dataesr/scientific-paragraphs-categorization huggingface dataset:
These five individuals per phenotype were then used for the machine learning in order to delimit the phenotype by hue. Following this approach, the hue range for the red phenotype ranged from 0 to 0. 1625770; and the hue range for the green phenotype ranged from 0. 3215928 to 0. 5637775. Finally, the ImaginR package provided the hue (H), saturation (S), brightness (V), average hex triplet code and interpreted colour phenotype of each sample ("green", "red" or "other") based on the machine learning of that hue. In the procedure, this task is looped over all the samples/pictures in the folder, and the package produces a final tabular file summarizing all the information detailed above, along with the name of the sample. The text file is saved in. csv format. For subsequent statistical analysis, the grouping of the harvested pearl dataset was made in two ways: (i) all the pearls were grouped according to the colour of the donor colour inner shell phenotype characterized in ImaginR (since the trait is only partially genetically inherited, pearls with different colour phenotypes were pooled into the same category); (ii) only the green pearls from green donors and red pearls from red donors were analysed.
A laser grain-sizer was used to estimate the particle size distribution and then after statistic treatment, the main fractions and other variables (D50, D90, skewness, kurtosis). The AnalySize Matlab function was used to unmix the PSD end-members and to estimate their abundance along the core. The proposed methodology is applied to a Lake Bourget sediment core and is compared with previous studies  (Giguet-Covex et al. , 2010; Jenny et al. , 2013 Jenny et al. , , 2014)). Then, the end-members were used to characterize the annual laminations (varves), to count them, to estimate their accumulation rate per year and their Vis-NIR signatures.
Data Analysis ENVI 4. 8 was used to perform the data acquisition. Then, MATLAB (R2018a, MathWorks) was used for advanced processing and analyzing these data. PLSR was used to establish a relationship between the HSI and the PSD of the core to create predictive models and then to estimate the PSD in each pixel. Then AnalySize Matlab function was used to unmix the predicted PSD to find pure sources  (Paterson and Heslop, 2015).
Sedimentary deposition sources discrimination Each PSD is a mixture of pure sub-PSD, that can be related to some specific sediment source. The Matlab function AnalySize  (Paterson and Heslop, 2015)  was used to unmixed each PSD and find these end-members. The assumption behind this function is a linear model equation (Eq. 1): = × + (Eq. 1) with X the reference PSD profile, A the estimated abundance, S T the estimated PSD of the pure components and E the residual matrix. The intensity of the PSD and its abundance should not be negative. Thus, non-negative spectra and concentrations constraints were used. Another constraint was added to have the sum of abundance and the intensity of S T at 1. AnalySize allows estimating end-members in two ways. One is called non-parametric end-member analysis (EMA) that estimates end-members empirically from the whole dataset.
, 2019). Some proxies can be estimated directly after HSI acquisition, and other proxies need to process the data with or without destructive analysis. A Matlab toolbox was created for the study of particle size presented in this paper and for a previous study of organic matter  (Jacq et al. , 2019b) , and also allows for the generation of models for organic matter (LOI550 and RockEval) and particle size on several lakes (unpublished works). This toolbox can be found at https://github. com/JacqKevin/HSI_PLSR, developed with Matlab R2018a (the Parallel Computing Toolbox can be used for faster calculations). It contains functions to create a PLSR model and to apply it to the data, and also spectral preprocessing and variable selection algorithms. The regular use of this toolbox allows for highlighting precautions and difficulties related to data.
Acknowledgements The Bourget core used in this study is stored in the EDYTEM laboratory that also performed the PSD analysis in 2009 during the  IPER-RETRO program  ( ANR-08-VUL 005 ). The hyperspectral imaging acquisition was made at the  University of Normandie-Rouen  and was funded by the  Region Normandie , which supports the scientific consortium SCALE UMR CNRS 3730. Furthermore, we thank the anonymous reviewer and  Jasper Knight  ( Editor-in-Chief ) for their helpful comments.
Generation of the graph of functions To set the graph of functions, the designer has to give all the functional surfaces required for the part in a CAD software. The creation of the graph, as shown in Figure  4 , is described thereafter. Each surface stemmed from the specifications is first recorded in a separate part file (Fig. 4. 1). This allows the design team to modify, if necessary, each surface independently. It also allows to form a functional surfaces database that can be reused for another design. In addition, these surfaces can be used both for interior or exterior contact surfaces as they have opposite normal in the CAD software. Each constraint from the specifications, not translatable in terms of surface, is also recorded in a part file (presence of a specific material, surface finish etc.
Application to the case study In the framework of our work, we use SolidWorks as CAD software. We begin to generate the graph of functions from the given surface constraints: the part needs to incorporate thirteen borings lying between two parallel planes distant by 20 mm and needs to receive an existing part (drilling tool, bearing, fast positioning screw or pin). Each of these pieces has a have a plane-parallel contact surface on the drilling grid, which means that each drilling has a contact surface and an associated contact volume. Since the specifications also provided an existing CAD mock-up (Fig. 1 ), we must also impose positioning constraints at this stage. Regarding the case study, it is possible to connect all the functional surfaces same set. Figure  5  presents a representation, on which only surfaces are represented: green areas correspond to functional surfaces and red areas correspond to contact surfaces.
The repeated appearance of nucleotide sequence reagents could also suggest that these reagents are selected from internal databases of RNA interference reagents and PCR primers. How 'theme and variations' manuscripts may evade detection. Particular features of the production of manuscripts in series could reduce the likelihood of their being detected through editorial practices and peer review (Figure  5 ). 33 Single-gene knockdown papers describe widely used and accepted experimental approaches  33  and could therefore appear sound to peer reviewers focussing on the technical aspects of described experiments. As described above, targeting under-studied genes or biological processes may reduce the likelihood that specific content errors will be detected during peer review, due to the lack of available content expertise and/or perceived poor reviewing standards at targeted journals (Figure  5 ).
83, 84 Semi-automated tools present advantages of higher throughput, and their use may also lead to the discovery of new features of concern. To increase the scale and efficiency of manuscript production, we have proposed that paper mills may use images from internal image databases (Figure  5 ). This practice could result in inadvertent image duplication within and between studies, particularly if large numbers of manuscripts are produced by an individual paper mill. Indeed, image duplication was cited as a reason for the retraction of single-gene knockdown paper to date. 49 A number of tools have recently been described to detect image reuse,  79, 80  which may be useful to detect papers produced with the assistance of paper mills, as well as other causes of image duplication. The development of improved algorithms that can reliably identify highly similar figures may also help to identify members of fraudulent publication series.
33  more prominent feature of single-gene knockdown papers is their description of incorrect nucleotide sequence reagents, which has characterised most of the highly similar gene knockdown papers that we have described (Figure  4 ). 33 The repeated description of incorrect nucleotide sequence reagents suggests that these reagents may also be selected from internal databases. Although mismatches between nucleotide sequence reagents and their stated identities can be detected through cross-checking using search algorithms such as blastn,  46  checking such detailed information may be broadly incompatible with paper mill workflows. A simple means to identify papers that have used common nucleotide sequence reagents is to employ nucleotide sequences as queries in Google Scholar searches. 33 However, this approach is limited to searching for incorrectly identified nucleotide sequence reagents whose identities are already known.
A second limitation of this approach is the variable formatting of nucleotide sequences within publications, and we have previously been unable to detect known instances of incorrectly identified nucleotide sequence reagents through Google Scholar searches. 33 A semi-automated approach to detect incorrectly identified nucleotide sequence reagents would present advantages of both increased throughput and the capacity for knowledge discovery. We have therefore written the Seek and Blastn tool to facilitate the identification of publications where the described identity of a nucleotide sequence does not match its described experimental status (manuscript in revision). 85 This tool is also freely available for researchers to access and test. 86.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/ or publication of this article: This work is supported by the funding from the  Post-Truth Initiative , a  University of Sydney Research Excellence Initiative  ( SREI 2020 ) (to J. A. B. ) and from the  US Office of Research Integrity  Project Grant  ORIIR180038-01-00  (to J. A. B. and C. L. ). This work was supported by donations to the  Children's Cancer Research Unit of the Children's Hospital at Westmead. deClARATIon oF ConFlICTInG InTeReSTS: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The physics of this upgraded algorithm are described in a companion paper  [40]. The new product version has been referenced as LSA-207 by EUMETSAT, corresponding to the MSG downwelling surface short-wave radiation fluxes total and diffuse fraction (MDSSFTD). Two different modules are used to calculate the set of MDSSFTD outputs, one for clear conditions and the other for cloudy conditions. The two methods are designed to ensure the spatial and temporal continuity of DSSF and diffuse fraction in the LSA-207 product. Details on the methodology as well as the major limitations are given in  [40]. The input cloud mask is used to distinguish between the two methods. The summary of the two methods is as follows and is described in details in  [40]. In clear sky conditions, the formulation based on the algorithm SIRAMix  [41, 42]  is used to estimate the total flux and diffuse fraction.
Requirements Over India, a satellite-based surface solar radiation dataset called Surface Solar Radiation Data Set-Heliosat (SARAH-E) was developed and evaluated against in situ measurements over a variety of sites. The results indicate an overestimation of the satellite DSSF, with a mean bias of 21. 9 Wm -2  [43]. A study over Finland and Sweden  [44]  also discussed the retrieval accuracies of two different satellite-derived DSSF datasets (the polar-orbiting satellite-based dataset, CLARA-A1, and the geostationary satellite-based dataset, SARAH). They showed comparable accuracies in comparison with ground measurements, in particular 10 Wm -2 for the monthly means metrics and 15 Wm -2 for daily means metrics. Over Europe,  [31]  completed an inter-comparison of satellite-derived incoming solar products from the different satellite application facilities of EUMETSAT and concluded that the products have comparable mean biases (+4 Wm -2 ) and root mean square differences (80-100 Wm -2 ) for instantaneous metrics.
MSG/SEVIRI satellite-derived products differ on a pixel basis from 0 to 12 min with the product time. The SEVIRI scan takes 12 min for the data acquisition over the MSG disk starting from the south pole and finishing its acquisition in the north pole. For the sake of a fair comparison, the ground measurements were averaged over 15 min, centered around the exact acquisition time of the satellite for every SEVIRI pixel. The relationship between the estimated time difference as a function of row number (or latitude) is detailed in other LSA SAF reports (product user manual MDSSF; 2. 6V2 at https://landsaf. ipma. pt/en/products/longwave-shortwave-radiation/; last time consulted on 2 September 2019). Note that the diffuse fraction is not a direct measurement. This variable is obtained by dividing the diffuse component over the total component, which are measured by the ground instruments.
CAMS All-Sky Radiation Data The Copernicus Atmosphere Monitoring Service (CAMS) all-sky radiation data were also used for comparison with the MDSSFTD product. CAMS all-sky radiation service distributes global, direct and diffuse irradiances as well as a direct at normal incidence irradiance, for all sky (clear + cloud), clear sky only, and cloudy sky only. These data are provided as timeseries with a temporal resolution of 1 min, 15 min, 1 h, 1 day, or 1 month, and are made available since February 2004 with a 2-day delay. The spatial coverage corresponds to the Meteosat Second Generation (MSG) disk. To produce the timeseries, radiation data were spatially interpolated to the point of interest from a product available at the native spatial resolution of MSG/SEVIRI. These data are publicly available from the CAMS portal (https://atmosphere.
copernicus. eu/solar-radiation, last consulted on 18/10/2019). The CAMS Radiation service relies on the Heliosat-4 method  [50] , which is composed of two modules: McClear v3 for clear skies  [51, 52]  and McCloud for cloudy skies  [50]. The McClear approach, version 3, used by the CAMS radiation service, has been upgraded from McClear v2 and now consists of physical modelling using the radiative transfer model libRadtran  [53]. As for the MDSSFTD method, the McClear v3 also now relies on aerosol content and load, as well as gas contents forecasted by ECMWF and distributed by CAMS. Compared to the MDSSFTD method, McClear v3 uses a monthly climatology  [54]  of the MODIS surface albedos  [55]  and a similar approach to MDSSFTD for the aggregation of the optical depths of each aerosol species to derive the properties of the aerosol mixture  [52].
These two methods used CAMS aerosol data and GADS aerosol properties, which may point to an overestimation of the highly scattering aerosol components by CAMS or a limited transformation from CAMS to GADS components. Figure  4  shows a comparison between satellite-derived estimates and ground measurements of the diurnal cycle of the diffuse fraction for clear sky conditions. Again, the satellite-derived estimates capture the diurnal variations well compared to the ground measurements. In particular, the increase of the diffuse fraction with extreme geometries is reproduced well. For these four days, a slight overestimation between 0. 055 and 0. 142 was found for fd < 0. 5 in clear sky conditions. This overestimation comes from the slight overestimation of the diffuse DSSF by MDSSFTD, which was also found for clear sky situations by  [41]  when using SIRAMix and the McClear method  [51].
These two methods used CAMS aerosol data and GADS aerosol properties, which may point to an overestimation of the highly scattering aerosol components by CAMS or a limited transformation from CAMS to GADS components. Figures  5  and 6  show the results of similar comparisons that were conducted for dates showing all sky conditions (partially clear and partially cloudy). The diurnal variations of the MDSSFTD product, including the total DSSF and diffuse fraction, are compared against ground measurements for selected days in Figures  5  and 6 , respectively. A rather satisfactory agreement exists between the satellite-derived estimates and the ground measurements. The increase of the diffuse fraction with the cloudiness is generally well represented (e. g. , see Figure  6d ). Figures  5  and 6  show the results of similar comparisons that were conducted for dates showing all sky conditions (partially clear and partially cloudy).
Comparison to the CAMS Radiation Product The retrievals from MDSSFTD were compared against the counterpart estimates from the CAMS radiation product. Figure  18a  shows the good agreement between the two products for all sky conditions, with a correlation of 0. 966. Figure  18b ,c take a further look at the comparison by exploring the clear sky and cloudy sky retrievals separately. The clear sky comparison indicates high agreement between the two products, which use the CAMS aerosol data as input. The comparison for cloudy sky conditions also shows good agreement (correlation of 0. 909) despite the differences in the retrieval methods for cloudy sky conditions. The higher dispersion is justified by the increased difficulty of the retrieval for cloudy skies. differences in the retrieval methods for cloudy sky conditions.
They indeed require that the considered QA system, trained on natural language, be able to find the answer in the input to assess, despite the variability of the generated texts. Extension to the unsupervised setting While being a useful complement to ROUGE, the two QA-based metrics described above still need human-generated summaries. In this paper, we investigate and propose extending the previously described QA-based approach in an unsupervised setting. With this aim, we extended the above metrics at the document level (i. e. , questions and answers are generated from the source article text rather than from the reference summary), dispensing of the need for human-generated reference summaries. Thus, in line with the APES approach described above, we propose two unsupervised QA-based metrics, to which we refer to as QA f score (unsup) and QA conf (unsup).
Quantitative Analysis We exploit human judgments obtained for 3 types of automatically generated summaries by  Paulus et al. (2017)  on 100 samples of the CNN/Daily Mail summarization dataset (see detail in section 4. 1), in terms of readability (how well written the summary is) and relevance (how well does the summary capture the important parts of the article). The summaries are generated by the three different systems proposed in the original work. Those samples have been scored, via Amazon Mechanical Turk, for Readability and Relevance (scores from 1 to 10 for both metrics). In Table  1 , we report Spearman's rank correlations on this data, where we compare summaries rankings obtained according to the assessed metrics. Scores render the ability of the various met-rics to reproduce human preferences (in terms of readability and relevance).
First, we observe that readability and relevance are naturally intertwined: intuitively, an unreadable summary will bear very little information, one of the facts that explains the high correlation between readability and relevance. From this correlation analysis against human judgments, we observe that, as expected, the Language Model metric captures readability better than ROUGE, while falling short on relevance. On the other hand, the results obtained using the proposed QA-based metrics indicate their potential benefits especially under the unsupervised setting, with QA conf and QA f score capturing readability and relevance better than all the others reported metrics, including ROUGE. We thus conclude that the proposed metrics, which favorably correlate with readability and relevance under human evaluation, are worth of a deeper experimental investigation: in the following sections we provide a thorough evaluation of their contributions as Reinforcement Learning rewards signals.
Implementation details As QA system we use the BERT "base" pretrained model  (Devlin et al. , 2019) , finetuned on the SQuAD dataset  (Rajpurkar et al. , 2016)  using the recommended parameters for the task 1. This differs from the approach adopted by  (Eyal et al. , 2019)  who trained their QA model on CNN-DM (the same data used for the summarization task).
, 2017)   More recently, the model proposed by  See et al. (2017)  was further extended  (Gehrmann et al. , 2018) , with the addition of an attention mask during inference: a pre-trained sequence tagger trained to select which input tokens should be copied and used to filter the copy mechanism. Such a filter, called Bottom-Up Copy Attention, was shown to help prevent copying from the source text sequences that are too long, hence resulting in more abstractive summaries. On the CNN/Daily Mail dataset,  (Gehrmann et al. , 2018)  found this two-step process to yield significant improvements in terms of ROUGE -resulting in the current state-of-the-art system. We base our experiments on this model. The differentiable loss function commonly used for training summarization models, negative loglikelihood, has several known limitations.
Data Used Task-specific corpora for building and evaluating summarization models associate a humangenerated reference summary with each text provided. We resort to the CNN/Daily Mail (CNN-DM) dataset  (Hermann et al. , 2015; Nallapati et al. , 2016)  for our experiments. It includes 287,113 article/summary pairs for training, 13,368 for validation, and 11,490 for testing. The summary corresponding to each article consists of several bullet points displayed on the respective news outlet webpage. In average, summaries contain 66 tokens (σ = 26) and 4. 9 bullet points. Consistently with  See et al. (2017)  and  Gehrmann et al. (2018) , we use the non-anonymized version of the dataset, the same training/validation splits, and perform truncation of source documents and summaries to 400 and 100 tokens, respectively.
In-Domain vs Out-of-Domain Finally, we experiment with the proposed QA conf and QA f score metrics in an unsupervised fashion, as they can be computed at article leveli. e. without accessing the reference human-generated summaries. We investigate the potential benefits of using this approach both in-domain and out-ofdomain: for the former, we resort to the test set of the CNN-Daily Mail (CNN-DM) dataset; for the latter, we leverage the TL;DR corpus. As the CNN-Daily Mail is built from mainstream news articles, and the TL;DR data comes from social media sources, we consider the latter as out-of-domain in comparison. From the latter, which includes circa 4 million samples, we randomly draw sample subsets of size comparable with CNN-DM for training, validation and testing splits. Due to computational costs, we restrict these experiments to the model trained under reinforcement using the QA learned metric.
Under this setup, the model has access at training time both to: • supervised samples for which a reference summary is given (and thus all metrics, including ROUGE and NLL, can be computed as a training objective), coming from the training set of CNN-Daily Mail corpus ; • unsupervised samples, for which no reference is available, thus allowing to only compute QA conf (unsup) and QA f score (unsup). Three unsupervised settings are considered in the following: TL;DR, corresponding to the out-of-domain setting where we use articles from the TL;DR dataset; CNN-DM (VAL), corresponding to an indomain setting where we use texts from the validation set from the CNN/Daily Mail dataset; and, CNN-DM (TEST) for an in-domain setting where we use the articles from the test set (thus containing texts used for evaluation purposes).