dataesr/scientific-paragraphs-categorization huggingface dataset:
Model validation. Although there are many SDMs, none stands out as better than the others  14. To improve the predictions, we therefore used an ensemble forecast approach, which combines several models weighted by their predictive accuracy  53, 54. Following recommendations in Barbet-Massin et al. 55  , we built our ensemble model with 10 runs of gradient boosting models (GBMs), generalized additive models (GAMs) and Maxent. We used 1000 pseudo-absences for the GBMs, and as many pseudo-absences as presences for the GAMs. We used 80% of the data for training the ensemble model and testing the single run of model, and 20% for validation. Each model was cross-validated with a fivefold procedure in package biomod2 (Supplementary Material 1), thus leading to 5 fits for each type of model and each pseudo-absences run.
We then evaluated the predictive accuracy of individual models with the true skill statistic (TSS) and the area under the receiving operating curve (AUROC). The TSS metric represents the ratio of hit rate to false alarm rate and varies from -1 to + 1  56. We used a threshold of TSS = 0. 4 to include models into the ensemble forecast  56. The AUROC is a measure of "separability", which represents the true positive rates graphically against the true negative rates. Following Fawcett  57  , we retained models with AUC > 0. 7 for the ensemble model. Finally, we converted the probabilities of presence predicted by the ensemble model into a binary presence/absence, with a cut point based on predictions which maximized the TSS (Supplementary Material 1). In package biomod2, the relative variable contribution is assessed based on the correlation between the prediction of a model including a given variable and the model where this variable was dropped.
However, juveniles of D. fimbriatus are generally found in the surrounding vegetation rather than on the water  35  , which makes aquatic dispersal unlikely. We modelled dispersal ability via the MigClim package (Supplementary Material 1), based on the predicted map of the BLU model. For each species, the MigClim model evaluates if suitable cells of the raster could become accessible between current time and 2050/2070. The package uses a dispersal kernel, i. e. , a vector of probabilities of dispersal, to simulate the dispersal of the species (Supplementary Table  S1 ). We used an imperviousness map50 to locate areas where the species settlement is highly unlikely. Since both fishing spiders are water-dependent, impervious regions where the soil seals, are barrier to settlement. Part of the MigClim modelling process is random  58  , so we replicated each model 30 times and model-averaged the estimates.
We considered that the probability of a settlement was similar for both species. Also, we hypothesized that it takes two years for a newly colonized area to produce new propagules, based on the > 2-year lifespan of spiders in Northern Europe  35. Accounting for landscape connectivity. We used the Circuitscape software  63  to predict the potential dispersal corridors that Dolomedes could use to colonize their suitable habitat. Circuit theory estimates multiple pathways based on the resistance and conductance of the landscape  64. We used the habitat suitability prediction map from our BLU model to define the resistance map used by Circuitscape. We transformed the estimates of habitat suitability according to recommendations in Keeley  65  (see also Supplementary Material 3). We used a "wall-to-wall" approach  66, 67  which estimates the conductivity of the landscape from South to North, and from West to East.
A consensus map was produced by multiplying the resistance layers of different directions. This consensus map was an estimation of the landscape connectivity for the two species. The consensus map was binarized by considering conductance higher than mean conductance plus standard deviation as corridors  67. Areas outside corridors were then considered as a barrier to short-distance dispersal in Migclim. Migclim was parametrized as for the model Disp but accounting for the landscape connectivity barrier to make predictions for model DispCS. Range expansion and geographic overlap in time. We compared suitable habitat predicted across species, models, and scenarios. To estimate the range expansion or reduction in the future, we used the biomod2 package in R. We compared the direction of the shift in suitable habitat by calculating the centre of gravity of the suitable range with the SDMTools package (Supplementary Material 1).
Since dispersal is not homogeneous within and among species  91  , a more realistic model should include information on dispersal and population size for each presence observation. The sampling of all sites is necessary to collect this information. There is a considerable gap between the theory and actual applications of data-demanding mechanistic SDMs  26. Knowing that the most used habitat is not necessarily the most suitable for the fitness of the species  92  , we used a hybrid model based on the lack of sufficient data for a full mechanistic model. Moreover, accounting for thermal niche information is possible with mechanistic models  93, 94. Including the lower lethal limit of Dolomedes could be relevant to estimate their future distributions. Indeed, we used air temperature data to characterize the temperature in our SDMs, but Dolomedes spiders overwinter under the snow.
The Epistemology of RCTs in the Field of Development In theory, randomistas see experimentation precisely as an antidote to preconceived ideas (see also  Rodrik 2009). This pragmatism may well give the impression of being a rejection of scientism. Yet laying claim to the method's superiority clearly reflects a scientistic concept of science  (Picciotto, Chapter 9). This scientism can be seen at work in two ways. First of all, the randomistas purport to provide universal answers for a large number of development interventions. In response to the question of contextual particularities, some randomistas like Esther Duflo argue that they should be considered as "global public goods" and an international body established to scale them up  (Savedoff et al. 2006; Glennerster 2012). This body would then build a universal database and act as a "clearing house, " providing answers as to "what works and doesn't work" in development  (Banerjee and He 2008; Duflo and Kremer 2005 ).
The Influence of the Randomistas on Development Research Early examples of the use of RCTs in social policy contexts include the various experiments on US social policies starting in the 1960s. 30 With regard to the recent development applications, the 3ie database has 133 published RCTs over the period 1981-1999. The earliest RCT in the database is from a World Bank research project on education interventions (textbooks and radio lessons) to improve the math scores of students in Nicaragua, namely  Jamison et al. (1981). 31 Among the pre-2000 RCTs, that done by the Government of Mexico for the Progresa evaluation, which started in 1997, is an especially notable example. The (generally positive) results in the literature generated by the data from that RCT were influential in the expansion of Conditional Cash Transfers to over 50 countries today.
Imbalances of this sort raise further ethical concerns, given pressing development challenges and limited resources for research. The next section takes up these issues. 52 This idea has been a much debated in biomedical applications, notably in the context of the revisions done in 2000 to the World Medical Association's 1964 Helsinki Declaration. For further discussion see  Levine (2006). 53 These are getting serious attention in biomedical research. For example, the US Food and Drug Administration (2010) has issued guidelines for adaptive evaluations. Also see  Cox and Reid (2000, Chapter 3 ). 54 Also see  Chassang et al. (2012)  and the discussion in  Özler (2018). 55 These are sometimes referred to as systematic reviews; see for example, the 3ie searchable database and the Campbell Collaboration on such reviews.
1). However, as discussed already, neither the internal nor the external validity of these development RCTs is fully evident. We do not know the counterfactual-what we would have learnt if those resources (financial and human capital) had been deployed elsewhere. The substitution effect relates to the methods used. Take, for example, the World Bank. While the earliest RCT in the 3ie database is by the Bank, until the early 2000s the tool was seen as only one of many credible options for IE. Since then there has been a marked switch in favor of RCTs within the Bank, which has been applauded by some observers; for example, an editorial in The Lancet declared (in ignorance of more than the history) that "The World Bank is finally embracing science. "  (Lancet 2004: 731). 65 The Bank's Independent Evaluation Group (IEG) reports that over 80 percent of the impact evaluations starting in 2007-10 used randomization, as compared to 57 percent in 2005-06 and only 19 percent in prior years  (World Bank 2012).
66 It is plausible that favoring RCTs exacerbates a myopia bias in development knowledge. This is not just happening in the World Bank. The sectoral bias in the use of RCTs more broadly is evident from the results of  Cameron et al. (2016)  who provide a cross-tab of over 2200 published impact evaluations (in the aforementioned 3ie database) by method and sector. 67 Overall, about two-thirds of these evaluations use RCTs, but the RCTs tend to be concentrated in certain sectors, notably education (58 percent used an RCT), health, nutrition, and population (83 percent; 93 percent in health alone), information and communications technology (67 percent), and water and sanitation (72 percent). Observational studies are more common-with under one-third using an RCT-in agriculture and rural development, economic policy, energy, environment and disaster management, private sector development, transportation, and urban development.
Arnold et al. (2018) , in contrast, point to the large number of poorly structured, householdlevel observational studies as evidence for a claim that observational studies in the literature predicted that these RCTs would find statistically signifi cant effects. In particular,  Arnold et al. show that children in the baseline dataset from the same sites are taller, on average, in households that have latrines than in households that do not. Based on their comparison, they conclude that, because their RCT did not show an effect, this contrast offers a reason to doubt all observa tional evidence in general, even evidence using careful strategies, and even evidence asking different questions. We read this as a straw man argument: of course it is possible to construct an observational study that is not credible, as Arnold et al.
It represents a decisive advantage when it comes to generalization. Not only does the Special Issue appear decisive in terms of results, but it also marks a 'good practices' shift by RCT proponents. Hence the issue seeks to address a number of limitations. For the first time, the issue as a whole, and the General Introduction in particular, provide elements of response to five types of recurrent criticisms of the pro-RCT movement  (Bédécarrats, Guérin, and Roubaud 2019 ): a theoretical model is developed in response to the agnostic empiricism criticism of RCTs; a cost-benefit analysis is proposed to answer the question of effectiveness, to move beyond mere causal impact; the issues of take-up rate, estimator accuracy and treatment heterogeneity are acknowledged and discussed; contextual diversity is addressed by a range of settings, products, and institutions covered by the six papers, enabling the Special Issue's editors to claim their sample is "fairly representative of the microcredit industry/movement worldwide" (Banerjee, Karlan, and Zinman 2015: 2); and, lastly, the Special Issue professes to make available the original databases in response to the complaint about replicability and in order to facilitate meta-analyses.
This is why AFD teams constantly seek to combine impact studies with lighter-weight operational evaluations, developing a diversified range of measurement tools to best capture aid effectiveness. In particular, we plan on making more use of science for evaluation, working with partners such as the French National Research Institute for Sustainable Development (IRD) and the world of research in general, including by promoting research conducted by partners in the Global South. I have spent most of the forty years of my academic career working closely with epidemiologists and biostatisticians who long upheld randomized trials as a key tool for rationalizing medical practice on the basis of scientific evidence, but ul tim ate ly expressed concerns about relying too much on randomized design. RCTs have long been presumed to be the ideal source for data on the effects of medical treatment.
What is the most appropriate strategy to reward bill collectors, sanitation workers, building inspectors, etc? r. What are the most effective strategies to minimize littering? We have placed several litter bins in public places, still people not using them. So, what should be done? Why has India become such a popular destination of RCTs? India has practically become a hub for RCT studies among developing countries. The American Economic Association data on RCTs lists 247 RCTs conducted in India since 2012. This is the highest in the world after the US. Out of these 137 were funded by JPAL. Researchers prefer India to other poor countries. This may be because English is commonly spoken and written in most of the country, there is a fair degree of peace and safety relative to other developing countries where violence and regular work disruptions can be a problem, upsetting experiments that run for even a few short months due to political uncertainty.
Is doing RCTs and efficient allocation of scare resources for the Indian economy? If I were a central planner allocating resources to doing economics better in India, it seems obvious to me that the highest bang for the buck lies in creating more and better data of many kinds. For example in India, we don't have good GDP, consumption, or employment data. There is a debate about how many people in India are unemployed. Policies made in the absence of data is like driving in the blind. From the perspective of an economist, it would be more useful to spend resources to measure the economy, jobs, labor markets, etc. We don't have measure of informal sector. We don't have measures of innovation or skills or prod uctiv ity, which are crucial to policy-making and driving growth. We are driving in the blind when it comes to the economy as a whole.
While we can find from RCTs the behavior of agents in local credit markets, we struggle with measuring trends in interest rates in informal credit markets. Economic data for households in India is limited. As an example, it is only in recent years that the first panel dataset about households has come about in India, which observes a sample of households three times a year. Such a dataset requires resourcing that is comparable to a few RCT papers, and will create an entire body of new knowledge about India. A lot of that knowledge will be descriptive, and some of it will be quasi-experimental. This expenditure seems to be a better use of resources, when compared with a few RCT papers which would spend the same amount of money and rarely give data, and the research is non-replicable. Panel datasets create replication and competition between researchers (at modest expenditure points), in a way that RCT papers do not.
Panel datasets create sustained measurement of simple facts about the country (e. g. basic facts about Women's labor force participation) which RCT projects do not. Panel datasets are much more valuable for the development of an Indian policy-relevant research community. I can see how the agency problems of government and philanthropic organizations, coupled with the solution seeking that has been fashionable in recent years, has created incentives to 'discover what works' , at the expense of the more complex construction of knowledge of the humanities and social science that is required for policy-making. Many years into the RCT revolution, I would argue in favor of shifting away towards more traditional approaches. Easterly, W. 4n. 8, [11] [12] [13] 25n. 22, 37, 40, 44, 101, 127, 135, 233, 244, 248n.
Acknowledgements For (generous, helpful, and amazingly rapid) comments on an earlier version, I am most grateful to  Nancy Cartwright ,  Anne Case ,  Shoumitro Chatterjee ,  Nicolas Côté ,  Jean Drèze ,  William Easterly ,  Reetika Khera ,  Lant Pritchett ,  Dean Spears , and  Bastian Steuwer. I acknowledge financial support from the  National Institute for Aging through NBER , Grant No. P01AG05842.
Acknowledgement François Roubaud encouraged the author to write this chapter. The author thanks  Jason Abaluck ,  Sarah Baird ,  Radu Ban ,  Mary Ann Bronson ,  Caitlin Brown ,  Sylvain Chabé-Ferret ,  Kevin Donovan ,  Ryan Edwards ,  Markus Goldstein ,  Miguel Hernan ,  Emmanuel Jimenez ,  Max Kasy ,  Madhulika Khanna ,  Nishtha Kochhar ,  Agnès Labrousse ,  Andrew Leigh ,  David McKenzie ,  Rachael Meager ,  Berk Özler ,  Dina Pomeranz ,  Lant Pritchett ,  Milan Thomas ,  Vinod Thomas ,  Eva Vivalt ,  Dominique van de Walle ,  Andrew Zeitlin , and participants at an authors' workshop in Paris, March 2019. The staff of the  International Initiative  for Impact Evaluation kindly provided an update to their database on published impact evaluations and helped with the author's questions. Acknowledgement I'm grateful for comments from  Isabelle Guérin ,  Florent Bédécarrats ,  François Roubaud ,  Tim Ogden , and  Martin Ravallion  and for discussions with  Tim Ogden ,  Michael Kremer ,  Lant Pritchett , and participants at the conference on RCTs in Development at  Agence Française de Développement , March 19 and 20, 2019.
Acknowledgement Thanks to  Jonathan Morduch ,  David McKenzie ,  Lant Pritchett , and  Isabelle Guérin  for helpful discussions in developing this chapter, as well as the participants in the AFD Workshop Randomized Control Trials in the Field of Development: The  Gold Standard Revisited (Paris, March 2019 ). Also thanks to  Cynthia Kinnan ,  Jessica Goldberg ,  Johannes Haushaufer ,  Cyrus Samii , and  Bruce Wydick  for helpful pointers, and the three editors of the book,  Florent Bédécarrats ,  Isabelle Guérin , and  François Roubaud , for valuable comments and discussions.
Acknowledgements The authors thank  Cécile Abramowicz ,  Britta Augsburg ,  Marie Brière ,  Andres Garchitorena ,  Marek Hudon ,  Marc Labie ,  Jonathan Morduch ,  Tim Ogden ,  Martin Ravallion , the participants to the AFD Workshop Randomized Control Trials in the Field of Development: The  Gold Standard Revisited (Paris, March 2019 ), and the three editors of the book,  Florent Bédécarrats ,  Isabelle Guérin , and  François Roubaud , for valuable comments and discussions.
3ie database, covering the period 1981-2015, of which 4338 were published in 2000-2015. The annual rates are 271 since 2000 and 9 for 1981-1999. 3 The 3ie series is constructed by searching for selected keywords in digitized texts. 3ie staff warned me (in correspondence) that their old search protocols were probably less effective in picking up observational studies relative to RCTs prior to 2000. So the earlier, lower, counts of non-randomized evaluations in Figure1. 1 may be deceptive. The 3ie counts pick up many more RCTs than reported in Bouguen et al. (2019)  (also noting that the latter give cumulative totals not annual flows).
Acknowledgements We are grateful to  Mr. Frédéric Gimat ,  Mrs. Aurélie Chan  and  Mrs. Ludivine Sariman , all from the  Service d'Inspection Vétérinaire Alimentaire et Phytosanitaire (SIVAP , the  New Caledonian Biosecurity Service) of the DAVAR, Government of New Caledonia , for their support and to provide intercepted material to us to allow identifications of intercepted specimens on the New Caledonian borders. We thank all the collectors who brought specimens of aphids available to the lab. We thank  Mr. Frédéric Rigault  ( IRD ) for the map he provided. We also thank  Mr. Haruki Suenaga  for the access to an important reference. Funding for this research was provided by the core budget of the  Institut Agronomique néo-Calédonien. We also thank the reviewers who highly improved the redaction of this article.
a Total number of mRNA studied by whole transcriptome stability analysis in pancreatic Sh and control Sc cells (see text for details). b Example of one Galectin-3 stabilised (ATF6) and one Galectin-3 destabilised mRNA (EIF2AK1). c, d Significantly enriched organelles (c) or biological pathways (d) to which belong the mRNA stabilised or destabilised by Galectin-3 (n = 440) as determined by enrichment analysis performed with the GSEA tool (the number of mRNAs in the experimental dataset which overlaps with the reference datasets is shown under brackets; horizontal bar indicates the p value of the Fisher's exact test (-log10). Examples of mRNA species stabilised/destabilised by Galectin-3 belonging to three different pathways are shown in the table (mRNA species in black were assigned to the biological category by the GSEA analysis whereas those in blue were manually assigned based on AmiGO-derived annotation; in bold dark are shown mRNAs which were assigned to mitochondria as cellular component by GSEA; underlined are shown mRNAs which were studied in e).
Discussion Our data showed that Galectin-3 behaves as a selective post-transcriptional regulator which coordinates the expression of 13 RNA regulons which merge subsets of functionally related mRNAs  26. Some RNA regulons control a biological function previously linked to Galectin-3, such as cell death/apoptosis  27  or protein secretion  28  , which strengthens the validity of our experimental approach. Interestingly, other RNA regulons such as fatty acid metabolism, cholesterol homeostasis or UPR were previously rarely or never related to Galectin-3. We thus focused our work on Galectin-3's control of the mitochondrial bioenergetics and the ER stress response in epithelial cancer cells. Regarding the mitochondrial function, our data demonstrated a more efficient OXPHOS in the absence of Galectin-3, and especially a higher activity of complex I (NADH oxidase) and complex IV (cytochrome C oxidase) in pancreatic cancer cells.
, using JASPAR, REACTOME, and TRUSST databases. Expression level is shown as the Sc cells mRNA levels/Sh cells mRNA levels ratio. The length of the graph bar is related to the level of statistical significance of variation. c ATF6 pathway activation: Variation of full length and cleaved ATF6 proteins (upper panel) during Thapsigargin treatment studied by western blotting (n = 2 independent experiments). Fold change in protein levels was analysed by densitometry (medium panel). Expression level was arbitrarily set at 100% for Sc cells. Data are shown as mean ± SEM and were compared by Student's t test. Variation of the mRNA levels of MBTPS1, an ATF6-N target gene, was followed by real-time qPCR both at baseline and during Tg treatment (n = 2, lower panel). d. IRE/Xbp-1s activation: variation of XBP-1s protein levels (upper part) by western blotting (n = 3) and variation of the mRNA levels of EDEM1 and ERP44 (two XBP1s targets, lower part) was followed by real-time qPCR both at baseline and during Tg treatment (n = 2).