dataesr/scientific-paragraphs-categorization huggingface dataset:
Where both networks are used and the last full year of data availability is different, the difference between the dataset mean and the dataset with the longer time series in this last year is used as an additive offset to the dataset with the longer time series. Some obvious inconsistencies are removed such as sudden changes in concentrations when missing data are reported as zero. Some of the more minor halogenated gases are not part of the NOAA or AGAGE operational network and are currently only reported in literature sources until 2019 or possibly 2015  (Droste et al. , 2020; Laube et al. , 2014; Schoenenberger et al. , 2015; Simmonds et al. , 2017; Vollmer et al. , 2018). Concentrations of gases where 2022 data are not yet available are extrapolated forwards to 2022 using the average growth rate over the last 5 years of available data.
14] W m -2 for 1750-2022. We determine from provisional data that aviation activity in 2022 had not yet returned to pre-COVID levels. Therefore, ERF from contrails and contrail-induced cirrus is The headline assessment of solar ERF is unchanged, at 0. 01 [-0. 06 to +0. 08] W m -2 from pre-industrial to the 2009-2019 solar cycle mean. Separate to the assessment of solar forcing over complete solar cycles, we provide a singleyear solar ERF for 2022 of 0. 06 [-0. 02 to +0. 14] W m -2. This is higher than the single-year estimate of solar ERF for 2019 (a solar minimum) of -0. 02 [-0. 08 to 0. 06] W m -2. For volcanic ERF, updating of the pre-industrial dataset for stratospheric aerosol optical depth (sAOD) increased the sAOD over 500 BCE to 1749 CE, resulting in a larger difference to post-1750 sAOD and resulting in a volcanic ERF difference of +0.
015 W m -2 compared to AR6 (see Sect. S4 in the Supplement). In addition, the earlier Holocene was more volcanically active than the period after 500 BCE, further increasing the mean sAOD baseline. Taking the longer baseline period into account in the new pre-industrial dataset, post-1750 ERF is further increased by 0. 031 W m -2. The net effect is that volcanic forcing after 1750 has increased by +0. 046 W m -2 compared to AR6 due to dataset updates and by account of the fact that the post-1750 period was less volcanically active on average than the Early Holocene, which is now used in the ERF calculation. (Gulev et al. , 2021). Updated estimates to 2022 were also given in AR6 SYR  (Lee et al. , 2023). The AR6 SYR estimates match those given here. We describe the update in detail and provide further quantification and comparisons.
There are choices around the methods used to aggregate surface temperatures into a global average, how to correct for systematic errors in measurements, methods of infilling missing data, and whether surface measurements or atmospheric temperatures just above the surface are used. These choices, and others, affect temperature change estimates and contribute to uncertainty (IPCC AR6 WGI Chap. 2, Cross Chap. Box 2. 3,  Gulev et al. , 2021). The methods chosen here closely follow AR6 WGI and are presented in the Supplement, Sect. S5. Confidence intervals are taken from AR6 as only one of the employed datasets regularly updates ensembles (see Supplement, Sect. S5). Based on the updates available as of February 2023 (which were reported in the AR6 SYR), the change in global surface temperature from 1850-1900 to 2013-2022, using the same underlying datasets and methodology as AR6, is 1.
5, are quoted in columns labelled (i) and are compared with repeat calculations in columns labelled (ii) for the same period using the updated methods and datasets to see how methodological and dataset updates alone would change previous assessments. Assessments for the updated periods are reported in columns labelled (iii). * Updated GMST observations, quoted from Sect. 5 of this update, are marked with an asterisk, with "very likely" ranges given in brackets. ** In AR6 WGI, best-estimate values were not provided for warming attributable to well-mixed greenhouse gases, other human forcings and natural forcings (though they did receive a "likely" range, as discussed in Sect. 7. 3. 1); for comparison, best estimates (marked with two asterisks) have been retrospectively calculated in an identical way to the best estimate that AR6 provided for anthropogenic warming.
Updated estimate using the SR1. 5 methodology applied to the AR6 WGI datasets While a variety of literature was drawn upon for the assessment of human-induced warming in SR1. 5 Chap. 1  (Allen et al. , 2018) , only one method, the Global Warming Index (GWI), was used to provide a quantitative assessment of the 2017, "present-day", level of human-induced warming. The latest results for this method were provided by  Haustein et al. (2017) , who gave a central estimate for human-induced warming in 2017 of 1. 01 • C with a 5 %-95 % range of (0. 87 to 1. 22 • C). SR1. 5 then accounted for methodological uncertainty by rounding this value to 0. 1 • C precision for its final assessment of 1. 0 • C and assessing the 0. 8 to 1. 2 • C range as a likely range. No assessment of the contributions from other components was provided due to limitations in the GWI approach at the time.
5 only provided an assessment for human-induced warming, updates in available attribution methods since SR1. 5 mean that it is now also possible to provide a fully consistent assessment for all warming components. As with the attribution assessment in SR1. 5, this update reports values in Table  6b  for single-year present-day attributable warming (as discussed in Sect. 7. 1. 1), with a comparison to results calculated using the SR1. 5 trend-based definition also provided below in Sect. 7. 4. and finally updated for the 2013-2022 period. Results from SR1. 5 are quoted in Table  6b  for the 2017 level of humaninduced warming, compared with a repeat calculation using the updated selection of methods and datasets (see Sect. 7. 2) and the WGI AR6 multi-method assessment approach (see Sect. 7. 3.
AR6 WGI reassessed all five terms  (Canadell et al. , 2021). The incorporation of factor (v) was further considered by  Lamboll and Rogelj (2022). Of these factors, only factor (i) (human-induced warming), where AR6 WGI used the decade-long period, 2010-2019, lends itself to a regular and systematic annual update. Historical CO 2 emissions from the middle of this period until the start of the RCB are required to have an as up-to-date RCB estimate as possible. Other factors can be updated but depend on new evidence and insights being published rather than an additional year of observational data becoming available. Factor (iv) (temperature contribution of non-CO 2 emissions) depends both on the available mitigation scenario evidence and the assessment of non-CO 2 warming. Additional scenario evidence has become available through the publication of the scenario database supporting the AR6 WGIII report  (Byers et al.
, 2022) , which is taken into account in this update. The RCB for 1. 5, 1. 7 and 2 • C warming levels is reassessed based on the most recent available data. Estimated RCBs are reported below. They are expressed both relative to 2020 to compare to AR6 and relative to the start of 2023 for estimates based on the 2013-2022 human-induced warming update. Note that between the start of 2020 and the end of 2022, about 122 GtCO 2 has been emitted (Sect. 2). Based on the variation in non-CO 2 emissions across the scenarios in AR6 WGIII scenario database, the estimated RCB values can be higher or lower by around 200 GtCO 2 depending on how deeply non-CO 2 emissions are reduced. The impact of non-CO 2 emissions on warming includes both the warming effects of other greenhouse gases such as methane and the cooling effects of aerosols such as sulfates.
Indeed, such efforts are planned within the World Climate Research Programme (WCRP) Grand Challenge on Weather and Climate Extremes, which will likely inform the next iteration of this study. As part of this first update, we provide an upgraded version of the analysis in Fig. 11. 2 from  Seneviratne et al. (2021)  (Fig. 6 ). Like the analysis of global mean temperature, the choice of datasets is based on a compromise on the length of the data record, the data availability, near-real-time updates and long-term support. As the indicator (in its current form) averages over all available land grid points, the spatial coverage should be high to obtain a meaningful average, which further limits the choice of datasets. The HadEX3 dataset  (Dunn et al. , 2020) , which is used for  Fig. 11. 2 in Seneviratne et al.
(2021) , is static and does not cover years after 2018. We therefore additionally include the Berkeley Earth Surface Temperature dataset (building off  Rohde et al. , 2013)  and the fifth-generation ECMWF atmospheric reanalysis of the global climate (ERA5;  Hersbach et al. , 2020)   to 2021, while ERA5 is updated daily with a latency of about 5 d (and the final release occurs after 2-3 months). Our proposed climate indicator of changes in temperature extremes consists of land average annual maximum temperatures (TXx) (excluding Antarctica). For HadEX3, we select the years 1961-2018, to exclude years with insufficient data coverage, and require at least 90 % temporal completeness, thus applying the same criteria as for  Fig. 11. 2 (Seneviratne et al. , 2021). Berkeley Earth provides daily maximum temperatures, and we require more than 99 % data availability for each individual year and grid, such that years with more than 4 missing days are removed.
Based on this criterion, Berkeley Earth covers at least 95 % of the global land area from 1955 onwards. ERA5, on the other hand, has full spatio-temporal coverage by design, and hence the entire currently available period of 1950 to 2022 is used. The annual maximum temperature is then computed for each grid cell, and a global area-weighted average is calculated for all grid cells with at least 90 % temporal completeness in the respective available period  (1955-2021 and 1961-2018  for Berkeley Earth and HadEX3, while ERA5 is again not affected by this criterion). We thus enforce high data availability to adequately calculate global land averaged TXx across all three datasets, but their coverage is not identical, which introduces minor deviations in the estimated global land averages. The resulting TXx time series are then computed as anomalies with respect to a baseline period of  1961-1990.
To express the TXx as anomalies with respect to 1850-1900, we add an offset to all three datasets. The offset is based on the Berkeley Earth data and is derived from the linear regression of land mean TXx to the annual mean global mean air temperature over the period 1955 to 2020. The offset is then calculated as the slope of the linear regression times the global mean temperature difference between the reference periods  1850-1900 and 1961-1990  (see Supplement, Fig. S4 ). Our climate has warmed rapidly in the last few decades, which also manifests in changes in the occurrence and intensity of climate and weather extremes. We visualise this with land average annual maximum temperatures (TXx) from three different datasets (ERA5, Berkeley Earth and HadEX3), expressed as anomalies with respect to the preindustrial baseline period of 1850-1900 (Fig.
6 ). From about 1980 onwards, all employed datasets point to a strong TXx increase, which coincides with the transition from global dimming, associated with aerosol increases, to brightening, associated with decreases  (Wild et al. , 2005). Together with strongly increasing greenhouse gas emissions (Sect. 2), this explains why human-induced climate change has emerged at an even greater pace in the last 4 decades than previously. For example, land average annual maximum temperatures have warmed by more than 0. 5 • C in the past 10 years (1. 72 • C with respect to pre-industrial conditions) compared to the first decade of the millennium (1. 22 • C; Table  8 ). Since the offset relative to our pre-industrial baseline period is calculated relative to 1961-1990, within the latter period, temperature anomalies align by construction but can diverge afterwards.
In an extensive comparison of climate extreme indices across several reanalyses and observational products,  Dunn et al. (2022)  point to an overall strong correspondence between temperature extreme indices across reanalysis and observational products, with ERA5 exhibiting especially high correlations to HadEX3 among all regularly updated datasets. This suggests that both our choice of datasets and approach to calculate anomalies does not affect our conclusion -the intensity of heatwaves across all land areas has unequivocally increased since pre-industrial times. The anomalies with respect to 1850-1900 are derived by adding an offset of 0. 9. "AR6" refers to approximately 2019, and "Now" refers to 2022. The AR6 period total emissions are our re-evaluated assessment for 2010-2019. For details and uncertainties, see Table  9.
8), bringing together and presenting up-to-date information crucial to effective climate decision-making in a findable, accessible, traceable and reproducible way. In ad-dition, the Climate Change Tracker provides standardised application programming interfaces (APIs), dashboards and charts to embed in third-party apps and websites. All data are traceable to the GitHub repository employed for this paper (Sect. 11). In time, and with feedback from the user community, the initial set of indicators displayed by the dashboard may be expanded to include others alongside their rates of change.
Code and data availability The carbon budget calculation is available from https:// github. com/Rlamboll/AR6CarbonBudgetCalc  (Lamboll and Rogelj, 2023). The code and data used to produce other indicators are available in repositories under https://github. com/ ClimateIndicator  (Smith et al. , 2023b). All data are available from https://doi. org/10. 5281/zenodo. 8000192  (Smith et al. , 2023a). Data are provided under the CC-BY 4. 0 Licence. HadEX3 [3. 0. 4] data were obtained from https://catalogue. ceda. ac. uk/uuid/115d5e4ebf7148ec941423ec86fa9f26  (Dunn et al. , 2023)   The first year of the Global Climate Change (IGCC) initiative has built on the AR6 report cycle to provide a comprehensive update of the climate change indicators required to estimate the human-induced warming and the remaining carbon budget.
This annual update traced to IPCC methods can provide a reliable, timely source of trustworthy information. As well as helping inform decisions, we can use the update to track changes in dataset homogeneity between their use in one IPCC report and the next. We can also provide information and testing to motivate updates in methods that future IPCC reports might choose to employ. Figure  9  shows decadal trends for the attributed warming and ERF. The most recent trends were unprecedented at the time of AR6 and have increased further since then (red markers), showing that human activities are consistently causing global warming recently of more than 0. 2 • C per decade. As nations and businesses forge climate policies and take meaningful action, the latest available evidence shows that global actions are not yet at the scale to manifest a substantive shift in the direction of global human influence on the Earth's en- ergy imbalance and the resulting global warming.
(2021) gas concentrations and reductions curacy level (Sect. 4) in aerosol precursors Global mean surface 2011-2020 average: 2013-2022 average: An increase of 0. 06 • C within 2 Methods match AR6 (Sect. 5). temperature change 1. 09 [0. 95 to 1. 20] • C 1. 15 [1. 00-1. 25] • C years, indicating a high decadal rate above 1850-1900 of change AR6 WGI Chap. 2: Gulev et al. (2021) Earth's energy 2006-2018 average: 2010-2022. average: Substantial increase in energy im- Ocean heat content time series ex- imbalance 0. 79 [0. 52 to 1. 06] 0. 89 [0. 63 to 1. 15] balance estimated based on tended from 2018 to 2022 us- AR6 WGI Chap. 7: W m -2 W m -2 increased rate of ocean heating ing four of the five AR6 datasets. Forster et al. (2021) Other heat inventory terms up- dated following von Schuckmann et al.
PMF coordinated the production of the manuscript with support from DR. WFL led Sect. 2 with contributions from CJS, JM, PF, GP, JG, JP and RA. CJS led Sects. 3 and 4 with contributions from BH, FD, SS, VN and XL. BT led Sect. 5 with contributions from PT, CM, CK, JK, RR, RV and LC. KvS and MDP led Sect. 6 with contributions from LC, MI, TB and RK. TW led Sect. 7 with contributions and calculations from AR, NG and MR. JR led Sect. 8 with contributions from RL and KZ. Section 9 was led by SIS and XC with calculations by MH and DS. All authors either edited or commented on the manuscript. The contact author has declared that none of the authors has any competing interests. Financial support. This research has been supported by the Horizon 2020 Framework Programme, H2020 Excellent Science (grant nos.
820829 and 821003), the H2020 European Research Council (grant no. 951542) and the Natural Environment Research Council (grant no. NE/T009381/1). Matthew D. Palmer, Colin Morice and Rachel Killick were supported by the Met Office Hadley Centre Climate Programme funded by BEIS. This paper was edited by David Carlson and reviewed by Albertus J. (Han) Dolman, Martin Heimann, Matthew Jones, Greet Janssens-Maenhout, and one anonymous referee. Competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Review statement. Supplement.
DNA Sequencing The sequencing reactions were performed with the BigDye Terminator v3. 1 Cycle Sequencing Kit according to the manufacturer instructions (Applied Biosystems), and the sequencing run was in an ABI PRISM 3500XL Genetic Analyzer (Applied Biosystems). The nucleotide sequences were aligned and edited by Sequencher 4. 1 program, and the species identity determined by the Blastn algorithm located in NCBI (https://www. ncbi. nlm. nih. gov/). The edited sequences were compared with the nucleotide sequences deposited in the GenBank database. The rDNA sequences have been registered in the GenBank database under accession numbers shown in Table  1.
Molecular identification The 16S rDNA sequencing results of the 41 isolates confirmed the morphological identification of the thirty-six bacteria, three actinomycetes, and two fungi. Among the 36 bacterial isolates, 35 belonged to the genus Bacillus, with the species B. licheniformis predominating (23 isolates), followed by B. subtillis (5 isolates), B. clausii (2 isolates), and one of each of B. cereus, B. paralicheniformis, B. pumilus, B. megaterium B. altitudinis. One bacterial isolate was Brevibacillus bortolensis. The three isolates of Actinomycetes were identified as Streptomyces thermogriseus, and the two fungi were Thermomyces lanuginosus and T. dupontii. The isolates with their respective GenBank accession numbers are shown in Table  1.
Objective: The purpose of this study was to simultaneously contrast prediagnostic clinical characteristics of individuals with a final diagnosis of dementia with Lewy Bodies (DLB), Parkinson's disease (PD), and Alzheimer's disease (AD) compared with controls without neurodegenerative disorders. Methods: Using the longitudinal THIN database in the United Kingdom, we tested the association of each neurodegenerative disorder with a selected list of symptoms and broad families of treatments, and compared the associations between disorders to detect disease-specific effects. We replicated the main findings in the UK Biobank. Results: We used data of 28,222 patients with PD, 20,214 with AD, 4,682 with DLB, and 20,214 healthy controls. All neurodegenerative disorders were significantly associated with the presence of multiple clinical characteristics before their diagnosis, including sleep disorders, falls, psychiatric symptoms, and autonomic dysfunctions.
3 All these neurodegenerative diseases are known to have a very long preclinical stage. For AD, accumulation of amyloid can precede a clinical diagnosis of dementia by up to 20 years. 4 Synucleinopathies, including PD and DLB, also have a long prodromal stage, as demonstrated by isolated rapid eye movement (REM) sleep behavioral disorders, which can precede symptomatic PD or DLB by 10 years or more. 5 he first dysautonomic signs have been estimated to start as early as 15 years before motor symptoms in PD. 6, 7  Neurological disorders are often underdiagnosed in the primary care setting. 8 Analyses on general practitioners' (GPs) databases have already led to the identification of disease-specific prodromal symptoms of PD and AD as well as risk factors in the primary care data. 9, 10 However, it is unknown how the prodromal features and risk factors of these disorders differ.
Analyses on the prodrome of DLB have so far been limited to core symptoms of DLB. 11, 12 Overall, cross-disorder analyses focusing on shared and specific associations with neurodegenerative diseases are rare. 13 We undertook cross-disorder analyses in order to identify prediagnostic associations and risk factors of the most common neurodegenerative conditions (AD, PD, and DLB), using data collected by GPs over a long-time window. We sought to identify disorders and drug prescriptions associated with AD, PD, and DLB, as well as those that are specific to each condition. Predictors of dementia in PD observable in primary care are also missing. 14 In a secondary analysis, we aimed at understanding which drug prescriptions are associated with the presence of dementia in the 5 years following a first PD diagnosis.
Methods Study Design and Participants THIN Database. We used The Health Improvement Network (THIN) database,  15  a large European standardized database of anonymized electronic medical records collected at the physicians' level by the company Cegedim and coded using the International Classification of Diseases, 10th Revision (ICD-10) codes. Data were collected in 400 general practices, representing around 6% of the UK population. 9 Several reports have shown that electronically coded diagnoses in this database are representative of the UK general practice population in terms of demographics and type of consultation. 15, 16 For each patient and visit, we obtained the diagnosis and prescription established during the visit, as well as all ongoing prescriptions and associated diagnoses. We defined our neurological cases based on ICD-10 codes given by the GPs of the THIN database recorded between January 1996 and April 2020.
For the neurodegenerative disorders of interest, AD cases corresponded to codes F00 and G30, PD cases to code G20, and DLB cases to code G31. 8 (and READ code Eu025, to exclude frontotemporal dementia). We extracted all patients with DLB in the database, and a random subsample of patients with AD or with PD, with at least 2 years of follow-up. We defined age at onset as the age at the first record coded with AD, PD, or DLB diagnosis. Controls were screened for any history of AD (F00 et G30), PD (G20), frontotemporal dementia (G31. 0 and G31. 8), DLB (G31. 8), Huntington's disease (G10), or multiple sclerosis (G35). We had access to 20,214 controls matched with AD cases on sex and age +/À 1 year at last record in the database from a precedent study which was specific to AD. 10  UK Biobank Replication Sample.