dataesr/scientific-paragraphs-categorization huggingface dataset:
This procedure is generally avoided at small neutron energies where the resonant structures of the cross section are visible (see Fig. 3 ) but can be safely applied at higher energies. Several corrections are applied using Monte Carlo simulations, including the multiple-scattering correction and the correction for the detection efficiency from the extended sample. The final step in data analysis employs an external nuclear structure database -namely the ENSDF (Evaluated Nuclear structure Data File)  [15]  -to generate transition probabilities from the γ-production cross section (i. e. correcting for the internal conversion when necessary) and further, using the branching ratios, to generate the total inelastic and the level cross sections. These are accurate only over a limited energy range depending on which γ rays from the reaction were observed.
0028), ALBI grade 2 (p = 0. 0043) and 3 (p = 0. 0014). Adverse events were recorded in 36. 7% of patients, with 9. 7% of patients experiencing grade 3 or higher adverse events. Conclusions: This large prospective observational dataset shows that TARE is an effective and safe treatment in patients with HCC. Using partition model dosimetry was associated with a significant improvement in survival outcomes. Impact and implications: Transarterial radioembolization (TARE) is a form of localised radiation therapy and is a potential treatment option for primary liver cancer. We observed how TARE was used in real-life clinical practice in various European countries and if any factors predict how well the treatment performs. We found that when a more complex but personalised method to calculate the applied radiation activity was used, the patient responded better than when a more generic method was used.
Leonards, NSW, Australia). The study (NCT02305459) was open to all indications and recruited one of the largest cohorts on TARE in liver malignancies to date. 25 The objective of the current subgroup analysis was to investigate factors influencing survival in patients with HCC treated with TARE, including the effect that methods to calculate the prescribed dose have on effectiveness outcomes. The primary endpoint was OS, while secondary endpoints were PFS, PFS in the liver only (hepatic PFS [hPFS]), safety, and identification of potential prognostic survival factors, including an evaluation of the impact of methods to calculate the prescribed activity on survival outcomes.
Study design We analysed 422 patients with HCC collected in the CIRSE Registry for SIR-Spheres Therapy (CIRT) study. CIRT is a prospective, single device, multi-centre observational study of patients with primary and metastatic hepatic malignancies treated with TARE using Y90 resin microspheres as the standard of care. The CIRT methodology was published by Helmberger et al. 26  Sites were invited to participate if they had at least 40 TARE cases and 10 cases in 12 months prior to invitation. In total, 27 participating sites in eight countries were identified and enrolled from April 2014 until April 2017, of which 25 sites included patients with HCC. 25  Data was collected using a customised electronic data capturing system and electronic case report form that was developed by ConexSys Inc (Lincoln, RI, United States) and hosted on a local secure server in Vienna, Austria maintained by ITEA (Vienna, Austria).
Assessments At the time of first treatment, baseline data, demographics and treatment-related data were collected. Information concerning post-TARE treatments, safety data and time-to-event data were gathered at every follow-up visit. Time-to-event was defined from the date of the first TARE treatment until the date of the event. Liver function was described using the albumin-bilirubin (ALBI) formula developed by Johnson et al. : ALBI score = (log 10 bilirubin [lmol/L] × 0. 66) + (albumin [g/L] × -0. 0852). ALBI score < --2. 60 is grade 1, >-2. 60 to < --1. 39 is grade 2, and >-1. 39 is grade 3. 27  BCLC classifications were determined at the sites, but all classifications were evaluated according to the uniform BCLC staging standards set out by Reig et al. in the recent (2022) update. 28 Where necessary, patients were re-classified.
The development over time of the ALBI, bilirubin, albumin and international normalized ratio (INR) values were explored using a linear mixed model. A multivariable analysis for OS, PFS and hPFS was performed using a Cox proportional-hazards model whereby the selection of variables was determined following a univariable analysis and a stepwise variable selection procedure, with a significance level of 0. 2 used to determine whether to enter a predictor into the stepwise model. The model with the lowest Akaike information criterion value was considered the final model. All available data were used, and no imputations of missing data were made. Additional analyses were performed to evaluate the impact on OS, PFS and hPFS of the two main methods to calculate the prescribed Y90 activity: partition model and (modified) body surface area ([m]BSA) methodology.
85 Gy [IQR 58-164 Gy]; p = 0. 02). 50 Unfortunately, the present study did not collect any data on the tumour target dose or radiationabsorbed dose and thus cannot provide any suggestions on optimal dosage. Analysis of differences in prescribed activity between BSA/mBSA and the partition model found no significant differences, when adjusted for tumour volume, percentage of tumour activity and number of tumour nodules. Nevertheless, the multivariable analysis and the propensity score matching suggest that patients whose dosages were calculated with the partition model generally performed better than patients whose dosage was calculated with BSA or mBSA, which is in line with the aforementioned studies. Our study adds to the findings from DOSISPHERE-01 and SARAH, by showing that personalised dosimetry methods also improve effectiveness outcomes in a reallife clinical context with Y90 resin microspheres, compared to activity calculation methods based on BSA, irrespective of a site's experience.
This means that retrospectively important data points such as precise administered activity and tumour-absorbed dose were not included in the evaluation at the time of study design. Furthermore, as an observational study, the design was non-prescriptive for tumour response assessment, which was performed with various criteria (e. g. , RECIST, modified RECIST or PET Response Criteria in Solid Tumours) according to local practice and expertise of centres. This prevented us from including tumour response in the analysis. We attempted to collect quality-of-life data from patients on a voluntary basis at the time of treatment and at every follow-up visit until study exit. The relevance of the collected dataset is currently being evaluated. The relatively high number of patients lost to follow-up can introduce bias regarding the interpretation of OS, and imprecise follow-up imaging intervals should be considered when interpreting PFS and hPFS.
Furthermore, our data revealed that optimising the application of the therapy by using the partition model instead of BSA models, can significantly improve survival outcomes. It is thus recommended that activity calculations with the partition model are considered when designing future randomised controlled trials on TARE. chairperson of the CIRT Steering Committee between 2015 and 2018 and is now enjoying his well-deserved retirement. CIRSE, the Cardiovascular and Interventional Radiological Society of Europe, is responsible for the independent execution of the CIRT study and has sole ownership of the data. The electronic data capturing system was developed and supported by ConexSys Inc (Lincoln, RI, United States). ITEA GmbH (Vienna, Austria) developed and maintained the necessary infrastructure and a customised data management system was designed by Joaquin Padilla Montani, Vienna, Austria.
Data Collection and Sampling The survey was conducted online from 25 February to 4 March 2021 with a panel of 600 French consumers. Data collection was executed by Toluna, a provider of survey panelists, which allowed for easier and quicker access to a large, heterogeneous sample of consumers. Participation in the survey was voluntary, and all data were anonymized. To ensure a representative sample of the French population, proportional quota sampling was used. Quotas were set for age, gender, region, and socio-professional category (based on the available census data) so as to recruit respondents in the same proportions as in the French population. Being a non-probability method, quota sampling can present potential biases which limit the generalizability of results. Nevertheless, as argued by Marsh and Scarbrough  [82] , no significant differences that would indicate considerable data biases were found when comparing quota sampling to random sampling.
Data Analysis In order to assess the measurement model and to test our hypotheses, Partial Least Squares Structural Equation Modeling (PLS-SEM) was performed using SmartPLS 4 software. PLS-SEM was deemed suitable given the exploratory nature of our research model, which investigates conceptual relationships that have not yet been explored in the literature on food labels. Moreover, PLS-SEM is recommended for the estimation of models which include both reflective and formative constructs  [83] [84] [85]. In reflective models, the observed variables (measurement indicators) are assumed to be caused by the latent variable (cf. Churchill, 1979). Indicators are, therefore, the manifestations that reflect the underlying construct. Thus, any variation in the value of the latent variable leads to a variation in the values of all its reflective indicators  [86].
Annual state-of-the-climate reports are released by the WMO which use much of the same data analysed here for surface temperature and energy budget trends. The Bulletin of American Meteorological Society (BAMS) releases annual state-of-the-climate reports covering many essential variables including temperature and greenhouse gas concentrations. However, these reports focus on statistics from the previous year and make slightly different choices over datasets and analysis compared to the IPCC (see Sect. 5). The Global Carbon Project publishes updated carbon dioxide datasets which are used directly in this report. There is no similarly structured activity that provides all the necessary datasets to update the assessment of human influence on global surface temperature annually. The update is based on methodologies for key climate indicators assessed by the IPCC Sixth Assessment Report (AR6) of the physical science basis of climate change (Working Group One (WGI) report; IPCC, 2021a) as well as Chap.
It also updated its approach for estimating human-induced warming and global warming level. In AR6 WGI and here, reaching a level of global warming is defined as the global surface temperature change, averaged over a 20-year period, exceeding a particular level of global warming, for example, 1. 5 • C global warming. Given the current rates of change and the likelihood of reaching 1. 5 • C of global warming in the first half of the 2030s  (Lee et al. , 2021 (Lee et al. , , 2023;; Riahi et al. , 2022) , it is important to have robust, trusted and also timely climate indicators in the public domain to form an evidence base for effective science-based decision-making. When making their assessments, authors of IPCC reports assess published literature but also apply established published analysis methods to assessed datasets, such as the dataset produced by the latest climate model intercomparison projects  (Lee et al.
, 2021). The authors combine and analyse both model and observational data as part of their expert assessment, making assessments of the trustworthiness and error characteristics of different datasets. It is this synthetic analysis by IPCC authors that derives the estimates of key climate indicators. Wherever possible, these same assessed methodological approaches are implemented here to provide the updates with variations clearly flagged and documented. The same approach, using the same datasets (updated by 2 years) and methods as employed in WGI, was used in the AR6 Synthesis Report (2023) (AR6 SYR;  Lee et al. , 2023)  to provide an updated assessment of the latest atmospheric well-mixed greenhouse gas concentrations (up to 2021) and decadal average change in global surface temperature (+1.
15 • C [1. 00-1. 25 • C] in 2013-2022 for global surface temperature). However, the assessment of humaninduced warming was not updated (and therefore only covers warming up to the decade 2010-2019), nor was the remaining carbon budget updated, so the related information in the AR6 SYR report remained based on data up to the end of 2019. The indicators in this first annual update give important insights into the magnitude and the pace of global warming. This paper provides the basis for a dashboard of climate indicators grounded in IPCC methodologies and directly trace-able to reports published as part of the AR6 cycle. We employ datasets that can be updated on a regular basis between the publication of IPCC reports. Note that there are other similar initiatives underway to update other AR6 cycle products; for example, the evolution of the WGI Interactive Atlas  (Gutiérrez et al.
, 2021)  is being developed under the Copernicus Climate Change Service (C3S) and has potential connections and synergies with this initiative that will be explored in the future. Our longer-term ambition is to rigorously track both climate system change and methodological improvements between IPCC report cycles, thereby building consistency and awareness. An example of why tracking methodological change is important was the updated estimate for historic warming (the increase in global surface temperature from  1850-1900 to 1986-2005). This was 0. 08 [-0. 01 to 0. 12] • C higher in the AR6 than in the fifth assessment report (AR5) and SR1. 5. Datasets and methods of evaluating global temperature changes altered between the AR5 and AR6, leading to a small shift in the historical temperature.
Section 8 updates the remaining carbon budget to policy-relevant temperature thresholds. Section 9 gives an example of globalscale indicators associated with climate extremes of maximum land surface temperatures. An important purpose of the exercise is to make these indicators widely available and understood. Plans for a web dashboard are discussed in Sect. 10 and code and data availability in Sect. 11, and conclusions are presented in Sect. 12. Data are available at https://doi. org/10. 5281/zenodo. 8000192  (Smith et al. , 2023a).
There are also varying conventions used to quantify CO 2 -LULUCF fluxes. These include the use of bookkeeping models, dynamic global vegetation models (DGVMs) and the national inventory approach  (Pongratz et al. , 2021). Each differs in terms of their applied system boundaries and definitions and is not directly comparable. However, efforts to "translate" between bookkeeping estimates and national inventories using DGVMs have demonstrated a degree of consistency between the varying approaches  (Friedlingstein et al. , 2022a; Grassi et al. , 2023). Each category of GHG emissions included here is covered by varying primary sources and datasets. Although many datasets cover individual categories, few extend across multiple categories, and only a minority have frequent and timely update schedules.
Notable datasets include the Global Carbon Budget (GCB;  Friedlingstein et al. , 2022b) , which covers CO 2 -FFI and CO 2 -LULUCF; the Emissions Database for Global Atmospheric Research (EDGAR;  Crippa et al. , 2022)  and the Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths (PRIMAP-hist;  Gütschow et al. , 2016; Gütschow and Pflüger 2023)  In AR6 WGIII, total net GHG emissions were calculated as the sum of CO 2 -FFI, CH 4 , N 2 O and UNFCCC F-gases from EDGAR and net CO 2 -LULUCF emissions from the GCB. Net CO 2 -LULUCF emissions followed the GCB convention and were derived from the average of three bookkeeping models  (Hansis et al. , 2015; Houghton and Nassikas, 2017; Gasser et al. , 2020). Version 6 of EDGAR was used (with a fast-track methodology applied for the final year of data -2019), alongside the 2020 version of the GCB  (Friedlingstein et al.
First, national greenhouse gas emissions inventories tend to use improved, higher-tier methods for estimating emis-sions fluxes than global inventories such as EDGAR or CEDS  (Dhakal et al. , 2022; Minx et al. , 2021). As GCB and PRIMAP-hist integrate the most recent national inventory submissions to the UNFCCC, selecting these databases makes best use of country-level improvements in datagathering infrastructures. Second, comprehensive reporting of F-gas emissions has remained challenging in national inventories and may exclude some military applications (see  Minx et al. , 2021; Dhakal et al. , 2022). However, F-gases are entirely anthropogenic substances, and their concentrations can be measured effectively and reliably in the atmosphere. We therefore follow the AR6 WGI approach in making use of direct atmospheric observations.
Third, the choice of GCB data for CO 2 -FFI means we can integrate its projection of that year's CO 2 emissions at the time of publication (i. e. for 2022). No other dataset except GCB provides projections of CO 2 emissions on this time frame. At this point in the publication cycle (mid-year), the other chosen sources provide data points with a 2-year time lag (i. e. for 2021). While these data choices inform our overall assessment of GHG emissions, we provide a comparison across datasets for each emissions category, as well as between our estimates and an estimate derived from AR6 WGIII-like databases (i. e. EDGAR for CO 2 -FFI and non-CO 2 GHG emissions, GCB for CO 2 -LULUCF).
It is important to note that this change is not a reflection of changed and improved methodology per se but an update of the resulting estimation due to updates in the available input data. Second, there are relatively small changes resulting from improvements in datasets since AR6, with the direction of changes depending on the considered gases. CH 4 accounts for the largest of these at -1. 8 GtCO 2 e in 2019, which is related to the switch from EDGAR in AR6 to PRIMAP-hist in this study. EDGAR estimates considerably higher CH 4 emissions -from fugitive fossil sources, as well as the livestock, rice cultivation and waste sectors -compared to country-reported data using higher tier methods, as compiled in PRIMAP-hist. Generally, uncertainty in these sectors is relatively high as calculations are based on activity data and assumed emissions factors which are hard to determine and vary greatly over countries.
Non-methane short-lived climate forcers In addition to GHG emissions, we provide an update of anthropogenic emissions of non-methane short-lived climate forcers (SLCFs) (SO 2 , black carbon (BC), organic carbon (OC), NO x , volatile organic compounds (VOCs), CO and NH 3 ). HFCs are considered in Sect. 2. 2. Updating emissions of many short-lived climate forcing agents to 2022 based on established datasets is not possible as compiling global data can take several years. Yet, as SLCF emissions are needed in this paper to update effective radiative forcing (ERF) estimates through 2022, updated emission datasets, where they are available, are combined with projected data to make SLCF emission time series complete. As in  Dhakal et al. (2022) , sectoral emissions of SLCFs are derived from two sources.
For fossil fuel, industrial, waste and agricultural sectors, we use the CEDS dataset that provided SLCF emissions for the Coupled Model Intercomparison Project Phase 6 (CMIP6)  (Hoesly et al. , 2018). CEDS provides global emissions totals from 1750 to 2019 in its most recent version  (O'Rourke et al. , 2021). No CEDS emissions data are available yet beyond 2019. As a first estimate, the SLCF emissions time series are extrapolated to 2022 using the "two-year blip" scenario  (Forster et al. , 2020)  of global emissions suppressed by the economic slowdown due to COVID-19. These projections are proxy estimates from Google and Apple mobility data over 2020 and assume a slow return to pre-pandemic emissions activity levels by 2022. Other near-real-time emissions estimates covering the COVID-19 pandemic era tend to show less of an emissions reduction than the two-year blip scenario  (Guevara et al.
, 2023). It should be stressed that accurate quantification of SLCF emissions during this period is not possible. We do not explicitly account for the introduction of strict fuel sulfur controls brought in by the International Maritime Organization on 1 January 2020, which was expected to reduce SO 2 emissions from the global shipping sector by 8. 5 Tg against a pre-COVID baseline (around 10 % of 2019 total SO 2 emissions). SO 2 reductions from shipping are partly accounted for in the proxy activity dataset, and including a specific shipping adjustment may double-count emissions reductions. For biomass-burning SLCF emissions, we follow AR6 WGIII  (Dhakal et al. , 2022)  and use the Global Fire Emissions Dataset (GFED;  Randerson et al. , 2017)  for 1997 to 2022, with the dataset extended back to 1750 for CMIP6  (van Marle et al.
Future updates of CEDS are expected to include uncertainties  (Hoesly et al. , 2018). Even though trends over recent years are uncertain, the general decline in some SLCF emissions derived is supported by aerosol optical depth measurements (e. g. Quaas et al. , 2022). 3 Well-mixed greenhouse gas concentrations AR6 WGI assessed well-mixed GHG concentrations in Chap. 2  (Gulev et al. , 2021)  and additionally provided a dataset of concentrations of 52 well-mixed GHGs from 1750 to 2019 in its Annex III  (IPCC, 2021c). Footnotes in AR6 SYR updated CO 2 , CH 4 and N 2 O concentrations to 2021  (Lee et al. , 2023). In this update, we extended the record to 2022 for all 52 gases. Ozone is an important greenhouse gas with strong regional variation both in the stratosphere and troposphere  (Szopa et al.
, 2021). Its ERF arising from its regional distribution is assessed in Sect. 4 but following AR6 convention is not included with the GHGs discussed here. Other non-methane SLCFs are heterogeneously distributed in the atmosphere and are also not typically reported in terms of a globally averaged concentration. Globally averaged concentrations for these are normally model-derived, supplemented by local monitoring networks and satellite data  (Szopa et al. , 2021). As in AR6, CO 2 concentrations are taken from the NOAA Global Monitoring Laboratory (GML) and updated through 2022  (Lan et al. , 2023a). Here, CO 2 is reported on the updated WMO-CO2-X2019 scale, whereas in AR6, values were reported on the WMO-CO2-X2007 scale. This improved calibration increases CO 2 concentrations by around 0. 2 ppm  (Hall et al.
, 2021). In AR6, CH 4 and N 2 O were reported as the average from NOAA and the Advanced Global Atmospheric Gases Experiment (AGAGE) global networks. For 2022, as updated AGAGE data are not currently available, we used only NOAA data  (Lan et al. , 2023b)  and multiplied N 2 O by 1. 0007 to be consistent with a NOAA-AGAGE average. NOAA CH 4 in 2022 was used without adjustment since the NOAA and AGAGE global CH 4 means are consistent within 2 ppb. Mixing ratio uncertainties for 2022 are assumed to be similar to 2019, and we adopt the same uncertainties as assessed in AR6 WGI. Many halogenated greenhouse gases are reported on a global mean basis from NOAA and/or AGAGE until 2020 or 2021 (SF 6 is available in the NOAA dataset up to 2022). Where both NOAA and AGAGE data are used for the same gas, we take a mean of the two datasets.