dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank staff of the  UEPR unit  for animal care,  Mickael Genissel ,  Julien Georges ,  Francis Le Gouevec , and  Vincent Piedvache. We also thank  Emilie Lebrun  and  Laurine Picquemal  for their involvements in running the administrative aspect of the Aniscan imaging facility and  Raphael Comte  ( Pegase unit ) for insulin measurements. We also acknowledge  Biogenouest and Prism core facility. Contributors C-HM planned the experiments, conducted the studies, analyzed the data, and was primarily responsible for writing the manuscript. AC conducted the studies and analyzed the data. MH and KLJ made a major contribution to the writing of the manuscript, including data interpretation. C-HM is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
6D). Expression levels of cuticle related genes were investigated in fdl1-1 and wild type plants treated with ABA and in corresponding untreated controls. The transcript levels of both ZmGL15 and ZmCER4 genes were reduced upon ABA treatment in both in fdl1-1 and wild type plants after 48 hours of ABA treatment (Fig. 6F, G ). The ZmCER1 gene (Fig. 6H ) was up-regulated in fdl1-1 after 24 hours of ABA treatment (164%) and in wild type plants after 48 hours (102%) while it was down-regulated in mutant plants after 48 hours (-74%). Consistent with our previous observations based on RNA-Seq data (Supplemental Table  S9 ) and quantitative expression analysis (Fig. 3D ), the ZmGL15 and ZmCER4 genes were down-regulated in untreated fdl1-1 seedlings compared to un-treated control wild type plants while ZmCER1 was up-regulated (Fig.
Chlorophyll leaching assay, leaf temperature and water loss For the chlorophyll leaching analysis, second, third and fourth fully expanded leaves were taken from a minimum of five independent plants per genotype and dissected into pieces of 8 cm in length as measured from the apex. Leaf sectors were weighed, immersed in ethanol 80% and incubated in the dark at room temperature. Absorbance was measured every day at 647 and 664 nm with a spectrophotometer (Agilent Technologies Cary 60 UV-Vis) to quantify the chlorophyll released in the solution and measurements were performed until complete chlorophyll extraction. The micromolar concentration of chlorophyll was calculated using the equation: total micromoles chlorophyll = 7. 93 x A664 + 19. 53 x A647  (Lolle et al. , 1997). The data obtained were normalized per gram of fresh weight and area of the 8 cm long leaf pieces to be expressed as a percentage of the total chlorophyll.
Thermal images of second fully expanded leaves were taken from 14 old-days fdl1-1 and wild type seedlings with a semi-automated long-wave infrared cameras system (FLIR T650sc). The temperature of the leaves was measured using the FLIR ResearchIR Max software. To determine the time course of the seedling water loss, 10-old-days seedlings were detached and weighed immediately. Seedling weight was then estimated at designated time intervals and water loss was calculated as the percentage of fresh weight (FW) based on the initial weight. Several biological replicates were measured for each genotype.
Cell density and stomatal index analysis To measure stomatal density and stomatal index, a leaf surface imprint method was used as previously described  (Castorina et al. , 2018). We analysed second, third and fourth fully expanded leaves of fdl1-1 mutant and wild type plants. Stomatal index (SI) was determined as [number of stomata / (number of epidermal cells + number of stomata)] × 100. One-way ANOVA test was performed with the statistical package SPSS 21. 0.
Accession Numbers The gene sequence from this article can be found in the Maize Genetics and Genomics Database (MaizeGDB) or GenBank/EMBL databases under the following accession numbers: Zm00001d022227 or GRMZM2G056407 (ZmFDL1), Zm00001d046449 or GRMZM2G153541 (ZmEF1a), Zm00001d045321 or GRMZM2G098750 (ZmRAB18). Accession numbers are available in the Supplemental Materials. RNA sequencing datasets analysed during the current study are available in the GEO repository, under the GSE146905 accession number (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE146905).
relying on large samples in order to maximize statistical power to detect a specified effect size. First, traditional methods of theory pruning often rely on extant data to reconcile or compare theoretical predictions. For example,  Schlaegel and Koenig (2014)  used meta-analytic path analysis to examine two competing explanations for entrepreneurial intentions in predicting propensity to start a firm. Although such sophisticated analytic techniques are useful for combining studies testing different theoretical orientations into a single analysis, the full set of terms and propositions for both theories may not appear within any single existing study or dataset. Moreover, because research finding support for the proposed hypotheses is far more likely to lead to a publication (i. e. , publication bias;  Fanelli, 2010; Kepes, Banks, McDanel, & Whetzel, 2012) , available reports using such an approach are unlikely to result in the conclusion that a third explanation may be superior (i.
This suggests that parents who have already entrusted their children to day care should be more, not less, prone to motivated reasoning in this domain. Table  3  displays the theoretical predictions of the Motivated Reasoning, Cognitive Schema, and Accuracy-Driven perspectives on reasoning in the wishful thinking paradigm  (Bastardi et al. , 2011; Ebersole, 2019). While conducting direct/literal replications of the original method, we thus at the same time attempt to achieve what  Köhler and Cortina (in press ) call generalizability tests, in this case specifically testing moderators about which competing theories make opposing predictions (e. g. , parental status). The pre-registered analysis plans and study materials are available on the OSF (https://osf. io/9fy8m) and in Supplement 1, and the data and code are likewise posted online (data: https://osf.
io/fhq45/, analysis code: https://osf. io/rphwv/). Notably, the creative destruction analyses were formulated and pre-registered after the Ebersole (2019) data collections were carried out, thus this Prior beliefs do not influence reasoning about scientific evidence. Prior desires and the processing of evidence Desired conclusions influence reasoning about scientific evidence. Desired conclusions do not influence reasoning about scientific evidence. Desired conclusions do not influence reasoning about scientific evidence.
constitutes a secondary analysis of the dataset (  Van den Akker et al. , 2019). The results of this re-analysis (1) reproduced the pre-registered predictions of  Ebersole (2019)  regarding the effects of pre-commitment on assimilation to prior beliefs, and (2) pitted theories of motivated reasoning, cognitive schema-based processing, and accuracy-based reasoning against each other in a highly informative manner. Conceptually replicating the assimilation-to-beliefs effect  (Lord et al. , 1979) , participants who had not committed to methodological standards rejected the methodology and findings of a scientific study whose results challenged their cognitive beliefs about the efficacy of home vs. day care. As hypothesized, the commitment condition eliminated cognitive assimilation  (Ebersole, 2019).
Funding acknowledgments Eric Luis Uhlmann is grateful for an  R&D grant  from  INSEAD  in support of this research. Anna Dreber  is grateful for generous financial support from the  Jan Wallander and Tom Hedelius Foundation (Svenska Handelsbankens Forskningsstiftelser) , the  Knut and Alice Wallenberg Foundation  and the  Marcus and Marianne Wallenberg Foundation  ( Anna Dreber  is a  Wallenberg Scholar ), and the  Swedish Foundation for Humanities and Social Sciences.
, 2018). The core genome MLST (cgMLST) typing method takes into account the sequence variation of 1,748 L. monocytogenes core genes, improving isolate discrimination and allowing a standardized comparison with isolate databases for outbreak investigations and surveillance  (Moura et al. , 2016 (Moura et al. , , 2017)). Besides its increased discrimination power as compared to previous methods such as PFGE, whole genome sequencing enables simultaneous identification of antimicrobial resistance and virulence genes as well as other genetic determinants playing a role in L. monocytogenes infection  (Moura et al. , 2016). Virulence of L. monocytogenes is associated with its ability to invade, multiply, and survive within host cells  (Portnoy et al. , 2002). Clones of L. monocytogenes differ in their pathogenic potential  (Maury et al.
, 2018). All calculations were made with the method described above. Allele numbers, STs, CCs, CTs, and SLs were determined according to the Listeria sequence typing database and its tools available on BIGSdb-Lm platform 1   (Jolley and Maiden, 2010; Moura et al. , 2016). MLST and cgMLST profile comparisons were done using Bionumerics 7. 6 software (Applied Maths, Belgium) with single linkage algorithm ignoring missing values in pairwise comparisons. Dendrograms were visualized using iTOL 2   (Letunic and Bork, 2016).
Acknowledgements. All comics are hand-drawn by  Alice Adenisdistributed  under the Creative Commons Attribution 4. 0 License. This work is volunteer-based. We are thankful to  Claudia Alves de Jesus-Rydin  who gave us the spark to start our adventure and support to continue,  Philippe Liotard  and  Cléo Schweyer  who guided us in the very beginning of the project, and to  Mary Anne Holmes  and  Johanna Stadmark  who improved this manuscript through careful review. Last but not least, we would like to thank all the anonymous contributors without whom the project would not exist. Review statement. This paper was edited by  Claudia Jesus-Rydin  and reviewed by  Mary Anne Holmes  and  Johanna Stadmark.
For instance, the Council of Europe has urged Member States to take measures against illegitimate forms of interference by AI tools. 7 'exploit their data' to. It suggests empowering users by robustly enhancing awareness of how platforms 'exploit their data' to train algorithms for commercial purposes. 8 he European Parliament (EP 2019b) makes a step forward. Like the Council of Europe,, it warns on the ability of algorithms to violate expectations the SMEs fiduciary expectations the SMEs have toward organisations using the same AI systems; on the other,  9  to contain this, it calls for empowerment strategies that are based on AI tools: '[I]n the AI era, an effective countervailing power needs to be supported by AI too'. 10  Interestingly, the EP considers AI-led empowerment tools more effective than traditional public regulation and enforcement to reduce AI-led manipulation (such as price discrimination, and over-targeting), suggesting that data mining algorithms could be used in manyfold ways to help SMEs, from 'analysing and summarising massive amounts of reviews, or comparing prices accross platforms,' to 'detecting discrimination' or 'build [ing] supporting tools that could identify prejudice an unfair treatments  '.
According to them, in the growing world of peer-to-peer, digital platforms can be accounted as credible parties in the regulatory arena, because they enjoy regulatory functions. Indeed, when designing its own architecture, a platform defines the rules governing the space where users operate; and in doing so it proceeds from a 'macro-level' down to a 'micro-level', where interactions between platforms and business and among peers are regulated at a very detailed level. 15 That is done through algorithms fed by data produced within the platform. 16 It follows that the kind of regulation produced at the 'micro-level' is not only algorithmic, 17 but also highly 'granular', compared to traditional (i. e. nonalgorithmic) legal rules. 18 or what interests us, the regulatory functions of platforms can easily be grouped into three main functions: (i) setting the architecture design; establishing rules governing (ii) the P2B relationships; and (iii) the interactions between users.
Concerning user-to-user relations (iii), it is the platform that typically establishes standard models for presenting commercial offers; decides about delivery and returning policies, and so forth. (EC 2019, at 61). While algorithmic production of rules by the platforms can be cost-effective, as it 'suits the scale of peer-to-peer' (EP, 2017 at 23) (flexibility and differentiability), and may generate efficiencies, by allowing transactions that were not possible before such innovations, there are nonetheless issues that need to be tackled. First, business users cannot know what the parameters used in algorithmic decisions are, nor can they be sufficiently aware of the legal consequences of the decisions taken by the platforms. Often, the data produced through the use of the platform -which is machine-generated data -tend to be treated as belonging to it, and therefore not accessible to the business user.
Think for instance to marketing data in marketplace platforms like Amazon: while these data are generated thanks to its users' interaction, they are nonetheless unavailable to them. 21 That puts the latter in a disadvantaged position vis-à-vis the platform, because they may not reuse it to profile their products or service further, and thus ameliorate them. Moreover, some biases are 'innate' to the use of algorithms and that depends on the datasets, such as overreliance on correlations, which might generate discrimination among users or disadvantaged treatment (think, e. g. to ranking manipulation) (Lim &  Taeihagh, 2019). Furthermore, many business users are still uninformed of the real profit-driven mechanisms governing digital platforms (Whittington & Hoofnagle, 2012, at 1357;  Acquisti & Grossklags et al.
, 2007). Often, digital platforms may take advantage of their users, who cannot understand and benefit from the full value of the data they generate, nor can they understand entirely the rankings practices applied to them. The myriads of micro and SMEs, as we shall see,  22  are not the big business and often behave much like individuals, who have no share in the vast amounts of profit that platforms make out of the personal data they circulate (Grunes, 2013, at 1123; Shelanski, 2013;  Argenton & Prüfer, 2012; Vaidhyanathan, 2011; Rust, Kannan, & Peng, 2002). In the same vein, with online bargains, SMEs' room for negotiation has shirked down, as is their bargaining power; digital platforms enjoy a strong information asymmetry against business users, which they can use to profile them and, accordingly, put themselves in the position to exploit and discriminate among and against them.
g. of a marketing platform). 22 See sect. 6, below. 23 EC (2019), at 62. 24  We refer to EC, 2020a, 2020b and 2020d. standards established by the platforms themselves. Notwithstanding these differences in the governance structure, however, the kind of disclosure regulation adopted is quite standard (i. e. non-targeted, non-differentiated, and general): it merely suggests employing transparency duties regarding contract terms and conditions, and to release information about data use, or reputation mechanisms. In no way does it encompass or avail of algorithmic tools, as purported by the EP to better empower platform's business users, and their participation to the making of platforms' decisions (e. g. their codes of conduct) is only marginal. In the following, we will review and discuss the EU model, starting with disclosure self-regulation (sect.
(Traditional) Solicited Codes of Conduct: the GDPR and EU regulation 2018/ 1807 Self-regulation by digital platforms is at the core of EU initiatives aimed at the liberalization of non-personal data circulation (Regulation UE 2018/1807). 27 Platforms are 'encouraged' to adopt self-regulatory CoC to provide (also) professional users with 'detailed information and operational requirements for data porting' and the switching of the service provider. 28 The porting of data from one provider to another being fundamental to create the broadest and most competitive data economy, EU institutions consider it essential that professional users are 'aware' of such possibility. Therefore, the CoC should, first of all, establish communication roadmaps to 'raise awareness' about the CoCs themselves (Art.
Coglianese & Lazer, 2003; Coglianese & Mendelson, 2010). 42 See, above, note 5. 43  According to EC, (2019), Regulation 2019/1150 has been adopted in response to various harmful trading practices realized by the platforms, such as: unilateral change of contractual terms and conditions without prior notice; delisting of goods or services; suspension of business users' accounts without a clear statement of reasons. Other practices related to transparency duties include: the setting of unclear conditions for access and use of the data generated and collected by the platform providers; lack of transparency in the ranking of goods and services; and the discriminatory treatment platforms reserve to providers, as compared to their own (competing) services. Finally, the EC contests that most-favoured-nation clauses have been widely used by the online platforms that restrict their couterparts' ability to offer more attractive conditions through other channels.
44 See the Executive summary of the Impact Assessment (SWD (2018) 139 fin. ) accompanying the Proposal, at 2. Besides that, the Proposed Regulation foresees: on the regulatory side: 'an obligation to set up internal redress mechanisms, as well as provisions to allow for collective redress for associations representing businesses'. (Art. 4). Discrimination practices (i. e. platforms favouring their business or related commercial partners) are also subject to disclosure duties (Art. 6); and platforms shall describe rules on access (or non-access) to personal and non-personal data which business users provide to them or which are generated through the platform's use (Art. 7). This does not grant data access or portability to business users 45 ; which are the subject of other rights under either the GDPR, the Regulation on the free flow of non-personal data, or other special regimes.
' Concerning controls, audits that are needed to check for compliance would be supplemented by an API; the latter, however, 'would not provide access to raw data, as in Piffault's model, but could allow a government to run "queries" to verify compliance. ' 53 For instance, APIs could randomly check for compliance of tax collection by platforms. The core of Sundararajan's proposal is that platforms are the only ones to own the data and the capacity to run analytics over big datasets that are relevant for regulatory purposes. Also, they are in the best position to check for discrimination, biases and other undesired consequences of (their own) algorithmic decisions. Therefore, 'rather than tasking the government with the development of such methods on data provided by platforms, it is better to allow, or perhaps even require, the platforms to develop these methods themselves and apply these to the data that remains within the platforms.
Algorithmic disclosure co-regulation for platforms' business users In view to empower small business users through AI tools and also strengthening the accountability of platforms' decision-making as far as disclosures are concerned, we suggest endorsing algorithmic disclosure co-regulation. Building on previous work, 57 we will articulate on how this model also answers the quest for increased participation 58 of business users in algorithmic decisions that affect their economic lives, without having to receive the disclosures passively. Our model provides a cost-effective way to carry on frequent assessment on datasets to produce well-functioning algorithmic disclosures on a collaborative fashion, instead of delegating this task completely to the platforms. Our model is based on three propositions and starts from a set of observations.
The propositions are: (1) that disclosures targeted at business users should be done by algorithms (as suggested by the EP); (2) should be pre-tested in a co-regulatory process that involves the regulator (possibly the European Commission, the Berec or an ad hoc EU authority enjoying enforcing powers), the platforms, the business users and the consumers (using regulatory sandboxes); and (3) enforced through legal and other empowerment tools, rather than sole fines. As far as the observations are concerned, our starting point is the EDPB's important statement (2018a), according to which: algorithms are subject to bias and 'can result in assessments based on imprecise projections'. (p. 27) Therefore, it is crucial to 'carry out frequent assessments on the data sets. to check for any bias, and develop ways to address any prejudicial elements, including overreliance on correlations'.
Those checks and audits require 'regular reviews of the accuracy and relevance of automated decisionmaking, including profiling. not only at the design stage but also continuously' (p. 28). This affects algorithms used by platforms, which are subject to frequent changes to provide for the best products and services. And makes EU legislative duty to disclose the 'main parameters' of algorithmic rankings 59 of little value for business users, because such parameters can become rapidly obsolescent, making their disclosure not timely, or the chosen format outdated or its content meaningless. Therefore, instead of requesting the platforms to provide for access to their data or their algorithms, we suggest instead that the disclosure duties to which they are subject (which may regard ranking parameters but also other contractual clauses), be drafted in a completely different fashion.
In the knowledge graph, both the texts and behavioral data would be integrated employing SMEs' user experience. 65 Behavioral data coming from the regulatory sandbox would be used to confirm or contradict the links described by the graph. 66 he human presence, as said, is essential to monitor if errors occur in the building of the knowledge graph: technicians supervising in the sandbox may intervene to eventually deactivate any error that may occur in the algorithm. That implies that we need technicians to participate in the sandbox, besides regulators, firms (platforms and SMEs), and consumers. It is important to stress that there would be no need for the platform to disclose any of its own algorithms (which might easily remain secret) to other stakeholders participating in the trials. 67 That is because the kinds of algorithms that are needed to get to targeted algorithmic disclosures are either available on an open access basis, 68 or because it could be provided by the regulator itself (Di Porto 2020).
The consumers and SMEs contribute with their behavioral data to feed the algorithm: for instance, in case of disclosures of standard form contracts (typically the fine print one finds online and hardly reads), the experimental phase would consist of the stakeholders testing different formats of ToCs. 69 esting is also relevant to implement rapid amendments to the algorithmic disclosures, should any major risks associated with AI-led decisions emerge during the training (such as algorithmic biases in rankings, overreliance on correlation or discrimination). Following the best practice identified by the Art. 29 WG, such modifications would feedback into the algorithm to ameliorate it and, consequently, the disclosures. Indeed, the pre-testing phase also allows detecting with some precision what are the informational needs and understanding capabilities of the business users.