dataesr/scientific-paragraphs-categorization huggingface dataset:
Consumer complaints can be filed online. American Medical Association members are bound by a code of medical ethics to act professionally  [33, 34] , but the American Medical Association does not have the legal authority or possess the proper resources to investigate individual cases and complaints. Therefore, practitioners are principally subject to oversight at the level of their state medical licensing boards. b) The EMA has issued public warnings about using unproven cell therapies that may be unsafe or ineffective  [27]. Physicians, institutions, companies or individuals who have questions about the regulatory status and/or proven safety and efficacy characteristics of a product are encouraged to contact the EMA or their National Competent Authority. EMA-reported Safety Adverse Event information is disclosed through language/country-specific databases.
The centralized European database, EudraVigilance, is obligatory for marketing authorization holders and sponsors of clinical trials and since June 2022 it is mandatory to report side effects. c) Health Canada has issued public notice of the risks associated with using unproven CGTs  [35]. A position paper by Health Canada  [36]  on autologous cell therapy products notes the legal distinctions between the Canadian regulatory framework and the EMA regulatory framework. However, on a practical level, there is shared emphasis in warning against the risks posed by unproven CGT use. Health Canada provides a consumer reporting portal for reporting product complaints, as well as the ability to specifically report Adverse Events by a variety of means. In addition, consumer complaints can be filed to Canada's Competition Bureau.
Error bars indicate the uncertainties from the references. Details in estimating the stress drop are given in the supplemental materials. Despite their scatter, the field data match well with the speed regions delineated by the theoretical model, that is in turn congruent with the laboratory data. earthquakes (Methods). A compilation of the average rupture velocities and estimated stress drop of strike-slip earthquakes is available from the SRCMOD database  60  (Supplementary Data 2), together with fast ruptures on fault segments of some well-instrumented earthquakes (Supplementary Data 3). As seen in Fig. 4b , most earthquakes assume average rupture speeds slower than the Rayleigh wave speed. However, ruptures on certain fault segments can propagate at speeds faster than the S wave speed of the crust (~3.
Estimates of rupture speed and stress drop for natural earthquakes The data for natural earthquakes are mainly adopted from the SRCMOD database of finite-fault rupture models  60. SRCMOD is an open-source database including more than 300 finite fault rupture models. For the models included in SRCMOD, we only consider the strike-slip events that can be approximately regarded as mode II ruptures (Supplementary Data 2). Except for the stress drops (Δτ), key source parameters, including the geometry of the fault, the seismic moment (M 0 ), and the rupture velocity (V r ) are provided in the finite fault rupture models. The analytical relation between the fault rupture length (L r ), fault rupture width (W r ) and the seismic moment (M 0 )  77  allows one to estimate the stress drop (Δτ) and to explore the correlation between V r and Δτ.
Acknowledgements This study was supported by  National Natural Science Foundation of China (NSFC)  grants no. 42174061and 42141010 (K. X. ), and Project funded by  China Postdoctoral Science Foundation  grant no. 2022TQ0319  (P. D. ), and the  Natural Sciences and Engineering Research Council of Canada (NSERC)  through the  Discovery grant  no. 72031326  (K. X. ). We thank  Prof. Hiroo Kanamori  from  Caltech ,  Prof. Shamita Das  from  Oxford University ,  Prof. Emily Brodsky  from  UC Santa Cruz , and  Dr. Yingjun Deng  from  Tianjin University  for discussions and comments. We thank  Shiqing Xu ,  Allan Rubin , the other anonymous reviewer, and the Editor for their constructive comments.
Data availability Raw data for Figs. 1, 3 and 4 are provided in the Supplementary Data 1-3. The source data for all other figures and the original laboratory data have been deposited in Figshare under accession code (https://doi. org/10. 6084/m9. figshare. 18865673. v2). The natural earthquakes data is available at SRCMOD database (http://equake-rc. info/srcmod/).
Supervised Learning Approaches: This subsection explores the use of supervised learning techniques for detecting generated texts. Supervised learning involves training a model on labeled datasets, where the model learns to classify texts as either humanwritten or machine-generated. It discusses the use of various feature representations, such as n-grams, lexical features, or syntactic features, along with classifiers like support vector machines (SVM) or random forests. The energy implications of training and utilizing supervised learning models for text detection can be discussed, considering the computational resources needed for training and inference.
Collaboration and Interdisciplinary Research This subsection emphasizes the significance of collaboration and interdisciplinary research in advancing energyefficient text detection. It highlights the need for researchers, policymakers, and industry stakeholders to work together to develop sustainable AI technologies. The section discusses the benefits of sharing knowledge, best practices, and datasets across disciplines to drive innovation and foster responsible AI development. By examining the implications for trustworthy AI applications, emphasizing environmental considerations, suggesting future research directions, addressing ethical considerations, and highlighting the importance of collaboration, this section concludes the article. It emphasizes the potential positive impact of energy-efficient text detection algorithms and sets the stage for further advancements in sustainable and responsible AI.
, 2021; Geer & Baordo, 2014; Kulie et al. , 2010; Ringerud et al. , 2019). Currently, in most NWP centers exploiting cloudy and rainy radiances, the high microwave frequencies are simulated with a single "optimal" particle shape for snowfall. Several studies focused on searching for optimal particle radiative properties to fit the microwave observation distributions (e. g. ,  Geer & Baordo, 2014; Guerbette et al. , 2016; Haddad et al. , 2015; Mangla et al. , 2021). Optimizations including different solid hydrometeor properties have been investigated: in Geer (2021) a parameter estimation method is used in order to select an optimal shape for several species at the same time. Knowing the diversity of the possible solid hydrometeor characteristics  (Hallett & Mason, 1958; Magono & Lee, 1966; Nakaya, 1954) , this represents a huge simplification of the atmosphere despite the availability of databases providing scattering properties for many solid hydrometeors  (Ding et al.
, 2017; Liu, 2008) , ARTS  (Brath et al. , 2020; Eriksson et al. , 2018; Kneifel et al. , 2018). Several studies have investigated the use of ensembles of radiative properties, for a different part of the spectrum. Baran and Labonnote (2007)  developed a model for cirrus clouds using a set of crystals to simulate brightness temperatures in the infrared range. Kulie et al. (2010)  averaged radiative transfer simulations with the properties available in the databases of  Liu (2008)  and  Hong (2007)  to derive microwave brightness temperatures. Using this latter method within a precipitation retrieval algorithm  (Kummerow et al. , 2001; Randel et al. , 2020; Ringerud et al. , 2019)  obtained positive improvements with the database of  Liu (2008). At Météo-France, the "1D-Bay+3D/4D-Var" scheme is operationally used to assimilate ground-based radar reflectivities since 2010  (Caumont et al.
Before performing an evaluation through data assimilation experiments, the particle choices within the revised 1D-Bayesian inversion scheme need to be documented. Therefore, our main objective is not yet to quantify the benefits of such a novel approach but to examine its potential in order to exploit it to the full afterward. The outcome of this study will be to provide guidance on how to exploit scattering property databases more efficiently in a data assimilation framework. Section 2 describes the NWP system and the observations used in this paper to conduct experiments with the 1D Bayesian inversion scheme. The experiments and the products selected to evaluate the results are introduced in Section 3. In Section 4 results from the experiments are presented and discussed. Finally, conclusions drawn from this study are summarized in Section 5.
| GPM MICROWAVE IMAGER DATA AND NWP SYSTEM The experiments with different mixtures of SSPs have been conducted over a 2-month period using Bts from level 1B products from the GPM Microwave Imager (GMI L1B ATBD, 2016) within a ±90 min time window around the time of validity of 3-h forecasts of the Application of Research to Operations at Mesoscale (AROME)-Antilles NWP model  (Faure et al. , 2020). This convective scale model, with a resolution of 2. 5 km, is used operationally at Météo-France over several geographical domains in the Tropics including the Caribbean geographical area  (10. 4 N to 22. 45 N; 67. 8 W to 52. 2 W). The microphysical scheme of this model generates prognostic cloud liquid water, cloud ice water, rain, snow, and graupel mixing ratios. We selected GMI Bts over a period of high convective activity from September 1, 2017 to October 31, 2017.
| Experiment definition A 1D Bayesian inversion has been developed at Météo-France to retrieve atmospheric profiles from cloudy microwave Bts  (Barreyat et al. , 2021; Duruisseau et al. , 2019; Guerbette et al. , 2016). The retrieved profiles are derived from a weighted average of profiles x i in the neighborhood of a given observation y. A 250 Â 250 km domain is chosen around each observation with profiles taken from an AROME short-range forecast (First-Guess  [FG] ). The largest weights are given to the simulated Bts closest to the observed ones. The retrieved profile is given by: x ret ¼ P i¼1 n w i x i P i¼1 n w i With w i ¼ e P j¼1 d À 1 2 y o,j ÀH j x i ð ÞÀb j ½ t R À1 y o,j ÀH j x i ð ÞÀb j ½ À Á , where i corresponds to a given profile in the inversion database (excluding the first guess), j is a given channel, d is the number of channels selected for the inversion, H(.
The first experiment is performed with 3 SSPs from the  Liu (2008)  database. These SSPs are Sector snowflake, Rosette 6-bullet, and Rosette 3-bullet. This choice was motivated by their Bt distributions simulated with the AROME-Antilles model which are close to the observed ones, as shown in Figure  1a-c. In order to highlight the variability of the simulated Bts depending on particles for the two experiments, the distributions of their differences in simulations are plotted at the frequency 183. 31 ± 7 GHz (Figure  1d, e ). This frequency is interesting due to its sensitivity to scattering signals produced by ice particles. However, despite similarities in Bt distributions for the three SSPs (Figure  1a-c ), Bts simulated with the Sector snowflake can be up to 50 K different from the two other particles (e.
Liu (2008) a single SSP is defined when one of the SSPs has a normalized weight larger than 0. 95. hasbeen assessed using a number of SSPs from theLiu (2008)database with two experiments: one with 3 SSPs leading to simulated Bt distributions with the AROME model close to the GMI one; and another one with the 3 previous SSPs plus one with a distribution characterized by a larger tail of low Bts compared to observations.
ACKNOWLEDGEMENTS This research is funded by  Météo-France  and  Région Occitanie  ( PhD grant for Marylis Barreyat ). The authors acknowledge the  Centre National d' Etudes Spatiales (CNES)  for the financial support of this scientific research activity part of (the  Infrarouge, Micro-Ondes et Transfert radiatif ensembliste pour la prévision des Extrêmes de Précipitations) (IMOTEP)  project. The authors also want to thank  Christophe Accadia  and  Yves Bouteloup  for their useful advice.
FUNDING INFORMATION This research is funded by  Météo-France  and  Région Occitanie  ( PhD grant for Marylis Barreyat ). The authors acknowledge the  Centre National d' Etudes Spatiales (CNES)  for the financial support of this scientific research activity part of the  Infrarouge, Micro-Ondes et Transfert radiatif ensembliste pour la prévision des Extrêmes de Précipitations (IMOTEP)  project. ORCID Marylis Barreyat https://orcid. org/ 0000-0002-1502-3120.
Liu (2008) a single SSP is defined when one of the SSPs has a normalized weight larger than 0. 95. hasbeen assessed using a number of SSPs from theLiu (2008)database with two experiments: one with 3 SSPs leading to simulated Bt distributions with the AROME model close to the GMI one; and another one with the 3 previous SSPs plus one with a distribution characterized by a larger tail of low Bts compared to observations. T A B L E 1 Percentage of use Surface precipitation between mixtures and single SSP within the 1D-Bayesian inversion over a Bin (mm/h) [0. 1;2] [2;10] [10;25] [25;+] 2-month period as a function of 3 Mixture (%) 55 (55) 38 (37) 24 (25) 24 (22) predictors respectively surface Single SSP (%) 45 (45) 62 (63) 76 (75) 76 (78) precipitation, P SI scattering index, and ice water path for the 3SSP (4SSP) experiments P SI Bin (K) [5;10] [10;15] [15;20] [20;25] [25;+] Mixture (%) 78 (76) 68 (67) 51 (51) 37 (38) 26 (27) Single SSP (%) 22 (24) 32 (33) 49 (49) 63 (62) 74 (73) Ice water path Bin (kg/m 2 ) [0.
All RRID suggestions should be verified; only the author can know whether the suggestions are correct. MDAR framework establishes a minimum set of requirements in transparent reporting applicable to studies in the life sciences (see Statement of Task: doi:10. 31222/osf. oio/9sm4x. ). The MDAR checklist is a tool for authors, editors and others seeking to adopt the MDAR framework for transparent reporting in manuscripts and other outputs. Please refer to the MDAR Elaboration Document for additional context for the MDAR framework. For commercial reagents, provide supplier name, catalogue number and RRID, if available No antibodes detected. State if sample or data point from the analysis is excluded, and whether the criteria for exclusion were determined and specified in advance. State whether newly created datasets are available, including protocols for access or restriction on access.
If publicly available data are reused, provide accession number in repository or DOI or URL, where possible. Design Analysis Materials Design Analysis Reporting (MDAR) Checklist for Authors Study protocol Attrition Yes (indicate where provided: page no/section/legend) Yes (indicate where provided: page no/section/legend) n/a n/a For clinical trials, provide the trial registration number Not detected. not required. OR cite DOI in manuscript. Please add identifiers for all resources where possible Experimental Animals Sample size determination www. scicrunch. com/sciscorereport-faq or if they were not carried out 7 For a full description of scored criteria and tips for improving your score, please see https:// Yes (indicate where provided: page no/section/legend) not required. Not detected. Cell Materials Yes (indicate where provided: page no/section/legend) Laboratory protocol Yes (indicate where provided: page no/section/legend) Cell lines: Provide species information, strain.
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Provide accession number in repository OR supplier name, catalog Randomization If data are publicly available, provide accession number in repository or DOI or URL. Blinding number, clone number, OR RRID Animal observed in or captured from the field: inclusion/exclusion criteria 11 No organisms detected Not detected. not required. Please add identifiers for all resources where possible not required. Not detected. Not currently checked by SciScore not required. Provide species, sex and age where possible Sample definition and in-laboratory replication Yes (indicate where provided: page no/section/legend) n/a Model organisms: Provide Accession number in in laboratory repository (where relevant) OR RRID State number of times the experiment was replicated See laboratory animals section for information.
Not detected. Plants and microbes replicates Define whether data describe technical or biological Yes (indicate where provided: page no/section/legend) Not detected. n/a Plants: provide species and strain, unique accession Not currently checked by SciScore number if available, and source (including location for collected wild specimens) Ethics Yes (indicate where provided: page no/section/legend) n/a Microbes: provide species and strain, unique committee(s), provide reference number for approval. accession number if available, and source authority granting ethics approval (IRB or equivalent Studies involving human participants: State details of Not currently checked by SciScore Not detected. Human research participants details of authority granting ethics approval (IRB or Studies involving experimental animals: State Yes (indicate where provided: page no/section/legend) Not detected.
Evaluation of the French incidence of new cancer cases Every year since 1990, the French National Cancer Institute (INCa) has collected all new cancer cases in the Francim network cancer registries and derived an estimate of cancer incidence and mortality in metropolitan France. The Francim network registries cover between 19 and 22 departments (county-sized administrative divisions), the number of departments depending on the cancer studied  [17]. The latest available data are for 2018 (July 2019 report (1)) and are freely available as two PDF files (https:// www. sante publi quefr ance. fr/ malad ies-et-traum atism es/ cance rs/ cancer-du-sein/ docum ents/ rappo rt-synth ese/ estim ations-natio nales-de-l-incid ence-et-de-la-morta lite-parcancer-en-france-metro polit aine-entre-1990-et-2018-volume-1-tumeu rs-solid es-etud), Volume 1: solid tumours and Volume 2: haematological malignancies were used for our analysis.
Distribution of MFP activity between the different types of French accredited centres Every year, the French Biomedicine Agency (Agence de la Biomédecine) collects data on fertility preservation activity from all accredited (private or public) centres in France. The MFP data are divided into three parts: i) sperm conservation activity with a medical indication (cancer or other); ii) sperm conservation before assisted reproductive technique (collection failure, personal convenience); iii) sperm conservation from surgical samples (epididymal, deferential, testicular). The data from the 2018 activity were published in 2020 in the form of a medical and scientific report  [19]  in open access (https:// rams. agence-biome decine. fr/ prese rvati on-de-la-ferti lite). We selected the part concerning sperm conservation activity with a medical indication, which provides the number and type of centres involved (private centres, centres of the CECOS network) and the number of new sperm conservations (number of patients) in 2018.
Activity of MFP before treatment of testicular cancer (TC) or lymphoma (L) in the CECOS network Every year, the French CECOS network collects from all its centres the annual activity report of fertility preservation activities. We requested permission from the presidency of the French CECOS network and obtained an agreement to analyse the activity data in an Excel File for the year 2018. The data from the CECOS network include, by centre, the following: i) The number of MFPs before cancer treatment according to the type of cancer: haematological (HL, NHL, leukaemias, other haematological cancers), urological (TC, bladder, prostate, kidney, other urological cancer), other (digestive, bone, brain, lung, melanoma, other). (ii) The type of cryopreserved samples (ejaculated sperm, surgically retrieved sperm, and testicular tissue).
Data analysis We first estimated the distribution of MFP activity by sperm freezing in metropolitan France in 2018 in private and CECOS centres by analysing Biomedicine Agency data. Then, we estimated the proportion of patients of reproductive age with TC or L who cryopreserved sperm by comparing the estimated national incidence in 2018 of these new cancers (INCa data) with the number of MFPs by sperm freezing performed in the CECOS centres for these indications during the same period. Because our study was an analysis of the CECOS network's annual operating report and public data from the National Institute of Cancer and Biomedicine Agency, it did not require any Institutional Review Board authorization.
Discussion To our knowledge, we present the first cross-sectional study analysing national data about the rate of male fertility preservation (MFP) in men of reproductive age newly diagnosed with testicular cancer (TC), Hodgkin lymphoma (HL) or non-Hodgkin lymphoma (NHL). Our results are based on the annual incidence of new cancer cases estimated by the French National Cancer Institute (INCa) and on the annual activity report of the CECOS network, which performs 90% of French sperm banking activity for medical indication. We showed that 40% of the 3,573 15-49 years-old men diagnosed with TC or HL in 2018 received sperm banking, following guidelines of the National Institute of Cancer  [20]. Studies analysing male fertility preservation (MFP) rate before TC or HL treatment are rare. Furthermore, in a systematic review, Valipour and al.
Organisation of pathway for male fertility preservation before cancer treatment The report of the Biomedicine Agency on FP activity showed that 90% of MFP procedures with medical indication (cancer or other) are performed in the CECOS network centres. This result confirms the data assessed by a previous national study  [31]  and shows that the patient's pathway for MFP before cancer treatment in France is mainly oriented towards CECOS network centres. The missions of the CECOS network are to coordinate practices between the different CECOS centres in order to harmonize the quality of patient care, to promote research, to improve the quality of practices, to support the centres in their decision-making, to promote exchanges between centres and to inform the public of the CECOS missions (https:// www.
Strengths and limitations A strength of our study is the very high participation rate of CECOS centres covering the French territory and the use of official national data on cancer incidence and FP activity. To our knowledge, our study is the first with national data and is also the only one with a cross-sectional design; this avoids the selection bias of retrospective studies, which are based only on volunteer cancer survivors. One limitation of our study is that the Biomedicine Agency data (number of sperm banking for medical indication) did not distinguish between MFP performed for cancer and for another medical indication (the latter estimated to 20% of FP for medical indication, unpublished data of FP activity in CECOS network), which could lead to an overestimation of MFP activity before cancer treatment.