dataesr/scientific-paragraphs-categorization huggingface dataset:
Descriptive sensory analysis In order to conduct sensorial analysis at the end of alcoholic fermentation, the three biological replicates of each Chardonnay conditions were pooled to have enough volume per sample. Once pooled, the samples were filtered and sulfited at 40 mg. l -1 before bottling. The samples were stored in a cellar at constant temperature (16°C) for 6 months until sensory analysis. Ten trained assessors were recruited for this study (four females and six males; median age, 40; range: 25-56). The ten assessors attended preliminary session to check their repeatability and their ability to recognize the typical attributes of Chardonnay 1 https://github. com/daniellyz/MXSAlign wines. Sensory analyses took place in a sensory room equipped with individual booths. Each sample was assessed in ISO standardized black glasses covered with a plastic Petri dish and coded by a three-digit random number.
Thirty milliliter of each wine was served at room temperature (20°C). The samples were assessed in a specific order for each panelist following a Latin square. For each sample, the assessors were asked to smell each samples and to rate the ortonasal intensity of following previously selected smell attributes to best describe the samples (translated from French): "white fruits, " "vanilla, " "yellow fruits, " "candied fruits, " "citrus, " "oxidation, " "pineapple, " "floral, " "amylic, " "polish, " "vegetal, " "reduction" on an eight-point scale ranging between "absent or missing" (score = 0) and "very strong/very intense" (score = 7) (Supplementary Figure  S2 ). The sensory data were subjected to an ANOVA and Newman Keuls test (α = 0. 05). Sensory statistical analyses were performed using R software (R-4.
Non volatile metabolome analysis Chardonnay wine compositions were analyzed by LC-q-TOF-MS. Detected features were extracted from the complete dataset of all the modalities in both positive and negative ionization modes. Of these, the unique masses were excluded for further data processing, resulting in 2054, 2054 and 2,637 features retained for modalities S2S3, S4S3 and S8S3, respectively. Putative annotation was assigned for each extracted feature using an online database (Metlin, KEGG) and the online tool Oligonet. Only features with a putative annotation (annotation Level 3) were retained for subsequent data processing. Principal component analysis (PCA) was performed on LC-q-TOF-MS data for each modality (Figure  5A ). All of these representations confirmed the close proximity of biological replicates for a given sample.
Considering the S4S3 modality, among the 101 biomarkers highlighted and associated with putative annotation, 66 were fragmented and 28 annotated according to the Metlin database (Supplementary Table  S5 ) which allows the annotation of metabolites according to their mass and associated fragments from MS/MS analysis (Supplementary Table  S6 ). Of these 28 compounds, 3 were identified using the standard comparison. Leucine was found to be more intense for the S3 pure culture while catechin and quercetin-O-glucoyranoside were significantly increased for the coculture. Although, only 3 compounds could be identified, fragmentation allowed us to localize biomarkers in the associated metabolic pathways. The annotated biomarkers were grouped into 18 metabolic pathways, classically described in S. cerevisiae, for the coculture of the S4S3 modality.
Serological detection and genome sequencing The DAS-ELISA tests were performed with polyclonal antibodies targeting the RYMV coat protein (CP) on the leaf samples as described previously  (N'Guessan et al. , 2001)  to confirm the infection status of symptomatic leaves. To obtain the coat protein (CP) gene (ORF4; 720 nt) or the complete genome (ca. 4,45kb) sequences, total RNA from infected leaves were first extracted using GeneJET Plant RNA Purification Kit (ThermoFisher SCIENTIFIC, MA, USA). Then, the CP genes and/or the complete genomes were specifically amplified by RT-PCR as previously described  (Pinel et al. , 2000). The RT-PCR products were sequenced using the Sanger technique (Genewiz, NJ, USA) and the sequences were deposited in GenBank (Accession Nos. from OQ200610 to OQ200655 for CP, from OQ225940 to OQ225946 for complete genomes; Table  S2 ).
We reconstructed Maximum-likelihood (ML) phylogenetic trees with SEAVIEW using the bestfitted nucleotide substitution models (Tamura 3-parameter+G+I and GTR+G+I for the CP and the complete genome datasets, respectively) determined with MEGA6  (Tamura et al. , 2013)  and 100 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn. com/abstract=4352956 P r e p r i n t n o t p e e r r e v i e w e d bootstrap replications. Phylogenetic trees were drawn using FigTree v1. 3. 1 (http://tree. bio. ed. ac. uk/software/figtree/). The genetic diversity of the ORF4 dataset was estimated using the best-fitted nucleotide substitution model, with standard errors of each measure based on 1000 replicate bootstraps, as implemented in MEGA6.
Tests of temporal signal In order to examine the degree of temporal signal in the CP dataset, we first used an exploratory linear regression approach  (Duchêne et al. , 2015; Murray et al. , 2016). Based on ML phylogenetic tree reconstructed with the CP dataset, the temporal signal of this sequence dataset was visualized and tested in PhyloStemS  (Doizy et al. , 2020)  to regress phylogenetic root-to-tip distances against sampling date using the root that minimized the residual mean squares. Then, the significance of the temporal signal was evaluated by a date-randomization test. Thus, the mean rate and its 95% highest probability density (HPD) estimated with the observed sampled dates using the Bayesian Evolutionary Analysis Sampling Trees BEAST v1. 10. 4 package  (Suchard et al. , 2018) , see below) were compared with a null distribution obtained by randomly permutating the tip dates 10 times  (Firth et al.
Bayesian evolutionary inferences We reconstructed time-calibrated epidemic histories of the RYMV in West Africa based on the CP dataset using a Bayesian statistical framework implemented in BEAST version 1. 10. 4 software  (Suchard et al. , 2018)  and the BEAGLE library  (Ayres et al. , 2012)  to improve computational performance. BEAST program uses Markov chain Monte Carlo (MCMC) integration to average over all plausible evolutionary histories for the data, as reflected by the posterior probability. The nucleotide substitution process was reconstructed using HKY85+G substitution model, the lognormal relaxed clock model and an initial substitution rate of 10 -3 substitutions/site/year as previously determined for RYMV  (Dellicour et al. , 2018; Issaka et al. , 2021; Trovão et al. , 2015). This preprint research paper has not been peer reviewed.
7. 2 (http://tree. bio. ed. ac. uk/software/tracer). A maximum clade credibility (MCC) tree was summarized from the posterior tree distribution using TreeAnnotator v1. 10. 4 (BEAST v1. 10. 4 package) and visualized with FigTree v1. 3. 1. The dates and the 95% HPD intervals of RYMV movements (introduction or release of RYMV to/from Ghana) were estimated as the interval between the nodes from the same clade with different geographical locations as identified in the MCC tree. The visualization of the continuous phylogeographic reconstruction of the dispersal history of RYMV into West Africa and Ghana were performed with R functions available in the package "SERAPHIM"  (Dellicour et al. , 2016).
Genetic diversity of RYMV in Ghana All positive samples collected in 2010, 2012 and 2020 were used to analyze the genetic diversity of RYMV in Ghana. The coat protein gene was successfully amplified by RT-PCR and sequenced for 46 RYMV isolates (Table  S2 ). These sequences were compared to the 261 CP gene sequences previously published  (Issaka et al. , 2021)  coming from isolates collected in 15 West-African countries between 1975 and 2018, including one CP sequence from an isolate collected in 1995 in Ghana. This dataset is thereafter referred to as the WA307 dataset. Based on this CP dataset, phylogenetic analysis showed that almost all RYMV isolates sampled in Ghana clustered within the S2 strain (Fig. S1 , Fig. 1C ), a strain which was previously reported in Ghana in 1995 (Gh1 isolate) and detected in several countries surrounding Ghana (Ivory Coast, Guinea, Mali, Burkina Faso and Benin).
The genetic diversity of S2 RYMV isolates from Ghana is similar to that observed for the S2 isolates from Ivory Coast and Guinea, and significantly higher than those from Mali or Benin (Table  1 ). In addition to the S2 strain, we identified that two isolates (out of 5) from the Northern region and one isolate (out of 5) from the Eastern region belong to the S1ca strain (Fig. 1C , isolates 2020Gh113, 2020Gh139 and 2020Gh140 in Fig. S1  and Table  S1 ). The genetic diversity of S1ca RYMV isolates from Ghana is similar to that observed in most of West-African countries (Table  1 ). The PHI test did not find statistically significant evidence for recombination within the WA307 dataset (p = 0. 93). Based on this CP phylogenetic tree, seven isolates representing the genetic diversity of RYMV in Ghana were selected to be fully-sequenced (S2 strain: 2010Gh47, 2010Gh55, 2020Gh106, 2020Gh109 and 2020Gh112; S1ca strain: 2020Gh113 and 2020Gh140) and then compared to the 37 complete genome sequences from West Africa.
This dataset is thereafter referred to as the WA44 dataset. The reconstruction of the phylogenetic tree by maximum likelihood confirmed that these isolates from Ghana clustered within the S2 and S1ca strains and demonstrated that these isolates are not directly genetically related between each other (Fig. 2 ). No statistically This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn. com/abstract=4352956 P r e p r i n t n o t p e e r r e v i e w e d significant evidence for recombination was detected in the RYMV genomes from Ghana (data not shown). Altogether, these results showed that the genetic diversity of the S2 and S1ca isolates from Ghana is representative of those circulating in West Africa, which suggests that several introductions of RYMV to Ghana occurred and account for the genetic diversity in this country.
Epidemic histories of RYMV in Ghana To reconstruct the evolutionary history of RYMV in West Africa and to gain information on the epidemic histories and trajectories of this virus in Ghana, we first examined the strength of the temporal signal of the WA307 sequence dataset. The linear regression exploration of root-to-tip distances as a function of sampling time suggested a weak but consistent temporal signal (Fig. S2 ), with a low correlation coefficient (adjusted R 2 <0. 1; p<10 -6 ). The presence of temporal signal was then confirmed consistently using BEAST and date-permutation tests (tip-date and clustered tip randomizations). Actually, the estimates of the permutations did not overlap those of the real (non-permutated) WA307 dataset (Fig. S3 ). As no signal for recombination event was detected on the WA307 dataset, we reconstructed the evolutionary and the dispersal histories of the RYMV in West Africa by Bayesian inferences.
2 , Fig. S1 ). Surprisingly, the genetic diversity detected with the CP gene sequences of the S2 isolates in Ghana is high, which is similar to that reported for the whole S2 strain dataset (96 isolates) from Ivory Coast, Guinea, Mali, Benin and Burkina Faso (Table1). This observation suggests that the RYMV isolates collected in Ghana are representative of the genetic diversity of the S2 strain in West Africa, which reflect the complex epidemiological history of RYMV in Ghana. In addition, the field survey in 2020 identified three isolates (out of 47, i. e. ca. 6%) from the Northern and the Eastern regions clustering within the S1ca strain. This suggests that Ghana has faced dynamic RYMV epidemics in the last decades. Although both RYMV strains circulate within the same agro-environment (Fig.
665 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn. com/abstract=4352956 P r e p r i n t n o t p e e r r e v i e w e d Table  S1 : Location, metadata and experimental treatments on rice samples collected in 2020 in Ghana. Only a portion of the CP gene was amplified and sequenced for the 2020Gh 119 isolates, but the genetic information was sufficient to confirm that this isolate belongs to the S2 strain. (local var. ): local rice variety. nd: not determined. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn. com/abstract=4352956 P r e p r i n t n o t p e e r r e v i e w e d.
Acknowledgements We thank  Dr Drissa Silué  and the technical staff at rice breeding programme of the  CSIR-Crops Research Institute, Ghana  for helping with the collection of samples from across the country. This study was conducted under the  European Union Funded   Scientists in Crop Improvement for Food Security in Africa (SCIFSA)  Project. This work was partially funded by the  French National Research Agency  as " young researchers" program  ( ANR   JCJC SPADYVA ;  ANR-20-CE35-0008-01 ) and  CGIAR Research Program RICE. The funders had no role in the study design, data collection, and interpretation, or the decision to submit the work for publication. We thank the ISO 9001 certified  IRD iTrop HPC  (member of the  South Green Platform , http://www. southgreen. fr) at  IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this article.
1). We used MAXQDA software to track the incidence of keywords, by running an array of simple and complex keyword searches to consider discursive patterns, such as whether, at what frequency, and to what extent climate and other relevant terms were invoked. A challenge with keyword searches lies in the ability to draw meaning from the incidence of terms. Incidence can be explained by more than discursive shifts, e. g. , distribution of documents over time or donor drafting methods. We addressed this in three ways. Firstly, we normalised all incidence data by dividing the incidence for each year by the number of documents in the year. Secondly, we noted that lower incidence could be explained by reporting becoming less substantive overall -i. e. , the donor simply writing 'lighter' reports.
We used data from the OECD CRS between 2010 and 2019, 38 relying on the Rio Markers to identify climate finance. 39  Using these markers, donors indicated whether activities target mitigation and/or adaptation by identifying whether an objective is 'significant' (a score of 1) or 'principal' (2) to the project. A score of 0 indicates that the activity was examined but does not target the objective, while those that are not assessed are left blank but can be assumed to not target climate change given overreporting and mislabelling. 40, 41  Despite their limitations, the OECD CRS and Rio marker system provide a widely used and comprehensive proxy for public climate finance flows that allow for comparability with wider development finance flows. We used official development assistance (ODA) and other official flows (OOF) disbursements between 2010 and 2019 and reclassified each data point according to its Rio Marker(s) for mitigation and adaptation.
Data availability Project datasets are available on OSF: https://osf. io/ts8eu/ Base ODF data was sourced from the OECD Creditor Reporting System: https://stats. oecd. org/Index. aspx?DataSetCode=CRS1 Policy documents were sourced from the DFAT website: https://www. dfat. gov. au and the Trove of the National Library of Australia: https://trove. nla. gov. au.
Context. Determining when the first galaxies formed remains an outstanding goal of modern observational astronomy. Theory and current stellar population models imply that the first galaxies formed at least at z = 14-15. But to date, only one galaxy at z > 13 (GS-z13-0) has been spectroscopically confirmed. Aims. The galaxy 'HD1' was recently proposed to be a z ∼ 13. 27 galaxy based on its potential Lyman break and tentative [O iii] 88 µm detection with ALMA. We hereby aim to test this scenario with new ALMA Band 4 observations of what would be the [C ii] 158 µm emission if HD1 is at z ∼ 13. 27. Methods. We carefully analyse the new ALMA Band 4 observations and re-analyse the existing ALMA Band 6 data on the source to determine the proposed redshift. Results. We find a tentative 4σ feature in the Band 4 data that is spatially offset by 1.
0 s and were carried out on March 3 and 6, 2022. The total on-source time was 4. 9 h, with an average PWV of 4. 746 mm. All data processing and calibration were performed with CASA, version 6. 2  (McMullin et al. 2007). We used the calibrated measurement sets generated by the observatory for the imaging of the observations. Imaging was performed with the tclean task, adopting natural weighting. We create both a continuum map (using the SPWs observed in TDM) and spectral cube (using the SPW centred at 133. 16 GHz). No continuum emission is detected. The continuum root-mean-square (rms) noise level is 5. 2 µJy beam -1 (yielding a 3σ upper limit of 15. 6 µJy beam -1 ). For the spectral cube at 133. 16 GHz, the rms is 127. 3 µJy beam -1 per 78 MHz. The continuum and spectral cube image have a beam of 2.
78×2. 16 and 2. 91×2. 27, respectively. The final data cube used in the analysis reported here spans a frequency range of 132. 22-134. 08 GHz, corresponding to a redshift range of z = 13. 17-13. 37. The cleaned Band 4 continuum map showed several off-centre point sources. For the spectral cube, these were removed through CASA-imcontsub 1 (because CASA-uvcontsub 2  is less optimised for removing off-centre codetections). emission lines. To easily compare the spectra extracted within apertures of different size, spectra are presented in terms of signal-to-noise ratio rather than actual flux density. For the [C ii] 158 µm emission we adopt apertures of 2. 8 and 3. 5, corresponding to a similar size to the beam and 1. 25 times the beam of the ALMA band 4 data, respectively. In the bottom panel we adopt an aperture of 0.
3 ). Moreover, integrating over a narrower velocity width (the left two panels, consistent with other high-redshift studies, e. g. Hashimoto et al. 2018 ) yields no clear positive signal at the reported location of HD1. The multitude of tentative features found in the moment-0 maps of both the Band 4 and 6 data just by shifting the central frequency and/or width over which channels are collapsed, illustrates the difficulty in interpreting tentative detections. A more robust analysis of the noise properties of the data is therefore necessary to provide actual meaning to these features.
Noise properties of the ALMA data In this section, we describe the tests we performed to determine whether the tentative line-and image-plane detections, in both the Band 4 and 6 data, could be attributed to structured noise or a non-Gaussian noise distribution. First, we compared the distribution of the pixel values (in flux density per beam) for different moment-0 maps, created with the Band 4 and 6 data (see Sect. pixel values for the Band 4 and Band 6 observations, respectively. We took the signal-free pixel values to be the pixel values that fall outside of a 2. 2 radius for the Band 6 observations and a 5. 2 radius for the Band 4 observations, centred on the phase reference position. Each line in Figs. 4  and 5  indicates a unique moment-0 map that is integrated at a certain central frequency and over a certain integration width (with the latter indicated by the colourbar).
The original and mock, jack-knifed data cubes have near equivalent noise properties and are all wellapproximated by Gaussian distributions, as shown in the middle and right panel of Figs. 4  and 5. In Figs. 4  and 5 , we highlight two jack-knifed measurement sets per Band. The central panels show realisations wherein all the brightest pixel values fall outside of the central region (2. 2 for the Band 6 and 5. 2 for the Band 4 data) for all moment-0 maps, which can be interpreted as a non-detection if the observations are treated as real. The right panels show two corresponding jack-knifed moment-0 maps per Band (with noise properties identical to the real observations) for which the brightest pixel value falls within the central region for specific integration widths when making the moment-0 maps.
Likelihood of detecting two, correlated, noise peaks We tested the likelihood of finding two tentative detections in each data set, in the case that neither corresponds to a real source. In other words, we estimated how likely we are to obtain emission-line-like features at the same redshift and location on the sky if neither are actually line emission from the source. To this end, we applied two line-finding algorithms to both the real and jack-knifed data sets: (1) the python-based line-finding algorithm used by  Béthermin et al. (2020)  (see their Sect. 6) and  Ginolfi et al. (2022)  and (2) the algorithm Findclumps, described in  Walter et al. (2016) , which is available as part of the python library, Interferopy  (Boogaard et al. 2021)  3. For the real data, we used the dirty image cubes and replicated the noise properties in the jack-knifed cubes as described in Sect.
The S/N corresponding to this fidelity threshold are 5. 3 and 5. 2, for the Band 4 and 6 data respectively. We repeated the same exercise for the jack-knifed data cubes, which are equivalent to pure noise cubes, any potential source having been nullified by the jackknifing (see Sect. 3. 2). We took the mean over all line widths and all 10 cubes. The mean S/N at a fidelity of 60% for both the Band 4 and 6, jack-knifed cubes is 5. 3 ± 0. 2. Conversely, the mean fidelity of the Band 4 and 6 jack-knifed cubes, at the S/N of the tentative features, are 0. 06 ± 0. 04 and 0. 09 ± 0. 05 respectively. The combined fidelity is then = 0. 5 ± 0. 4%. Thus, the significance of the tentative features, both on their own and in combination, is too low for them to be considered real. Next, we tested the significance of the line detections given the expected source position.
8σ features in the Band 6 data is mainly due to the difference in spatial resolution. The fidelity within this smaller search region is 0 for the Band 4 data but is negative for the Band 6 data. Cross-matching the real Band 4 and 6 data cubes, we recovered only one matched pair of positive ≥3. 8σ features within 10 kpc and 1000 km s -1 of each other, which correspond to the proposed [O iii] 88 µm line at z = 13. 27 (Band 6 cube) and tentative [C ii] 158 µm line, offset by ∼6 kpc and ∼-190 km s -1 4. However, we also found three matched pairs of negative ≥3. 8σ features within 10 kpc and 1000 km s -1 of each other, implying that the positive matched pair is unlikely to be real. The number of matched peaks are shown as the filled circles in Fig. 7. We also restricted the line-finding procedure to find peaks within ∼2.
9 of the source position for the jack-knifed data cubes (of which there are 10 each for the Band 4 and 6 data). Of the 10 jack-knifed Band 4 cubes, five (four) have at least one positive ≥3. 8σ feature within 10 kpc of the expected source position (the centre of the cube), whereas three (four) have at least one negative feature of the same significance. The two quoted values are for the two different line-finding algorithms. Of the 10 jackknifed Band 6 cubes, all have at least one positive ≥3. 8σ feature within 10 kpc of the expected source position, with a mean of six (eight) such features per entire cube. Likewise, all jack-knifed Band 6 cubes have at least one negative ≥3. 8σ feature, with a mean of seven (eight) such features per cube (with Findclumps finding the higher number of peaks).