreme variation was observed in a micropixel (coefficient of variation lower than 1. 5). The following Level-2 data quality masks were applied to the MEGS 8.