dataesr/scientific-paragraphs-categorization huggingface dataset:
Real ARGO-CR dataset In the second experiment, we consider images of a ARGO-CR calibrated sample acquired by an epifluorescence microscope. Images were acquired on an AxioObserver Z1 (Zeiss, Germany) with 63x/1. 15W Korr LD CApochromat objective, ORCA Flash 4. 0 camera (Hamamatsu, Japan), 540570nm LED excitation and 581619nm emission filter. The spatial pattern is similar to the one used in our simulated data, with the distance between the two middle filaments of each set increasing by 30nm and varying from 0 to 390nm (see Figure  4  for a graphical representation). In this second example the resolution reached by the methods can be estimated under real conditions. Only FluoGAN and SRRF are applied to these data, as due to a slight sample shift during acquisition, these data are not adaptable for COL0RME, which is based on the estimation of temporal covariance matrices.
Ostreopsis images To test FluoGAN on challenging real biological samples, we considered a dataset of the unicellular alga Ostreopsis cf. Ovata (see Figure  10a ). Such dataset combines several difficulties. First, it shows a 20m thick sample which can exhibit a strong out-of-focus signal which is clearly the case here for the microtubules staining as they form a cortical structure all around the alga. Another difficulty is the reduced transparency and nonenegligible turbidity, which induces PSF distortion. Due to those complications, confocal microscopes cannot acquire through the whole thickness of the alga. To avoid this, we considered epifluorescence acquisitions. The flexibility and the data-adaptivity of FluoGAN allows to deal with such data, whereby other approaches fail. For the experimental design, microtubules were stained with TRITC dye, but also with Hoechst for DNA (for details see  [33] ).
Acknowledgments The authors would like to thank  D. Velasquez  and  S. Castagnetti  from the  Developmental Biology Laboratory of Villefranche sur Mer (LBDV ) who kindly prepared and provided the experimental samples. Image acquisitions were conducted in the microscopy platform PIM (member of MICA microscopy platform labelled by IBiSA), supported by  EMBRC-France , whose  French  state funds are managed by the  ANR within the Investments of the Future program  under reference  ANR-10-INBS-0. The work of VS and LBF has been supported by the  French government , through the  3IA Côte dAzur Investments  in the Future project managed by the  National Research Agency , grant  ANR-19-P3IA-0002. VS, LBF and LC acknowledge the support by the  ANR MICROBLIND  grant  ANR-21-CE48-0008. LC acknowledges the support received by the  EU   H2020 RISE program   NoMADS ,  GA 777826 , the one received by the  GdR ISIS  grant SPLIN and by the  IEA CNRS  grant  VaMOS.
Training ChatGPT for its versatility and powerfulness is not cheap. According to miscellaneous information sources, OpenAI originally used ~40 GB of text data to train the early GPT model with 8 NVIDIA V100 GPUs and 256 GB of RAM. To train GPT-3, which laid the foundation for ChatGPT, the 2016-2019 Common Crawl data set  [6]  of 45 TB of compressed plain text was used. Nowadays the data set used for training ChatGPT consists of more than 145 million dialogues scraped from various social media and online knowledge bases (eg, Twitter, Reddit, and Wikipedia). Note that it is also expensive to clean up such text data as spam, offensive language, low-quality content, and so on need to be removed before they can be fed to ChatGPT. The typical hardware configuration for training ChatGPT may include 64 or more NVLink-connected NVIDIA V100 GPUs with 32 GB of memory each, and each round of training may take 2 weeks.
The estimated cost of training ChatGPT is close to US $5 million dollars, and for general large language models, the training cost falls between US $2 million and US $12 million  [7]. OpenAI obviously found the right business model to share such costs via, for example, a monthly user subscription, so there are strong reasons to believe that ChatGPT will survive well into the foreseeable future. In addition, Microsoft, the big investor behind OpenAI, recently announced a series of Office products that will be deeply integrated with ChatGPT for AI-assisted productivity improvement. OpenAI also launched its own application store, and it allows connection to >5000 other applications via the Zapier ChatGPT plug-in. ChatGPT looks unstoppable. There have been various concerns about AI since its concept was consolidated back in the 1950s.
Scanning electron microscopy A Zeiss Supra 55 VP scanning electron microscope was employed, and the micrographs were obtained with a secondary electron detector at 10 -5 mbar. The silk was coated with a 10 nm gold layer by means of a Leica SDC050 sputter coater equipped with a quartz micro balance to confirm the layer thickness. The diameter of the silks was evaluated manually using the image analyzing software Smart TIFF.
Pixels at non-sensitive detector areas were excluded from the fit. The lattice spacings (d[hkl]) were derived by azimuthally integrating the data and plotting the corresponding intensity against q (q = 4πsinΘλ -1 = 2πd -1 , where 2Θ is the Bragg angle, λ is the wavelength, and d is the lattice spacing). The crystalline domain size along certain (hkl) lattice directions (L[hkl]) was calculated from the full-width-at-half-maximum of the Gaussian fitted Bragg peaks according to Scherrer's Eq. [56]. The degree of crystallinity was calculated by the total area under the Bragg peaks divided by the sum of the area under the Bragg peaks and diffuse scattering. The unit cell parameters a (interchain), b (intersheet), and c (chain direction) of the orthorhombic poly(L-Ala) structures were calculated based on the reflections (210), (020), and (002), respectively  [57].
Tensile testing The tensile properties of the silk were measured using a FAVIMAT single fiber testing device from Textechno equipped with a 210 cN load cell. The gauge length was set to 3 cm, and the tests were performed at a 3 mm min -1 speed. Experiments were carried out at 25 • C and 30 % humidity. Silk strands were carefully removed from the silk frame and cut with scissors. To ensure straight suspension of the silk in the device, a clamp with a weight of 43 mg was attached to one end of the fiber and the silk was hung vertically between the two grips of the device. After the measurements, the fibers were inspected by SEM and results for double silk strands were discarded, leading to eight single fibers measured per condition. Smart TIFF software was used to measure the diameter the silk fiber from the SEM images at six points on areas away from fibers' end to avoid artefacts caused by clamping.
Acknowledgments The authors would like to thank  Dr. Stephan Puchegger  and the faculty center for nanostructure research at the  University of Vienna  for support with scanning electron microscopy as well as  Professor Bruno K. Podesser  and his team at the  Center for Biomedical Research, Medical University of Vienna  for sharing euthanized rats for tissue harvest. We are grateful for the constant support from  Dr. Marion Gröger  and her team at the  Core Facility Imaging of Medical University of Vienna. The XRD experiments were performed on beamline ID13 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. We are grateful to  Manfred Burghammer  at the  ESRF  for providing assistance in using beamline ID13. We also thank  Dr. Robyn Plowright  from  Newrotex Ltd. for partly providing the silks employed for the revisions.
(2021)  compared several decision-making tools for GI planning and implementation by identifying the capabilities and challenges they offered to end-users. Other reviews have presented a more specific focus. For example,  Li & Babcock (2013)  focused on the hydrological modelling of green roofs, while  Jayasooriya & Ng (2014)  presented an economic analysis, comparing various financial issues linked to ten selected GI modelling tools, but with a de-emphasis on the hydrological modelling aspect. The aforementioned reviews have all focused on the advantages and drawbacks of tools such as the Storm Water Management Model (SWMM)  (Rossman, 2015)  and Hydrus software  ( Šimůnek et al. , 2016). None, however, has specifically assessed how the practice of hydrological modelling has been dealt with in the GI literature.
Bias and limitations We selected relevant papers by employing an approach similar to that described by  Moher et al. (2009). This involved a keyword search in two databases, duplicate removal, title screening, abstract screening, and finally, full-text screening. Based on the papers ultimately selected, examples of good practice were highlighted and discussed. This review aims to be systematic and in so doing restrict bias  (Moher et al. , 2009). We managed bias in two ways. The first was by applying a systematic reading approach to each paper, and the second by making the reader aware of existing biases.
Data availability and reproducibility The paper analysis methodology adopted in this review is presented in detail Section 3, and examples of items assignment for three selected papers are presented in the Supplementary Materials (Table  A3 ). A list of the selected 270 papers, together with the items assigned for each category, is also available in the Supplementary Materials. However, in terms of reproducibility, the items assigned should be considered with a number of caveats. As noted in the previous section, some papers fall within the 'grey zone'. Their evaluation may contain biases and should thus be regarded as at least partly subjective. Item assignment was performed with the aim of identifying general trends of practice in the field and not the individual assessment of each papers.
Parameter selection and model testing: how did the authors select their parameters and test their model? This review has also evaluated the practice of selecting model parameters and testing datasets  (Table 2h, i). In environmental study contexts, models require relevant parameters and inputs that are suited to their objectives. For this to be achieved, a framework is required as an aid to parameter selection. Parameters may be chosen from the literature or may be based on expert knowledge. They can be set by manual calibration, or assigned by automatic calibration. Furthermore, an evaluation of whether a calibrated model is suited to its study objectives will be influenced by the selection of its testing dataset  (Beven and Young, 2013; Sargent, 2004; Shen et al. , 2022). In this context,  Silberstein (2006)  emphasized the importance of collaboration between 'experimentalists' and 'modellers'.
The difficulties inherent in environmental models (we refer here to 'model equifinality':  (Beven, 2006) ) mean that achieving a calibrated model suited to all conditions, and which encompasses all processes, is unlikely  (Beven and Young, 2013). The selection of testing datasets must therefore be performed with care in order to be able to justify that a model is fit for purpose. The same applies to the selection of calibration datasets, which is outside the scope of this review. A large uncertainty should be assigned to conclusions drawn from models utilized outside their calibration range. Silberstein (2006)  has illustrated this point using an example in which the selection of a testing dataset concealed a seasonal pattern, leading to a significant change in model performance. Thus, good practice demands an investigation not only of how parameters are selected, but also of the extent to which the dataset used for model testing is aligned with the objectives of model utilisation.
, 2019). Fig. 2  shows that the application of best practice in UA and SA methods, as defined in this study by the terms 'SenJust' and 'UncJust' in Table  2 (h,i), has not increased in recent years. This is a major issue in terms of the development of the field. We found that it is rare to apply a method that propagates uncertainties to model outputs, and this places limits on the reliability of the published results. UA and SA methods may have been ignored by researchers in green infrastructure modelling because the most commonly used software applications (SWMM and Hydrus, used by 27% and 10% of the reviewed papers, respectively) do not include Fig. 2. Evolution of the practice of applying uncertainty and sensitivity analyses in green infrastructure modelling. The figure shows a 95% confidence interval with a 2-year moving window.
As Fig. 1j , current model testing practice (as defined in Table  2 ) relies on limited data volumes, and this has characterised 60% of the papers reviewed. In a limited number of studies, we have judged that their data volumes have been sufficient for model testing, as is the case for the multimodel comparison published by  (Zhang and Guo, 2015). In general, however, we consider that continuous GI time-series data such as soil moisture, regulated discharge, overflow, infiltration, etc. , have not been proven to belong enough to ensure sufficient data representativeness to achieve the modelling objectives. A 'long enough' dataset is defined as being of sufficient extent in time to permit convergence of the estimated quantity of interest. More specifically, there may be a difference between an estimator of the performance of a GI in a specific location based on available data, and that based on theoretically sufficient data.
The difference between the 'true' performance, and an estimator based on limited data, has to be sufficiently small in accordance with the objectives of the study. In addition to proving convergence, good practice in situations where limited data are available must encompass; i) a presentation of the characteristics of the testing dataset that includes the number and characteristics of rainfall events, as is the case in  Carson et al. (2013) , who classify these events based on their depth, and ii) the consequences and uncertainty linked to a limited dataset, as is the case in  Maniquiz et al. (2010) , who state that due to limited data, calibration and validation were unable to provide a reliable parameterization of their model. An increase in levels of justification by authors of their testing datasets should help to increase the confidence in developed models.
Likewise, the sharing of data may help to push back limitations linked to limited data volumes. Another approach to the management of limited data sets, especially in the case of rare and extreme precipitation events, consists of creating a database based on an experimental setup. Experiments can readily be designed to provide relevant data with the aim of aligning modelling objectives with calibration or testing (e. g. ,  Hamouz et al. , 2020; Vesuviano et al. , 2013).
The issues of sensitivity analysis and uncertainty management have recently been introduced to the field of urban drainage  (Deletic et al. , 2012; Tscheikner-Gratl et al. , 2019)  but do not appear to have had any impact in the sub-field of GI modelling. It may therefore help to introduce a supplementary step involving implementing these aspects in practice by means of a practice tracking framework. The use of such frameworks in modelling studies could be subject to peer review on the submission of manuscripts for publication. Alternatively, a framework could be filled by journal editors in agreement with reviewers, or verified by a qualified reader as part of an open forum discussion. This may improve the practice in the field if requested by journal editors and reviewers. In this regard, we have attempted here to develop such a framework, called the 'STAMP' framework (STormwater infrastructure Alignment of Modelling Practices), which we believe offers a transparent and efficient means of sharing meta-information related to GI modelling tasks (Error! Reference source not found.
Improving modelling tools that facilitate good practice Researchers in the field of GI hydrological modelling often confuse the terms 'model' and 'modelling tool'. In the case of the latter, they may be referring to a computer program or software. A model is a simplified representation of a physical world  (Wheater et al. , 2007) , which may be in a mathematical or scaled-down physical form  (Jajarmizadeh et al. , 2012). A modelling tool is used to facilitate the modelling process and often includes several models and model choices. In the case of mathematical models, such tools may consist of software packages with a user interface, such as SWMM and Hydrus. Our review indicates that the most commonly used modelling tools exert a major influence on many of the steps of GI modelling practice.
For instance, the current version of SWMM, which was applied by 27% of the authors reviewed as part of this study, does not include built-in functions for automatic model calibration, sensitivity analysis or uncertainty analysis. We suggest that extending these open tools to include such functionality will represent a positive step towards promoting a good modelling practice by making the process more user-friendly for non-programmer users. There have been several attempts made recently to develop software tools and programming packages that will enable SWMM to perform model calibration and other functions. Examples include the 'swmmr library for R'  (Leutnant et al. , 2019)  and the 'pyswmm package for Python'  (McDonnell et al. , 2020). Despite some disparities in their capabilities and documentation, both of these libraries enable the user to modify parameters, execute modelling and extract the results of models developed in the SWMM tool.
Such functionality includes modelling tasks such as calibration, sensitivity, and uncertainty analysis. The use of these libraries requires a certain level of programming skills, which may be challenging for some GI researchers and stormwater practitioners, such as engineers and urban planners. Alternatively, a small number of external software tools with user interfaces have been developed with the aim of calibrating models developed by SWMM. These include OSTRICH-SWMM  (Shahed et al. , 2020)  and SWMM2PEST  (Lin et al. , 2019). The former requires the pre-processing of optimizing files using a Python package. We believe that the development of user-friendly tools is an important step in the promotion of good modelling practice. Nevertheless, we also argue that it this may not be sufficient to increase the adoption of the steps involved in 'good modelling practice'.
For example, the Green Infrastructure Flexible model (GIFMod/https://gifmod. com/) enables users to build GI models at various levels of complexity, and also includes built-in functions for automatic model calibration, sensitivity analysis, and uncertainty analysis  (Massoudieh and Aflaki, 2017). To date, according to the Web of Science, GIFMod has only been applied in four published studies  (Alikhani et al. , 2020; Almadani et al. , 2023; Chen and Chui, 2022; Yang and Chui, 2019). In comparison, the SWMM software has been applied in more than 700 published studies since the release of GIFMod. It should be emphasised that our intention in this paper is not to promote a specific tool, but to highlight the fact that alternative, high-capability software tools to SWMM do exist, even though they are still rarely applied.
Sharing data for improved model evaluation Collecting GI-related hydrological data is a relatively new and growing field of research. As a result, there are only limited amounts of GI data available on which to apply good modelling practice. This review has also shown that the practice by authors of presenting datasets for model testing is limited. We believe that the sharing of datasets that have been applied and analysed will offer great opportunities for the improvement of GI models and modelling practice. The study by  Versini et al. (2020) , in which they provided hydrological measurements of a large-area green roof in Paris, represents one of the early attempts to share GI data. In the field of hydrological modelling at catchment scale, many large-scale measurement datasets have been published that have led to the development and verification of a number of modelling techniques.
Examples include the CAMEL dataset applied in the US  (Newman et al. , 2015)  and the CAMELS-GB in Great Britain  (Coxon et al. , 2020). Such practice should be integrated into tthe field of GI and GI modelling, taking the opportunity of the international drive toward data-sharing. Similarly, we encourage the sharing of both knowledge and tools as a means of standardising the management of monitoring data  (Bertrand-Krajewski et al. , 2021).
In this regard, the recently issued Journal of Trial and Error (https://journal. trialanderror. org/) may offer an incentive to researchers, and in doing so help to improve practice. Only a few of the GI modelling studies reviewed in this study published negative results with discussions of the implications for current GI models. Peng & Stovin (2017)  discussed the unsatisfactory performance of an uncalibrated SWMM-LID module in connection with the modelling of runoff from large-area green roofs. They also discussed issues related to evapotranspiration modelling in their SWMM-LID module. Randall et al. (2020)  also evaluated the SWMM-LID module in the case of a single-event modelling of a permeable pavement. They pointed out some of the limitations of the module when it came to simulating events with multiple peaks.
• Despite improvements in recent years, authors continue to fail to justify the selection of model parameters and provide clear documentation of the methods they use. The representativeness of data used to test models is only rarely discussed, revealing a clear flaw in model testing practice. In order to improve the alignment between modelling methods and objectives, the authors propose the adoption of a standardized modelling framework (named STAMP). This could help researchers and reviewers to document and justify their modelling steps. The authors also suggest that modelling tools should be enhanced in order to make the implementation of 'good practice steps' more user friendly. We also recommend better sharing of datasets, and more thorough analysis with the aim of enhancing model testing and transferability.
Acknowledgment This study was supported by the  Klima2050 Centre for Researchbased Innovation (SFI)  and funded by the  Research Council of Norway  and its consortium partners under grant no. 237859/030. The research was performed in part within the framework of the  OTHU (Field Observatory  for Urban Water Managementhttp://othu. org, last access:  26 May 2022 ) and realized within the  Graduate School H2O ' Lyon  ( ANR-17-EURE-0018 ) and  Université de Lyon (UdL)  as part of the  program 'Investissement d'Avenir'  and involving the  Agence Nationale de la Recherche (ANR).
Parameters selection and model testing i) How are the model parameter selected? j) How is the model tested? ParNone The TesNone The model is not tested, and it is not justified why it is not tested parameters selection process is not mentioned or not aligned with the objectives ParMent The TesLim The data for testing are limited (i. e. , it is not explained why the dataset is sufficient) and it is not justified parameters are selected from literature or measured ParLitJust The TesLJust The data for testing are limited, but the limitations are stated, or the dataset is presented parameters are selected from literature or measured; it is explained why their choice is relevant to the study ParMan A manual calibration is used to select parameters TesSJustThe dataset is sufficient (i. e. , it is justified why the data are sufficient through the convergence of performance indicators), and the dataset is presented* (continued on next page).