dataesr/scientific-paragraphs-categorization huggingface dataset:
Verification This phase signifies verification of the approach which has been proposed in this research work. Due to the time stipulation of the research work and the cumbersomeness and difficulty in collecting manual data from Google Earth, data extraction using Google Earth for the French railway infrastructure was impossible. French railway system, just like any railway infrastructure, is brimming with civil engineering structures such as bridges, tunnels, and level crossings throughout the lengths of railway tracks. Out of all these structures, bridges and level crossings are the most frequently repeated structures that intersect or cross railway tracks. Based upon their frequent appearance along the paths of the railway tracks, four comparable parameters were developed, which were manually identified from Google Earth and, then compared with SNCF data for the same part of the French Railway Network for the sake of verification of the proposed method developed in this research work.
These four parameters are; a) Kilometer per bridge for Google Earth Data (K BG ), b) Kilometer per level crossing for Google Earth Data (K LG ), c) Kilometer per bridge for SNCF Data (K BS ) d) Kilometer per level crossing for SNCF Data (K LS ) In general, these parameters are simply the distances, after which each structure in consideration is repeated. These parameters were calculated using the following equations: K BG = Total length of the tracks measured in Google Earth No. of bridges counted in Google Earth (1) K LG = Total length of the tracks measured in Google Earth No. of level crossings counted in Google Earth (2) K BS = Total length of the tracks in French Railway system as per SNCF No. of bridges in French Railway system as per SNCF system (3) K LS = Total length of the tracks in French Railway system as per SNCF No.
of level crossings in French Railway system as per SNCF (4) Table  2  presents two unique values obtained from Eq. (3) and Eq. (  4 ), which describe that, on average, there is a bridge after every 1. 08 km while there is a level crossing for every 1. 67 km of the French Railway System. The significance of these values is such that if similar parameters (K BG and K LG ) are obtained using Google Earth for the data manually collected for one-third of the selected railway infrastructure and those values are found comparable to K BS and K LS (SNCF data), then, the newly developed approach of data collection is validated.
2e. In Fig. 2f , same bridge is shown through the 'Street View' option in which the user is virtually standing on the road bridge passing over the selected line indicated by the blue line. Track Multiplicity is one of the data collected in this research work using Google Earth. For the scope of this work, this is defined as the number of tracks running through the route per unit length. Track type (single, double, multiple) or purpose (proximity of a railway yard or station) changes track multiplicity and can easily be observed and measured, as presented in Fig. 2g. Another type of data that has been extracted through Google Earth is the measurement of the lengths of different structures. When collecting data for the number of bridges, these were divided into two categories; (a) Long bridges, which are longer than 50 m and (b) Short bridges, which are shorter than 50 m.
Hence, one-third of the total French Railway Network has been covered by length, and the proposed method was applied. Through the use of scientific justifications, assumptions and inference, the proposed method has been attempted to be justified. • Data collected through Google Earth may not be exactly identical to actual on-ground data, given that it is an all-out human effort. Data extraction for such a large portion of the complex system is very tedious and cumbersome. There were a very few portions of the map which were efficiently available on Google Earth due to weather affected imagery on the program (for example, clouds over a particular area), map distortion due to pixel problems or in very rare cases, failure to locate parts of railway track on the Google Earth corresponding to the actual one on SNCF map.
6) In this notation, the equations for the evolution of DM abundances caused by 2 → 2 reactions read dn µ dt = - α≤β; γ≤δ C αβ n α n β vσ α,β→γ,δ (δ µα + δ µβ -δ µγ -δ µδ ) -3H(T )n µ (3. 7) where H(T ) is the Hubble expansion rate. Taking into account entropy conservation ds dt = -3Hs we get 3H dY µ ds = α≤β; γ≤δ Y α Y β vσ α,β→γ,δ (δ µα + δ µβ -δ µγ -δ µδ ) (3. 8) By solving these equations, the relic densities, Ω 1,2,3 , can be calculated. We rely on micrOMEGAs for numerically solving this set of coupled equations. Since its version 4. 0, micrOMEGAs incorporated two-component dark matter scenarios, and has been used in the analysis of different Z N models  [42, 45, 57]. For this work, the code has been extended to models with three dark matter particles, a version that will become public shortly  [64].
Acknowledgments OZ and CY received funding from the  Patrimonio Autónomo  - Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación Francisco José de Caldas (MinCiencias -Colombia ) grant  82315-2021-1080. OZ has received further funding from  MinCiencias  through the Grant  80740-492-2021 , and has been partially supported by  Sostenibilidad-UdeA  and the  UdeA/CODI  Grant  2020-33177. CY would like to acknowledge support from the  ICTP  through the  Associates Programe  ( 2020-2025 ). The work of AP was carried out within the  scientific program  " Particle Physics and Cosmology" of the Russian National Center for Physics and Mathematics. JHEP03(2023 ) 100.
We investigate differences in predictions by individual models with a hierarchical clustering method. The rest of the paper is organised as follows. Section 2 describes the PlioMIP2 models, experiment design and simulation data that are relevant to this study. Section 3 addresses the following main questions: Q1 How do PlioMIP2 models perform in simulating the mPWP and the preindustrial climate of the MC (Sect. Q4 How can we best emphasise results from individual models that may otherwise disappear in the multi-model ensemble mean (MMM)? How can we account for the effect of any bias duplication caused by models from the same "family" within our analysis (Sect. 3. 4)? Finally, we discuss the drivers for changes in the ITF and of the climate of the MC (Sect. 4. 1), we summarise performance of individual models and of the MMM in simulating mPWP and preindustrial climate (Sect.
PlioMIP2 models PlioMIP  (Haywood et al. , 2011)  is a project aimed at studying climate and environments of the late Pliocene. PlioMIP is part of the Palaeoclimate Modelling Intercomparison Project (PMIP). In consideration of the development of models and proxy data in the past years, this project has progressed to the second phase (PlioMIP2;  Haywood et al. , 2016b Haywood et al. , , 2020)). In this ongoing phase, there are more models (17 models in comparison to 11 models that participated in PlioMIP phase 1; Table  1 ). Most of the newly participating models are descendants of existing models. For example, CCSM4 participated in PlioMIP1. The models derived from it, namely the Community Earth System Models of CCSM4-Utr, CCSM4-UoT, CESM1. 2 and CESM2, participated in the second phase; IPSL-CM5A from the Institute Pierre-Simon Laplace (IPSL) participated in PlioMIP and then joined PlioMIP2 with IPSL-CM5A2 and IPSL-CM6A-LR; the Norwegian Earth System Model (NorESM-L), which has been developed from CCSM4, participated in the first phase and then participated in the second phase with NorESM1-F.
Results In Sect. 3. 1, model results are compared to proxy reconstructions and the Hadley Centre Sea Ice And Sea Surface Temperature reanalysis version 1 (HadISST1) data set to evaluate model performance for reproducing mPWP and modern climate. In addition, the multi-model mean method (MMM) is evaluated in comparison to individual models. We illustrate the most relevant patterns of mean climate for the MC in mPWP relative to the preindustrial in terms of SST, precipitation minus evaporation, wind stress and salinity at the ocean surface (Sect. 3. 2). Then we analyse oceanic circulation in the MC (Sect. 3. 3). In Sect. 3. 4, we introduce a new metric, the multi-cluster mean (MCM), which takes the weight of models from the different clustering branches into consideration to better illustrate the results from multiple models.
Comparison with HadISST1 Observational data and reconstructed climatic information from proxy data can offer a way to evaluate simulation results. Here, we adopt HadISST1 data set  (Rayner, 2003)  from January 1870 to January 1900 to evaluate the model performance in reproducing preindustrial climate. The HadISST1 data set is available at https://www. metoffice. gov. uk/hadobs/ hadisst/ (last access: 19 October 2023). Figure  2  shows the spatial distribution pattern of the preindustrial SSTs over the MC from the HadISST1 reanalysed SSTs and from the PlioMIP2 MMM. The MMM reproduces the SST zonal gradient from the Equator to higher latitudes, but simulated SSTs are too warm around the tropical islands and too cold in the northwestern Pacific Ocean when compared to the reanalysis data. By calculating the root mean square error (RMSE) between reanalysed SSTs and model SSTs over the MC, we quantify the model and observation discrepancy for the modern period, which is shown in Fig.
Comparison with proxies For the mPWP, the data of multi-proxy reconstructed SST anomalies (SSTAs) compiled by  McClymont et al. (2020)  are available for a narrow time slice centred on 3. 205 Ma. These data  (McClymont et al. , 2020)  are compared with the simulated mPWP SSTAs in this study. Data and the full details for reconstructed SSTAs are available at https://doi. org/10. 1594/PANGAEA. 911847 and from  Mc-Clymont et al. (2020). There are four sites with available data over the MC, as shown in Fig. 4a. Sites ODP214 and ODP763 are located at the eastern Indian Ocean; site ODP806 is located at the western tropical Pacific Ocean; and site ODP1143 is located near the islands of the MC. The SSTA for ODP1143 is reconstructed from U K 37 with the alkenone-derived (BAYSPLINE) calibration  (Tierney and Tingley, 2018) ; the SSTAs for ODP763, ODP214 and ODP806 are reconstructed from planktonic foraminifera Trilobatus sacculifer Mg / Ca, with the Mg / Ca-derived (BAYMAG) calibration  (Tierney et al.
There is a big difference in the spatial distribution of anomalies between models. Because some models will amplify similar signals and because some climatic signals from individual models will get lost in the MMM, a new metric, MCM, has been introduced. It takes the weight of the model families into account, provides a new perspective on the ensemble of multiple model results and demonstrates different climate states by means of clusters. The evaluation of the MCM shows that it outperforms 12 out of 17 models in simulating preindustrial climate and shows a discrepancy that is less than or equal to the reconstructed mPWP SSTAs for 10 out of 17 models, which suggests that the MCM is a valuable method for exploring multi-model results. Code and data availability. The PlioMIP2 model data used in this work are available from the PlioMIP2 database upon request from Alan M.
Haywood (a. m. haywood@leeds. ac. uk). PlioMIP2 data from CESM2, EC-Earth3. 3, NorESM1-F, IPSL-CM6A, GISS2. 1G and HadGEM3 can be obtained from the Earth System Grid Federation (https://esgf-node. llnl. gov/search/cmip6/, ESGF, 2022). The boundary conditions for the PlioMIP2 experimental design can be downloaded from https://geology. er. usgs. gov/egpsc/ prism/7. 2_pliomip2_data. html  (Dowsett, 2019). Most of the analyses in this paper are processed with NCAR Command Language. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-19-2053-2023-supplement. Author contributions. XR carried out the data process, wrote the paper, and led the project. DJL and EH provided assistance during the whole work. DJL and CS contributed to writing the paper.
Special issue statement. This article is part of the special issue "PlioMIP Phase 2: experimental design, implementation and scientific results". It is not associated with a conference. All IPSL simulations have been run on Très Grand Centre de Calcul du CEA (TGCC) through GENCI (Grand Équipement National de Calcul Intensif) allocation (grant no. gen2212). We acknowledge the work of GENCI and TGCC for making our simulations available for the present work. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6, and the Palaeoclimate Modelling Intercomparison Project (PMIP) for coordinating its fourth phase, including the mPWP simulations analysed here, with CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access and the multiple funding agencies who support CMIP6 and ESGF.
Acknowledgements. Xin Ren, Daniel Lunt and Erica Hendy acknowledge the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions (grant no. 813360 4DREEF). This research used data provided by the Ocean Drilling Program (ODP). Wing-Le Chan and Ayako Abe-Ouchi acknowledge funding from JSPS (KAKENHI grant no. 17H06104 and MEXT KAKENHI grant no. 17H06323) and computational resources from the Earth Simulator at JAMSTEC, Yokohama, Japan. The development of GISS-E2. 1 was supported by the NASA Modeling, Analysis, and Prediction (MAP) program. CMIP6 simulations with GISS-E2. 1 were made possible by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. Charles J. R. Williams and Daniel J.
Lunt acknowledge the financial support of the UK Natural Environment Research Council (NERC)-funded SWEET project (grant no. NE/P01903X/1) and funding from the European Research Council (under the European Union's Seventh Framework Programme (FP/2007-868 2013; ERC grant no. 340923; TGRES). Gerrit Lohmann and Christian Stepanek acknowledge institutional funding from the Alfred Wegener Institute (AWI) via the research programme PACES-II of the Helmholtz Association. Christian Stepanek acknowledges funding from the Helmholtz Climate Initiative REKLIM. Michiel L. J. Baatsen and Anna von der Heydt acknowledge support by the programme of the Netherlands Earth System Science Centre (NESSC), which is financially supported by the Ministry of Education, Culture and Science (OCW). Xiangyu Li acknowledges funding from National Natural Science Foundation of China (grant nos.
However, the transcriptomic datasets originated so far did not focus on young secondary neurons, not allowing for their clear distinction from mature neurons  [18, [27] [28] [29] [30] [31] [32] [33] [34] [35]. In this study, we characterized the transcriptional changes that lead to mature neurons in Drosophila larval central brain (CB) and ventral nerve cord (VNC) lineages. We devised a conditional genetic strategy to label, select, and sequence single-cell transcriptomes of secondary neuronal lineages, including only 0-h-to 12. 5-h-old neurons (time relates to neuron birth, i. e. , when the neuron is generated by a ganglion mother cell (GMC), it is considered to be 0 h old; 12. 5 h later, this same neuron will be 12. 5 h old). This time window was chosen as it is prior to neuron transcriptomic convergence  [27].
Two samples were processed in parallel using the Chromium system (10x Genomics) and analyzed with standard Seurat pipeline  [41, 42]. Quality control (QC) metrics attested an overall good quality of the samples, such as low percentage of mitochondrial genes, and allowed us to set appropriate filters, resulting in a library of 12,671 single-cell transcriptomes and a total of 10,250 genes. The 2 samples were evenly scattered when visualized in low dimensional space using the uniform manifold approximation and projection (UMAP) algorithm  [43] , validating the good quality of our dataset (S2A Fig). We used Seurat's graph-based clustering approach to select a resolution that was able to set apart one of the least frequent cell types in our dataset, the type II mature intermediate neural progenitors (INPs).
GMCs express wor and ase, but not dpn nor mira (Figs 1D, 1E and S2C)  [44, 46, 50, 53]. Neurons express pros and elav  [54, 55]  but lack dpn, mira, and wor (Figs  1D, 1E  and S2C ). The absence of cells expressing repo, a known glial marker  [56, 57] , shows that this dataset does not include mature glia cells. Clusters characterized by the expression of ribosomal subunit genes were annotated as "low quality"  [58] , and clusters with no obvious expression of any neural markers were identified as "nonannotated. " Overall, this dataset represents a snapshot of the different CB and VNC secondary larval neural lineages including only their young secondary neurons.
Transcriptomic differences distinguish neurons by age Remarkably, the UMAP plot itself recapitulates the in vivo type I neural lineage progression order: NBs > GMCs > neurons (Fig 1D, dashed arrow). We have further validated this in our dataset by predicting the future state of each cell using the RNA velocity method (Velocyto;  [59] ). This method balances the abundance of unspliced and spliced mRNA variants to predict the future state of each cell. Moreover, it allows to calculate the local average velocity and represent it in a plot with an arrow. This Velocyto analysis predicted that cells progress from type I NBs, the less differentiated cells, to neurons, the more differentiated cells (S3A Fig). Moreover, Velocyto identifies age-related differences within the same cell type, as is the case for type I GMCs that seem to progress from young, closer to the NBs, to older, closer to neurons (Figs 1D, S2B and S3A).
The same age progression was identified in the neuron population. Interestingly, even though all neurons in this dataset are younger than 12. 5 h, both cluster analysis and the RNA velocity method suggest that within the neuronal population it is possible to identify different ages or degrees of maturation (Figs 1D, S2B and S3A). For instance, Hey, a known target of Notch that is transiently expressed only in Notch ON early-born neurons  [60]  is expressed in approximately half of the neurons closest to the GMCs, i. e. , the youngest neurons (Fig  2A ). Conversely, nrv3 and nSyb, known markers of differentiated neurons, are only expressed in neurons that are farther away from the GMCs in the UMAP plot (Fig  2B ). We have additionally assayed for the expression of other markers of differentiated neurons as ion channels, immunoglobulins, and cadherin super families, which are essential for axonal development and neuronal circuit formation  [61, 62]  (see Materials and methods for list details).
Using these gene lists, we determined the correspondence between each cluster and the cluster top marker genes, which are genes present in these clusters and differentially expressed versus all others. Consistently, the neuronal clusters furthest from GMCs (clusters 5 and 35), predicted to be the older neurons in this dataset, have more ion channels and adhesion molecules as top marker genes (Figs 2C and S3B-outline). We also assessed the expression pattern of genes involved in the activity and biosynthesis of different NTs: VGlut (glutamatergic), ChAT and VAChT (cholinergic), Gad1 (GABAergic), and Vmat (monoaminergic). This analysis revealed that these NT pathway genes are expressed in the neuron clusters furthest away from GMCs (Fig  2D ), with the majority of neurons in our dataset being glutamatergic or cholinergic (S3C Fig).
Notably, the young secondary neurons in our dataset mainly express a single NT pathway gene (S3C Fig) , consistent with what has been described  [29]. Thus, we propose that young neurons can be subdivided into 2 main groups depending on their degree of maturation. This distinction, correlated with neuronal age, is schematically represented in Fig 2E. We propose a "Phase 1" of maturation, which includes the very young immature neurons, which are specified and thus identified as neurons but are not yet transcribing terminal differentiation genes. Then, neurons progress into a "Phase 2" of maturation, when neurons start transcribing terminal differentiation genes. To verify if there is indeed a transcriptional difference between younger/immature neurons and older/more mature ones, we did a subset of the neuronal population to analyse with Monocle.
Monocle can identify differentially expressed genes throughout pseudotime and group them based on similar kinetic trends  [63]. Within the neuronal population of our dataset, 2 major gene groups were identified based on their pseudotemporal expression pattern (Fig  2F ). Gene cluster 2 represents a transcriptional profile composed by genes that are more expressed in neurons at the beginning of the pseudotime trajectory. A gene ontology (GO) analysis of these genes showed enrichment for terms associated with cell fate determination, regulation of neurogenesis, and neuron fate commitment (S1 Table ). On the other hand, gene cluster 1 includes genes that are more expressed in neurons at the end of the pseudotime trajectory, meaning the oldest and more mature neurons in our dataset. Consistently, their GO term analysis revealed enrichment for terms related with synaptic transmission and NT regulation (S2 Table ).
VGlut, ChAT, and Gad1 proteins were only detected, albeit at still low levels, at approximately 24 h APF (Figs 4A, 24 h APF; S4E Fig,  24  h APF). As a large fraction of optic lobe neurons are interneurons, whose cell bodies and projections remain within the optic lobe  [65] , the absence of NT proteins in the entire optic lobe region additionally shows that there is no protein in neither neuron cell bodies or in their extensions. As Gad1 is also normally expressed in cell bodies  [69] [70] [71] , this experiment also supports that NT protein absence in young stages is not related to the status of vesicle formation or maturation. To determine if NT pathway genes are also transcribed in the optic lobe during the stages when no protein is detected, we have reanalysed the single-cell transcriptome dataset generated in  [18]  where the single-cell transcriptome of the optic lobe was sequenced from 0 to 96 h APF in 12-h intervals.
For these analyses, we focused on CB young secondary glutamatergic neurons, which represent the biggest NT family in our dataset (S3C Fig). We first tested if ecdysone is necessary for the inhibition of VGlut translation in CB secondary neurons during larval stages. We have thus again generated 18-h GFP + clones in third instar larvae, now coexpressing EcRDN. However, the inhibition of ecdysone signalling in these lineages did not cause premature expression of VGlut protein expression at 105 h ALH (S5A and S5A' Fig). These results show that ecdysone is not necessary for the inhibition of VGlut translation in young secondary larval neurons. Next, we tested if ecdysone was regulating NT gene translation initiation in pupal stages. To analyse this, we expressed EcRDN in neurons using the VT201094 driver for 72 h, assessing VGlut protein expression at approximately 48 h APF, a time point when normally VGlut protein expression is already started.