dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We are grateful to the officers, crew and scientific team of the  Camadas Finas III  and  Amadeus II research  project for their contribution to the success of the operations. The present study was not possible without the support of all members from  LABZOO  and other laboratories from UFPE and UFRPE. We are grateful to the  Brazilian National Institute of Science and Technology for Tropical Marine Environments (INCT AmbTropic) , the  Brazilian Research Network on Global Climate Change (Rede CLIMA)  and  European Integrated CARBO-CHANGE  and all the scientific team on board of the  Camadas Finas  III cruise. We thank the  CNPq  ( Brazilian National Council for Scientific and Technological Development). This work is a contribution to the LMI TAPIOCA (www. tapioca. ird. fr),  CAPES/COFECUB program , the  European Union 's  Horizon 2020  projects  PADDLE  and  TRIATLAS.
Oyster biomass in the tank had no effect on viral concentration in the seawater, and the addition of this covariate reduced the quality of the model ac cording to Akaike's information criterion (AIC). Therefore, oyster biomass was not further considered. Finally, ANOVAs were used to determine differences in % organic matter, carbohydrate and TAG: ST ratio, according to temperature, food level, line and growth rate. Only main effects were investigated here. For models with statistical significance (p < 0. 05), LSMEAN multiple comparison tests were used to determine differences among treatments. The normality of residuals and homogeneity of variance were graphically checked, and data were log(x + 1) transformed to meet the normality assumption where necessary. Statistics and graphing were all conducted using the SAS 9.
Acknowledgements. We thank the  Ifremer staff at Ifremer Argenton and Bouin  for animal production and maintenance. We are grateful to  Dominique Ratiskol ,  Matthias Huber ,  Isabelle Queau  and  Jacqueline Le Grand  for helping with the experimental set-up; to  Benjamin Morga  and  Nicole Faury  for providing viral suspension; and to  Valérian Le Roy ,  Claudie Quéré  and  Clément Tolleti  for biochemical analyses. We thank  Frédérique Le Roux  and  Andreaz Dupoué  for their insightful comments on an early version of the manuscript. This work is part of the  REVENGE  project led by  Frédérique Le Roux  and funded by the  French National Research Agency  No. ANR-16-CE32-0008.
Dataset 1 was a balanced combination of the depth, solar elevation and sky condition factor levels (3 series × 4 trees × 4 GUs × 3 depth × 3 solar elevations × 2 sky conditions) and was used for model calibration. On the contrary, Dataset 2 was unbalanced for solar elevation and sky condition factors (there were four series at 7:00 under clear sky, five series at 12:00 under clear sky, two series at 17:00 under clear sky and two series at 17:00 under cloudy sky) and was used for model validation.
Data analysis 2. 4. 1 Light variability in the mango tree crown in response to positional and environmental factors Three-way analyses of variance (ANOVAs) were used to test the effects of the positional (depth within the crown) and environmental (sky condition and solar elevation) factors and their second-and thirdorder interactions on the six light variables (PPFD, TrPPFD, UVA-BL, Rn, FRn and z). Light variables were log-transformed prior to analysis to conform to normality, except z for which a square-roottransformation was applied. Analyses were performed on Dataset 1.
e. , independently of the positional and environmental factors, on Dataset 1 using the least-squares method. Model goodness-of-fit was assessed on the calibration data (Dataset 1) with calculation of model bias (systematic errors) and accuracy (random errors). A common bias measurement is the average error ME (Equation  4 ;  Walther and Moore, 2005). It indicates if the model, on average, overestimates (ME > 0) or underestimates (ME < 0) the data. The expected value is 0. A scaled statistic was used to assess model accuracy in terms of the percentage relative root mean square error with mean normalization (RRMSE) (Equation  5 ). Model accuracy is considered excellent when RRMSE < 10%, good if 10% < RRMSE < 20%, fair if 20% < RRMSE < 30%, and poor if RRMSE > 30%  (Despotovic et al. , 2016).
4 )  5 ) where y i is the i th observed value, ŷi is the corresponding value estimated by the model, y is the mean of observed values, and n is the number of observed data. ME = 1 n o n i=1 (b y i -y i ) (Equation RRMSE = 100 y ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 n o n i=1 (b y i -y i ) 2 r (Equation The models were also evaluated by assessing their predictive quality on independent data (Dataset 2). Values predicted with the model were compared to observed values using the previous statistics (ME and RRMSE) and a plot analysis. The regression line between predicted and observed values was computed. The expected values for the slope, the y-intercept and the coefficient of determination of the regression are 1, 0 and 1, respectively. It was then checked if 1 and 0 were included in the 95% confidence interval of the slope and y-intercept, respectively.
When a significant effect of a factor with more than two levels was detected, the same LRT approach was used to perform pairwise comparisons with Bonferroni P-value adjustment for multiple comparisons in order to identify which pair(s) of curves significantly differed. Covariance and LRT analyses described above were performed on Dataset 1 in two steps. First, the analyses were performed separately for each factor. All data were used when testing the effects of sky condition and solar elevation. However, only the data subset corresponding to the highest solar elevation (local time = 12:00) was used when testing the effect of depth within the crown in order to have direct, vertical and unmodified light beams reaching the tree crown, and without light beams passing through the crown of the adjacent tree crowns at lower solar elevation.
Second, the effect of solar elevation was separately tested under clear and cloudy sky conditions because it was assumed that the effect of solar elevation on the functional relationships was more likely under the clear than the cloudy sky condition. Indeed, directional distribution of light beams is anisotropic under the clear sky condition and isotropic under the cloudy sky condition. Light beams are therefore more homogeneous between the different solar elevations under cloudy than clear sky. Statistical analyses were performed with R software, version 4. 1. 2  (R Core Team, 2021). Pairwise comparisons of estimated slopes and y-intercepts were performed with the emtrends() and emmeans() functions, respectively, of the emmeans package  (Lenth, 2016). The Schnute non-linear equation was fitted with the nxlb() function in the nlmrt package  (Nash, 2014).
Light variability in the mango tree crown in response to positional and environmental factors During the measurement series (Dataset 1), incident PPFD ranged from 119 to 2383 μmol m -2 s -1 , with an average (± standard error, s. e. ) value of 934 (± 17) μmol m -2 s -1. PPFD measured within the mango tree crown ranged from 1 to 2221 μmol m -2 s -1 , with an average value of 136 (± 10) μmol m -2 s -1 TrPPFD ranged from 0. 001 to 0. 94, with an average value of 0. 107 (± 0. 005). UVA-BL ranged from 0. 2 to 767. 5 μmol m -2 s -1 , with an average value of 47. 8 (± 3. 7) μmol m -2 s -1. Rn ranged from 0. 01 to 77. 6 μmol m -2 s -1 with an average value of 4. 66 (± 0. 36) μmol m -2 s -1. FRn ranged from 0. 10 to 73. 0 μmol m -2 s -1 with an average value of 5. 02 (± 0. 32) μmol m -2 s -1. Values of z varied from 0.
Calibration and validation of the functional relationships Fits of the linear models linking MAR variables UVA-BL, Rn and FRn to PPFD on the calibration Dataset 1 were highly significant (r² > 0. 99; P < 0. 001), with little variability around the fit (Table  2 ; Supplementary Figure  S3 ). Goodness-of-fit indicators showed that the models were not biased (ME null or close to 0) and had an excellent (Rn) to good (FRn and UVA-BL) accuracy (RRMSE < 20%). The fit of the non-linear Schnute equation linking z to TrPPFD on the calibration Dataset 1 was highly significant (r² = 0. 92; P < 0. 001, Figure  3 ). Goodness-of-fit indicators showed that the model was not biased (ME = 0. 002) and had a good accuracy (RRMSE = 17. 9%). The validation of the functional relationships linking MAR variables to PPFD and z to TrPPFD on the independent Dataset 2 revealed the quality and the relevance of these relationships (Table  3 ; Supplementary Figure  S3  & S4 ).
When TrPPFD ≤ 0. 30 (except for very small TrPPFD values), z was lower at low solar elevation in the morning (7:00) and in the afternoon (17:00) than at high solar elevation (12:00). The maximal difference was 0. 08 at TrPPFD = 0. 10, and 0. 07 at TrPPFD = 0. 14 for the 7:00 and 17:00 levels, respectively. When TrPPFD > 0. 30, z became higher at low solar elevation in the morning (7:00) than at high solar elevation (12:00), whereas at low solar elevation in the afternoon (17:00), it remained lower than at high solar elevation (12:00). The functional relationships estimated for the three depths within the crown at the highest solar elevation (12:00) were also TABLE  3  Statistics of model validation performed on the independent Dataset 2 for the linear models (Equation  1 ) predicting narrowband red (Rn), narrowband far-red (FRn) and UVA-blue (UVA-BL) as a function of the photosynthetic photon flux density (PPFD), and for the simplified Schnute nonlinear equation (Equation  3 ) predicting the Rn : FRn ratio (z) as a function of the relative transmitted PPFD (TrPPFD) in mango tree crowns (Slope, yintercept, r 2 : slope, y-intercept and coefficient of determination of the regression line between predicted and observed values; 95% CI: 95% confidence interval around the slope and y-intercept estimates; ME, mean error; RRMSE, percentage relative root mean square error).
The non-linear functional relationship linking z to TrPPFD was affected differently by solar elevation under clear or cloudy sky (Table  7 ). Under clear sky, the relationships estimated for each solar elevation were significantly different from each other. Under cloudy sky, the relationships estimated at low solar elevation in the morning (7:00) and at high solar elevation (12:00) were not significantly different, and both differed from the relationship estimated at low solar elevation in the afternoon (17:00). As for the previous result obtained on the complete dataset (Table  6 ), the relationships estimated at low solar elevation in the morning and in the afternoon were significantly different, independently of the sky condition.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was carried out as part of the CIRAD DPP COSAQ (activities 2018-2021) and DPP SADUR (activities 2022-2023) agronomical research programs funded by the European Community (ERDF fund) and the Conseil Reǵional de la Reúnion. The funding sources had no involvement in study design, in the collection, analysis or interpretation of data, in the writing of the report, or in the decision to submit the article for publication.
Acknowledgments The authors would like to thank all those involved in landscape tool development and implementation, especially national health authorities and focal points from all related sectors, PAHO country offices, FAO and WOAH local/regional representations,  Carmen Bullon  ( FAO HQ ),  Camille Loi  (WOAH HQ legal),  Adriana Gardina  (PAHO),  Jeannette Danty  (FAO),  Marcos Cunha  (PANAFTOSA) and  Ivan Alvarez  (previously PAHO). Very special thanks go to the consultant teams who led the country landscape analyses:  Maria David ,  Mercedes Flores ,  Emilio León ,  Jéssica Lopes ,  Daniel Marcano ,  Roberto Melano ,  Zaidi Mussaret ,  Jorge Reyes ,  Ximena Riesco-Cruzat ,  José Villacis , and  Nicolas Winter.
Results Choice of the template protein and identification of residues for randomization. CheY, a response regulator, was chosen as initial scaffold to construct the library. Response regulators are proteins involved in prokaryotic two-components transduction systems and have evolved as versatile protein interaction modules regulated by phosphorylation  29. The CheY (pdb: 2TMY) protein from the thermophilic microorganism Thermotoga maritima  30, 31  has been characterized in detail. It is a relatively small (125 aa, 13. 2 kDa) monomeric and highly thermostable (Tm = 95 °C) protein. The protein is efficiently expressed in E. coli (20 mg of Che Y protein/100 mg of protein from the cytoplasmic supernatant). The overall fold, common to all phosphoacceptor receiver domain (REC) of response regulators proteins is made from a five-stranded parallel β-sheet surrounded by five α-helices (Fig.
1a ). The exposed loops are involved in mediating the interaction both with the upstream kinase and the downstream effector domain of response regulators  32. Due to their biological function, these loops are accessible for interaction with protein partners. For randomization, we selected four structurally contiguous beta-to-alpha connecting loops as the flexible shape of this surface appears well-adapted to fit in pockets such as active sites of enzyme  33, 34. To build the novel library based on CheY, 12 residues were identified to be randomized corresponding to positions 10, 11, 12, 57, 58, 59, 60, 84, 85, 86, 105, 107 in the protein final sequence (Fig. 1b ). Some mutations were also introduced in the CheY sequence (GenBank: AAA96389) before randomization: the previously described substitutions R13N and D64R were introduced to stabilize the CheY-fold  35.
0) display the expected in-frame nucleotide sequences while the remaining 45% encoded incorrect sequences (errors in the oligonucleotide assembly and/or frame shift in the sequences). New enrichment procedure to capture the ensemble of folded variants. The proportion of incorrect sequences remained high in the Lib-Cheytins 1. 0 and had to be improved. Furthermore, an unknown proportion of coding nucleotide sequences could result in highly destabilized proteins, unable to give rise to specific binders. To solve both of these potential difficulties an innovative "conformational filtration" procedure based on phage display selection with a specific protein partner was devised. The CheY protein has a biological partner CheA composed of 5 domains P1-P5  46. The P2 domain (PDB code: 1U0S_A) is directly involved in the interaction with CheY.
Differential scanning calorimetry. Thermal stability of the Cheytin-variants was performed by DSC on a MicroCal VP-Capillary DSC Calorimeter from Malvern in a standard buffer. Each measurement was preceded by a baseline scan with the standard buffer. Scans were performed at 1 K min -1 between 20 and 110 °C. The heat capacity of the buffer was subtracted from that of the protein sample before analysis. Thermodynamic parameters were determined by fitting the data with the following equation: where K D is the equilibrium constant for a two-state process, ΔH vH is the enthalpy calculated on the basis of a two-state process and ΔH cal is the measured enthalpy.
Isothermal titration calorimetry. The binding parameters were measured with an ITC 200 microcalorimeter (MicoCal, Malvern) at 25 °C. 2 μL aliquots of the titrant were injected from a computer-controlled 40 μL microsyringe at intervals of 180 s into the solution of target dissolved in relevant buffer (stirring at 800 rpm). The data were integrated and analyzed using the MicroCal Origin software provided by the manufacturer according to the one-binding-site model. Phage display procedure. The final library 2. 1 was used for the selection of binders against the Kaz protein. Phages were prepared from the library Lib-Cheytins 2. 1 as described in  20. Briefly XL1-Blue MRF' bacteria corresponding to the phagemid libraries were infected with the helper phage (M13KO1)  60. After replication of phages overnight at 30 °C, the cultures were centrifuged at 5000 g for 30 min.
Acknowledgements This work was supported by funds from the  Centre National de la Recherche Scientifique, University Paris-Saclay  ( UMR9198 ). The present work has benefited from: the facilities and expertise of the I2BC platform PIM supported by  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INBS-05 ; the facilities and expertise of the I2BC proteomics platform ( Proteomique-Gif , SICaPS, supported by  IBiSA ,  Ile de France Region ,  Plan Cancer ,  CNRS  and  Paris-Saclay University ). We thank  L. Sago ,  D. Cornu  and  V. Redeker  ( I2BC, Gif-sur-Yvette ) for technical assistance and discussions for the mass spectrometry experiment. MG was supported by a  CIFRE (BioXtal)  fellowship. FG was supported by  ANR  ( ANR-18-PRC44 ).
Using 134 eruptive events from the new Independent Volcanic Eruption Source Parameter Archive (IVESPA, v1. 0), we explore empirical MER-height relationships for four measures of column height: spreading level, sulfur dioxide height, and top height from direct observations and as reconstructed from deposits. These relationships show significant differences and highlight limitations of empirical models currently used in operational and research applications. The roles of atmospheric stratification, wind, and humidity remain challenging to detect across the wide range of eruptive conditions spanned in IVESPA, ultimately resulting in empirical relationships outperforming analytical models that account for atmospheric conditions. This finding highlights challenges in constraining the MER-height relation using heterogeneous observations and empirical models, which reinforces the need for improved eruption source parameter data sets and physics-based models.
Plain Language Summary Explosive volcanic eruptions expel gas and tephra in the form of a volcanic column (or plume) that rises into the atmosphere. Two important metrics characterizing these eruptions are the maximum rise height and the eruptive intensity, that is, the rate at which material is emitted from the eruptive vent. Understanding the relationship between these parameters is critical for reconstructing past volcanic events and managing hazards during volcanic crises. In this study, we use a new database of well-characterized eruptions to constrain simple relationships between column height and eruptive intensity. We distinguish four different measurements of column height: the maximum height reached by tephra from observations and from analysis of deposits, the height at which ash spreads in the atmosphere, and the height reached by volcanic sulfur gases.
, 1978). Development of these empirical relationshipsand eruptive column models in general (1D, 3D)-is limited by data sets with a narrow range of eruptive and atmospheric parameters, absent or sparse information on uncertainty, and the use of dependent data, for example, when MER is derived from height. To address these issues, the Commission on Tephra Hazard Modeling of the International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI) developed the Independent Volcanic Eruption Source Parameter Archive (IVESPA,  Aubry et al. , 2021). Here, we use IVESPA to explore new empirical relationships between MER and the height of both tephra and SO 2 eruption columns and compare these results with analytical scaling models that account for atmospheric conditions.
Accounting for Atmospheric Conditions Using Analytical Scaling Models This result is not explained by the use of the full IVESPA data set to both calibrate and test models (including the empirical power-law). If we split IVESPA into distinct calibration and evaluation data sets, we find a 99. 6% probability that the   2 adj of the empirical power law exceeds that of any analytical scalings (Table  S5  in Supporting Information S1). The poor performance of analytical scaling relationships could be explained by poorly constrained parameter values in IVESPA, or the fact that multiple events from one eruption dominate the database. We find that weighting the data (Table  1 , column 4-7) to account for these factors does not change the main results: (a) the empirical power law fit between  top and  MER outperforms (higher  2 adj ) the analytical models; and (b) the best-performing model is the empirical power law that includes   and   terms, with a positive (unphysical) exponent for.
Future Eruption Data Requirements and IVESPA Developments The challenging detection of atmospheric influence on the MER-height relationship in IVESPA v1. 0 may be due to the use of simple scaling (0D) models, and future studies could investigate application of more sophisticated eruptive column models (1D, 3D) or data analysis techniques (e. g. , machine learning) to IVESPA. However, our study hints at developments of IVESPA, and eruptive data more generally, that will help build a better understanding of the relationship between MER, column height and atmospheric conditions. First, Figure  3b  shows that future versions of IVESPA should explicitly separate column heights according to measurement type. Second, Figure  3a  and other studies suggest that compiling information such as magma composition or type (e.
g. ,  Trancoso et al. , 2022)  and conduit information (e. g. ,  Gouhier et al. , 2019)  would help constrain other factors modulating the relationship between height and MER. Whether interpolation of large-scale reanalysis data sets to infer local atmospheric profile is adequate for plume modeling could be further tested. Last, the use of time-averaged eruption source parameters might prevent the detection of atmospheric influence on plume dynamics in a database with such a variety of eruptions. Advances in near real-time measurements of MER  (Bear-Crozier et al. , 2020; Caudron et al. , 2015; Freret-Lorgeril et al. , 2018 , 2021; Mereu et al. , 2022 Mereu et al. , , 2023) )  are critical to build time-dependencies in global databases like IVESPA. These database development should aid understanding of the dynamics of volcanic plumes, and in particular detect and model the influence of atmospheric conditions (Section 5.
The newly calibrated power law relationship between  top and  MER (Equation  1 ) still results in discrepancies of 50% for predicted  top , and a factor of ∼6 for predicted  MER. However, it still outperforms analytical scaling models accounting for atmospheric wind and stratification. It is important to note that empirical relationships will always provide information within the range of eruptions considered and will evolve depending on the data set considered, while analytical models are designed to be of wider application. Discrepancies in the analytical scaling shown in our analysis might be related to both uncertainty in the data and uncertainty in the existing models. More work is required to better interpret these results and provide more accurate models. Further improvements to IVESPA might be needed to detect atmospheric influences on plume dynamics, but we may simply be identifying an inherent limitation in the accuracy with which secondary controls on plume dynamics can be captured in global databases using time-averaged plume heights or erupted mass by deposit mapping.
We sincerely thank Natalia Deligne and two anonymous reviewers who helped us improve the manuscript, Frances Beckett and Marcus Bursik for helpful discussions, Christian Huber for editing this paper. TJA acknowledges support from the Sidney Sussex College of the University of Cambridge during completion of this work. SLE was supported by the NC-ODA Grant NE/R000069/1: Geoscience for Sustainable Futures and publishes with permission of the CEO, British Geological Survey. LGE and ARVE acknowledge support from the U. S. Geological Survey. This is LabEx ClerVolc contribution number 570. IAT and RGG's effort was partly supported by NERC Grants VPLUS (NE/S004025/1) and R4-Ash (NE/S003843/1) as well as the NERC Centre for the Observation and Modeling of Earthquakes, Volcanoes and Tectonics (COMET).