dataesr/scientific-paragraphs-categorization huggingface dataset:
Analogous conclusions are drawn from a restricted sample of the new dataset to match the country coverage of Table  3. From these various anchoring exercises, a clear recommendation emerges. Averaging the explanatory variables within the fiveyear spell should be favored over initial values, except for the convergence variable. 12 It is preferable to keep more observations through data averaged over sub-periods, while controlling for endogeneity biases by properly instrumenting the explanatory variables. 13 Otherwise, coefficient estimates remain exposed to mismeasured true explanatory variables.
14 Fortunately, there are two usual telltale signs: a number of instruments greater than the number of crosssectional units (the number of countries), and a perfect Hansen test p-value of 1. 00. The non-linear conclusions systematically meet both telltale signs. Column (1) of Table  5  presents the typical non-linear conclusion based on the old dataset. The coefficient estimates on private credit are significant, and the SLM test corroborates the presence of an inverted U-shape relationship. It indicates that financial depth starts yielding negative returns as credit to the private sector reaches 90% of GDP. However, there are no less than 318 instruments in this default implementation of the System GMM estimator, for only 130 cross-sectional units. Along with the perfect p-value of 1. 00 for the Hansen test, this casts doubts on the reliability of the result, with possible overfitting and failure to expunge the endogenous part as the tests would be weakened in this setup.
To circumvent this issue,  Bazzi and Clemens (2013)  have come up with a simple "2SLS analog" technique. Since weak instrument tests are available within the 2SLS setup, carrying out the equivalent regression using 2SLS with the identical GMM-type instrument matrix provides "simple and transparent tests of instrument strength in a closely related setting"  (Bazzi and Clemens, 2013, p. 167). This exercise requires to split the System GMM in two: the difference part and the level part. Table  6  reports point estimates for this exercise along with various specification tests for the typical threshold from the old dataset. Once again, the table displays tests for underidentification (Kleibergen-Paap LM test) and weak instruments (Cragg-Donald and Kleibergen-Paap Wald tests). 16  Column (1) provides the benchmark System GMM estimates.
The panel regressions are based on ten non-overlapping five-year spells and contain time fixed effects. The dependent variable is the average real GDP per capita growth rate. While the first column simply reproduce the baseline result from the old dataset for 1960-2010 (see Table  5 , column (1)), the subsequent columns report the decomposition of the System GMM following the "2SLS analogs" of  Bazzi and Clemens (2013). Windmeijer (2005)  standard errors in parentheses. * * * p < 0. 01, * * p < 0. 05, * p < 0. 10. Chatelain and Ralf (2014)  have documented that including an additional classical suppressor, highly correlated with the first one, may lead to very large and statistically significant point estimates. Unfortunately, these results are spurious and outliers driven. The typical additional classical suppressor in dynamic panel setup is the square term of the first one.
The null hypothesis of the AR(2) serial correlation test is that the errors in the first difference regression exhibit no second-order serial correlation. The null hypothesis of the Hansen test is that the instruments fail to identify the same vector of parameters (see  Parentes and Silva, 2012). The SLM test provides p-value for the relevance of the estimated threshold. The absence of p-value for columns (3) to (  5 ) is due to a trivial rejection of the inverted U-shape. Robust  Windmeijer (2005)  standard errors in parentheses. * * * p < 0. 01, * * p < 0. 05, * p < 0. 10. Column (3) shows OLS fixed effects estimates of the same model as the baseline results in column (1). Column (4) displays the OLS fixed effect estimates similar to column (2). Finally, column (5) presents the same regressions using this time the new dataset.
Column (2) is based on the new dataset with the same time coverage as column (1) but with additional countries. Column (3) expands the coverage up to 2015. Columns (4-5) incorporate a slight methodological correction. The SLM test provides p-value for the relevance of the estimated threshold. Robust standard errors in parentheses. * * * p < 0. 01, * * p < 0. 05, * p < 0. 10.
Cross-country OLS Between regressions. This table reports the results of a set of cross-country OLS Between regressions in which the dependent variable is the average real GDP per capita growth rate. While the first column provides a benchmark of the typical non-linear result from the old dataset, the subsequent columns report various exercises based on the new data set expanding the period and country coverage. Column (2) presents a reassessment. Column (3) excludes CHE, JPN, and USA. Column (4) incorporates a slight methodological correction. The SLM test provides p-value for the relevance of the estimated threshold. Robust standard errors in parentheses.
Windmeijer (2005)a, 2012)he five-year spells in dynamic panel regressions (1/2). This table reports the results of a set of panel regressions consisting of non-overlapping five-year spells. The dependent variable is the average real GDP per capita growth rate. All regressions contain time fixed effects. The first column reports the best-attempted replication of the typical threshold result from the yearly version of the old dataset. Column (2) provides point estimates with a one-year forward shift for the starting point of each spell. The subsequent columns continue shifting forward by one year the beginning of the five-year spells. The null hypothesis of the AR(2) serial correlation test is that the errors in the first difference regression exhibit no second-order serial correlation. The null hypothesis of the Hansen test is that the instruments fail to identify the same vector of parameters (seeParentes and Silva, 2012).
Windmeijer (2005)a, 2012)he five-year spells in dynamic panel regressions (2/2). This table reports the results of a set of panel regressions consisting of non-overlapping five-year spells. The dependent variable is the average real GDP per capita growth rate. All regressions contain time fixed effects. Each column presents one possible anchoring for the five-year spells in the new dataset. The null hypothesis of the AR(2) serial correlation test is that the errors in the first difference regression exhibit no second-order serial correlation. The null hypothesis of the Hansen test is that the instruments fail to identify the same vector of parameters (seeParentes and Silva, 2012). RobustWindmeijer (2005)standard errors in parentheses.
Windmeijer (2005)a, 2012)in System GMM panel regressions for1960-2010. Notes:This table reports the results of a set of panel regressions consisting of ten non-overlapping five-year spells. The dependent variable is the average real GDP per capita growth rate. All regressions contain time fixed effects. While the first column reports a replication of the typical threshold result from the old dataset for 1960-2010, the subsequent columns report various instrument reduction exercises. The null hypothesis of the AR(2) serial correlation test is that the errors in the first difference regression exhibit no second-order serial correlation. The null hypothesis of the Hansen test is that the instruments fail to identify the same vector of parameters (seeParentes and Silva, 2012). The SLM test provides p-value for the relevance of the estimated threshold.
Acknowledgement The author thanks  Jean-Bernard Chatelain  for his support and fruitful discussions, as well as the editor, an anonymous referee,  Olivier Damette ,  Jérôme Héricourt ,  Samuel Ligonnière , and seminar participants at  Paris School of Economics , the  GDR "Money, Bank, Finance" at the Alexandru Ioan Cuza Iaşi University in Romania , the 14th BiGSEM Workshop at the  Bielefeld University in Germany  and the 3rd Ermees Macroeconomic Workshop at the  Strasbourg University in France  for helpful comments and suggestions.
Acknowledgements This manuscript is dedicated to  Susan Abmayr , whose important contribution to the above discussed topics is unforgettable. We thank  P. Schultz  and  A. Ben-Shem  for stimulating and insightful discussions. We apologize to our colleagues whose work could not be cited due to space limitations. This study was supported by  NIH   RO1 grant  ( 1R01GM131626-01  to L. T. ), by the  Agence Nationale de la Recherche  ( ANR-15-CE12-0009-01  and  ANR-15-CE11-0022-03  to  D. H. ,  AAPG2019 PICen  to G. P. and L. T. ,  ANR-PRCI-AAPG2019 EpiCAST  to L. T. ,  ANR-18-CE12-0026  to D. D. ), grant  ANR-10-LABX-0030-INRT  and a  French State  fund managed by the  ANR  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02  to IGBMC.
The strong summer convection in the region is controlled by atmospheric processes that are part of the development of the South American Monsoon System (SAMS;  Vera et al. 2006 ; see section 3. 1 for details) and which may be sensitive to impacts on surface energy/water fluxes induced by land use change  (Li and Fu 2004). Taking into account the potential sensitivity of the Amazon-Andes transition region to deforestation and the need for high-resolution studies, the present work analyzes the deforestation land-use change impacts on regional energy and water budgets, as well as rainfall alterations at a local scale through the use of high-resolution simulations (up to 1km horizontal grid size) with the regional climate model Weather Research and Forecasting Model (WRF). Model hourly outputs are compared with observational data from rain-gauges and satellites in order to elucidate the possible changes caused by Amazonian deforestation in the diurnal and nocturnal precipitation inside a typical Andean valley during 2001-2011 austral summer.
The second and third domains have a spatial resolutions of 5 km x 5 km and 1 km x 1 km, respectively and cover the south-western part of the basin. Time steps for these two domains are 30 seconds and 6 seconds, respectively. Domain 3 comprises the instrumented Andean valleys of Zongo and Huarinilla used for model validation (Fig. 1d ). In order to analyze the diurnal and nocturnal atmospheric conditions we use an output time resolution of 3 hours for domains 1 and 2, and 1 hour for the finest domain. Lateral boundary conditions from ERA5 reanalysis  (Hersbach et al. 2020)  are used. This reanalysis presents an enhanced horizontal resolution of 0. 28° x 0. 28° (31 km) with data available every 6 hours (https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era5). Significant improvements in the representation of several model processes in this reanalysis are reflected in a better fit in winds, temperature and humidity in the troposphere compared against radiosonde data  (Hersbach et al.
Description of the experiments For each austral summer in the period 2001-2011, we conduct two simulations with WRF sharing the same atmospheric lateral boundary conditions, but with different land-cover states. The control simulation ("Control" or "Con") corresponds to vegetation conditions for the year 2000 using the land cover map developed for South America by  Eva et al. (2004)  (Fig. 1a ). This dataset was implemented in WRF by  Saavedra et al. (2020)  and exhibits a spatial resolution of 1 km x 1km. For the deforested simulation ("Deforested" or "Def"), we use the deforested scenario 2050 business-as-usual from  Soares-Filho et al. (2006; Fig. 1b ). This scenario reaches 45% of forest lost and assumes the continuation of the 1997-2002 registered deforestation rates, no definition of new protected areas in the basin and the building of all planned highways  (Soares-Filho et al.
1d ) from the Bolivian Electric Power Company (COBEE) and managed in collaboration with the Institute of Research for the Development (IRD) -France. Additionally, three stations with hourly data were used from  Schwabe et al. (2011). Table  3  presents the general information of each rain-gauge station. The quality assessment of these rainfall stations was carried out by previous works  (Molina-Carpio et al. 2019 and Laura Ticona, personal communication). The use of in-situ information also allows characterizing the model skill to capture the precipitation variability at small spatial scale in an Andean valley where local processes are important  (Egger et al. 2005; Trachte et al. 2010; Junquas et al. 2018). Additionally, the representation of the regional atmospheric circulation and moisture transport are validated by a direct comparison between WRF outputs and ERA5 data.
To test whether WRF responds realistically to the changes induced by land-use alterations, we use observations of energy surface fluxes, air temperature, precipitation and wind speed from the LBA project  (Saleska et al. 2013 ). This dataset is available at the website https://daac. ornl. gov/LBA/guides/CD32_Brazil_Flux_Network. html. The Large-Scale Biosphere-Atmosphere (LBA) project was a large scientific effort carried between 1996 and 2006 for assessing the influence of tropical forest conversion in carbon storage, nutrient dynamics and trace gas fluxes in the Amazon. Since the region of interest of this study is the Amazon-Andes transition region, we only use the closest stations from the LBA project that fits inside our second modelling domain (Fig. 1c ). Because of their geographical location, these stations experience annual cycles of precipitation and temperature similar to the south-western Amazon variability.
Acknowledgments This research has been supported by the  French AMANECER- MOPGA  project funded by  ANR  and  IRD  (ref. ANR-18-MPGA-0008 ). Jorge Molina-Carpio was supported by the  Universidad Mayor de San Andres (UMSA)  within the framework provided by the  PHYBAAM (Processus Hydrologiques des Bassins Andins Amazoniens) Project and HYdrogéochimie du Bassin AMazonien (HYBAM) program. We would like to thank  Marcus Schawe  for providing the rain-gauge data for  Hornuni Valley.
ACKNOWLEDGMENT This paper is dedicated to Jerome Manuceau, Eric Bernard, Spiros Manuceau, and Giovanni Bouder. I would like to thank my close family, Nadine, Julien, Helene, Ianis, and Olivier, for all their help and encouragement during this research, whom without this would not have been possible. I also appreciated all the support I received from the rest of my family and friends, with special thanks to my friends Elkana Lesmond and Michel Pedurand. Lastly, I would like to thank the French Government for their social grants that allowed me to conduct this self-funded and independent research.
Although the results demonstrate that ChREBP does contribute to the induction of TxNIP in refed conditions, our data also suggest that other molecular mechanisms may be involved. Indeed, a significant (although reduced) glucose effect was maintained on Txnip in Chrebp À/À  hepatocytes challenged with high glucose concentrations. One possible candidate for mediating this effect could be MondoA, the paralog of ChREBP  (Richards et al. , 2017) , which was shown to be important for Txnip expression in skeletal muscles  (Ahn et al. , 2016). Interestingly, we also reported that glucose still induces Txnip expression in islets from Chrebp À/À mice and that this effect does in fact require MondoA in human b cells  (Richards et al. , 2018). While MondoA functions were predominantly reported in skeletal muscle and proliferative cells  (Wilde and Ayer, 2015) , a role for MondoA in hepatocytes should not be excluded.
, 2016) , plays a crucial role in promoting Txnip expression when insulin signaling is disrupted in the liver of LIR-KO mice. Studies in transgenic mice expressing a constitutively active form of FoxO1 in the liver also demonstrated that FoxO1 promotes the expression of Txnip in the liver, and studies with adenoviral vectors demonstrated that FoxO1 stimulates hepatic Txnip expression in primary hepatocytes in a cell-autonomous fashion. Reporter gene studies and ChIP analysis showed that this effect of FoxO1 is mediated through the cis-acting FoxO1 target site located in the Txnip promoter, supporting the concept that TxNIP is a direct downstream target of FoxO1 in the liver  (Zhang et al. , 2012). Together, these data provide strong support for the concept that FoxO1 promotes TxNIP expression in the liver under conditions where insulin levels are low (fasting) or insulin signaling is impaired.
ACKNOWLEDGMENTS The authors thank  E. Anthony  ( U1016-Institut Cochin ),  C. Naylies  ( GeT-TRiX, INRA ToxAlim ), and  G. Michel  for providing excellent technical assistance. The authors would like to thank  R. Dentin  and  F. Levavasseur  ( U1016-Institut Cochin ) for the use of the ChREBP knockout mouse model. We also thank the animal facility staff ( EZOP, INRA ToxAlim ) for their excellent work. Postic's lab ( U1016-Institut Cochin ) is supported by grants from  Servier Laboratories, FRM (Foundation for the Medical Research ,  DEQ20150331744 ),  EFSD-Novonordisk (European Foundation for the Study of Diabetes ), and  National Agency for Research (ANR)  ( ANR-15-CE14-0026-Hepatokind ). Unterman's lab is supported by  VA Merit Review Grant  ( IO1BX001968 ).
4 ). This is based on morphology and anatomical criterions. So, the exclusion of indeterminate (or more really unnamed species), could strongly underestimates the observed diversity, just due to human bias. It is true that the levels of names seem to have been mixed, but the biological level are not, as only different species are included. They are assigned to a particular species (different from others), not to a particular name, the objective being to evaluate the biological diversity, not the name diversity. One of the limitation to using phylogenetic diversity estimates is their sensitivity to changes in phylogenetic hypotheses and the selective inclusion of wellknown taxa  (Lane et al. , 2005; Tennant, 2016). So, a dataset including these unidentified (or unattributed to a species name) specimen has been tested.
As the distribution of each observed species is restricted to a unique continent, I also have considered in the same dataset these indeterminate remains as a different species in the diversity counts. I considered that an isolated dyrosaurid skeleton in India (even not more precisely identified that Dyrosauridae indet. ) attests the presence of a dyrosaurid species in this continent (Fig. 4 ). As no dyrosaurid and gavialoid species has a transcontinental distribution (Figs. 1, 2), I hypothetized that the Indian dyrosaurid remains are representative of a different species than those found in other continents. This allows one to consider in the analysis the presence of tethysuchians and gavialoids in a continent where no identifiable remains at specific or generic levels have been found so far, in particular, the Indian continent (Appendix 3 of supplementary data).
In a second dataset, the indeterminate species have been included. Only well preserved taxa are included in phylogenetic analyses, and thus in phylogenetic diversity estimates. This conducts to underestimate the diversity and introduces biases in the analysis  (Lane et al. , 2005; Tennant, 2016). An empirical test suggests that diversity count including the species not included in the phylogenetic analyses is always less underestimated and closer to the diversity estimated with all phylogenetic hypotheses including this species than the diversity count excluding species not included in phylogeny (Fig. 5 ; Appendix 6 of Supplementary Data). This dataset will allow to test the impact of considering only species included in phylogenetic analyses on diversity estimate. In this dataset, the indeterminate species are excluded from the count for a continent where the phylogenetic tree suggests the presence of a ghost lineage (transparent taxa, Fig.
1 ), considering the possibility that the indeterminate taxa could be the ghost taxa (Appendix 5 of Supplementary data). The sampling used in the present analysis is low, and could be thus sensitive to changes in phylogenetic analyses, one of the lim- itation of using the phylogenetic diversity estimates  (Lane et al. , 2005; Tennant, 2016). This is particularly true for low sampling. To evaluate the robustness of the analysis and compare the results, I tested several datasets as suggested in previous works  (Brocklehurst, 2015; Dunhill et al. , 2018). To test the impact of recent discoveries on diversity curves  (Jouve et al. , 2017) , I constructed the same curves excluding recently described new or reattributed species from Bolivia  (Jouve et al. , 2021) , Paleocene pholidosaurid remains from Morocco  (Jouve and Jalil, 2020) , and the species Aigialosuchus villandensis herein tentatively considered as a tethysuchian (Appendix 4 of Supplementary data).
Five occurrences are thus excluded in this dataset (Appendices 7, 8 of Supplementary data). Also, the phylogenetic relationships of the gavialoids with other eusuchians is still strongly debated between molecular and morphological analyses (e. g. Brochu, 2003 Brochu, , 1999 Brochu, , 1997b;; Gatesy et al. , 2003). To reconcile both views, it has been suggested that Cretaceous-Paleocene fossil species considered as gavialoids in morphological analyses are, in fact, basal eusuchians (thoracosaurins), and only the post-Eocene forms could be real gavialoids, much more derived in the crocodylian tree  (Gatesy et al. , 2003; Lee and Yates, 2018). So, I also consider this possibility in the phylogenetic corrections, considering the most derived gavialoids (gryposuchins and Asian gavialoids) as being more closely related to tomistomines, excluded from the phylogenetic corrections, and considering only the thoracosaurins in another dataset (Appendix 9 of Supplementary data).
In summary, to evaluate the evolution of longirostrine crocodyliform diversity and test the impact of various corrections I used eight datasets: raw data; raw data excluding recent remains; raw data corrected with phylogeny; raw data corrected with phylogeny excluding species not included in phylogenetic analyses; raw data corrected with phylogeny excluding recent remains; taxic diversity corrected by phylogeny, per unit of time; taxic diversity corrected by phylogeny, per unit of time excluding recent remains; taxic diversity corrected by phylogeny, per unit of time excluding derived gavialoids; For each dataset, four graphical comparisons are conducted: tethysuchian and gavialoid diversity; Laurasian tethysuchian and gavialoid diversity compared with Gondwanan diversity; and global longirostrine crocodyliform diversity.
Late Cretaceous -Paleocene evolution The evolution of the longirostrine crocodyliform diversity throughout the Late Cretaceous-Paleocene varies according to the corrections applied to the dataset. Using raw data, the curves show an increase of diversity from Campanian to Selandian-Thantian (Fig. 6C ) in tethysuchians and Gondwanan continent (Fig. 6A, B ). This is strongly biased by the high number of Maastrichtian geological Formations (24) compared to those available for other stratigraphic levels (9 Campanian, 14 Danian, 12 Selandian-Thanetian) (Appendix 4 of Supplementary data), but also to the differences between Laurasian (15 Maastrichtian Formations) and Gondwanan (9 Maastrichtian Formations) provinces. The phylogenetic corrections of this dataset strongly impact the shape of the diversity curves (Fig.