dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, this eruption is a well-documented case, which was studied in detail in  [11, 12]. In these studies, the parameters of the eruption (plume height, ash, and SO 2 flux) were estimated using the SEVIRI instrument and compared against the estimations made from the FLAME ground-based data (see Section 3). Even though there is an overall good agreement of the two instruments, some differences remain between the retrieved fluxes  [11]. The SEVIRI's SO 2 flux estimate was also compared with estimates from other spaceborne instruments (Aqua/Terra-MODIS, NPP/NOAA20-VIIRS, Sentinel5P-TROPOMI, MetopA/MetopB-IASI, and Aqua-AIRS) and shows a generally good agreement  [12]. However, some differences are visible, caused by the measurement sensitivity of each instrument and the input parameters (plume height or wind speed) used by the flux retrieval algorithm.
8 µm of a fixed pixel located over Mount Etna's summit crater and the vertical profile of temperature measured in the same area and at the same time. Due to the relatively small thickness of the volcanic plume, the "dark pixel" method could only be used on 24th December when the plume top was the highest. • From 25th to 30th December, the volcanic plume height was retrieved using a groundbased network of visible cameras. There are two stations: one in Catania on the south flank of the volcano and another in Bronte on the west flank. By knowing the wind speed and direction, it is possible to retrieve the plume height on the camera's recorded footage  [11]. The uncertainty associated with those data is estimated at ±500 m  [25]. The two datasets of flux emissions are chosen because they are based on different types of instruments and methodologies.
• The SO 2 concentration columns retrieved by the instrument TROPOMI (TROPOspheric Monitoring Instrument) onboard the Sentinel-5 Precursor  [26]  are available since 2018. The spatial resolution of the instrument is 3. 5 × 7. 2 km 2 for this study. After the first measurement period, during 2019, its spatial resolution was improved to 3. 5 × 5. 5 km 2 at nadir. Its temporal resolution over the Mediterranean region is of one or two overflies per day (around from 11 to 12 UTC). Sulfur dioxide is measured by TROPOMI in the UV band. In this work, we use two datasets. The first one, named TROPOMI_OP, corresponds to the operational product  [27] , which uses a retrieval algorithm based on the DOAS method (Differential Optical Absorption Spectroscopy). The uncertainty associated with those data is estimated at 35%  [26].
• The total SO 2 columns retrieved using the OMI (Ozone Monitoring Instrument) instrument onboard the Aura  [29, 30]  satellite have been available since 2004. Their spatial resolution is 13 × 24 km 2 at nadir and their temporal resolution over the Mediterranean region is of one or two overflies per day (around from 11 to 12 UTC). Similar to TROPOMI, sulfur dioxide is measured in the UV band. The uncertainty associated with those data is estimated at 30%  [31]. The retrieval algorithm is based on a different method than the one used for TROPOMI's products; the PCA method is used instead (Principal Component Analysis)  [29].
• The total columns of SO 2 retrieved using the IASI instrument (Infrared Atmospheric Sounding Interferometer) onboard Metop-A and Metop-B  [13, 32]  have been available since 2006 and 2012, respectively. The spatial resolution of the instrument is a circle of 12 km diameter at nadir and its temporal resolution over the Mediterranean region is about four overflies a day (two around from 08 to 09 UTC and two around from 19 to 21 UTC). Unlike TROPOMI and OMI, sulfur dioxide is measured in the IR band, which means that it is sensible up to the pole. However, the sensitivity below 5 km altitude is strongly reduced. The uncertainty associated with those data is estimated at 50%  [13].
MOCAGE Chemistry-Transport Model MOCAGE (Modèle de Chimie Atmosphérique à Grande Échelle) is the offline global and regional three-dimensional chemistry-transport model (CTM) developed at CNRM  [33, 34]. Its applications are very diverse: effect of climate change on atmospheric composition [e. g. ,  [35] [36] [37] [38] ], global impact of biomass burning  [39]  and of volcanic emissions  [1]  on air composition, or operational use for the forecasting of air quality both for France (Prev'Air program  [40] ) and for Europe  [41]. As an offline model, the meteorological fields (wind speed and direction, temperature, humidity, pressure, precipitation, and cloud) used as inputs in MOCAGE are from an independent numerical weather prediction model. MOCAGE can produce simulations with global and regional resolutions thanks to its grid nesting capability (from 2 • × 2 • to 0.
Organic and black carbon aerosols are taken into account following MACCity  [43]. DMS oceanic emissions are a monthly climatology  [57]. Finally, the biomass burning emissions are from the daily GFAS products  [58]. The first simulation, named FL, uses the SO 2 flux estimations retrieved by FLAME while the second simulation, named SV, uses the retrieval from SEVIRI. In the two simulations, eruptive fluxes are injected between 24th December at 10:45 UTC to 30th December at 12:30 UTC, each 15 min. In the FLAME dataset, SO 2 fluxes are available for only a few hours per day. In order to have a continuous emission flux in the model to simulate the emission of the volcano when not sampled by FLAME, for each day D, we performed an interpolation during the FLAME data gap. Assuming we have no other source of information, we chose to use two values for the interpolation.
For the first half of the missing period, we use the last estimation available on day D and for the second half of the missing period, we use the first estimation available on day D+1. On 25th December, the presence of a meteorological cloud also masked part of the volcanic eruption for both instruments, principally for FLAME, which has no retrieved data this day. Therefore, in the same manner as for FL missing data periods, the emission flux is interpolated in the FL and SV simulations for 25th December. The corresponding emission fluxes implemented in the simulations are presented in Figure  1. For most days except 27th and 30th December, the FLAME and SEVIRI emissions at the time periods when the FLAME data are available mostly agree within their uncertainty range. On the 27th and 28th, there was a gap of data in the FLAME station located south of the crater due to a technical problem.
Concerning the quantity of volcanic SO 2 , we first perform a comparison against TROPOMI_OP and TROPOMI_MPIC at 11:00 UTC (Figure  4A ). The SO 2 columns simulated in FL are lower than the two datasets of observations, with the exception of Plume 3. In the SV simulation, the concentrations of volcanic SO 2 are consistent for Plumes 1 and 2 but slightly higher. For example, for Plume 2, the maximum of volcanic SO 2 simulated is 13 DU against 11 DU for the estimations of the two TROPOMI products. However, for Plume 3, the columns of SO 2 simulated in SV are much higher than the observations. This could be due to an overestimation of the SO 2 emission flux by SEVIRI due to ash. Secondly, we compare the simulations against the OMI estimates (Figure  4B ) at 12:00 UTC. Here, we note that Plume 2 and 3 in the SV simulation are consistent with the observations in location and in quantity of SO 2 within the plume but with a slight overestimation in Plume 3.
27th December On 27th December, we have a total of five crossings of satellite instruments available, but, here, we only select the two with enough observation points. Therefore, we only show the comparison against the UV instrument TROPOMI at 11:00 UTC and 13:00 UTC. Figure  5  presents the total column of SO 2 simulated in FL and SV and the observations of the two datasets TROPOMI_OP and TROPOMI_MPIC. Even if Plume 1 is on the edge of the TROPOMI swath and not totally detected, the transport of the plume is correct in the simulations with a location of the plume consistent with the complex shape showed in the observations. However, the spatial variability within the volcanic plume is not as fine as in the TROPOMI observations, especially for Plumes 3 and 2. The modeled plume is smoother and more spread out as expected because the model resolution is coarser (0.
Secondly, for the UV instruments, the use of different algorithms between OMI and TROPOMI leads to differences in the retrieved columns. The instrument OMI, using a PCA algorithm, retrieves higher amounts of SO 2 compared to TROPOMI, using a DOAS algorithm. Note that with the finest spatial resolution, TROPOMI provides more detailed information on the fine scale features within the plume than OMI and IASI. Finally, even with the same instrument (TROPOMI), the use of similar but slightly different algorithms can generate differences in the retrieved columns. Here, the observational data from the TROPOMI_OP product are generally a bit higher than those from the TROPOMI_MPIC product, but in some local places the reach a factor of 2. Knowing this, we now discuss the evaluation of the two simulations compared to the observations starting with the representation of the volcanic plume transport.
Moreover, the presence of ash can lead to an overestimation of the emission flux from SEVIRI even after the correction is applied by the algorithm. For FLAME, the impact of ash is an underestimation. Another part of the uncertainty is linked to the data availability. The emission fluxes retrieved from the SEVIRI instrument are available during the whole period of the eruption except over a 12-h time period on 25th December, unlike those from FLAME, which are available only a few hours per day and not every day. To ensure the continuity of the emission flux in the model, we completed the missing part of the FLAME emission fluxes using an interpolation that generally leads to less realistic results in the FL simulation than in SV. These two datasets could be combined to provide a better estimate of the SO 2 fluxes.
The reason they were not applied to the results of the MOCAGE model is because they were only available for one product (TROPOMI_OP). In order to analyze the impact of applying the averaging kernels on the model results, we performed this operation on the MOCAGE simulations based on TROPOMI_OP dataset. We used the product that assumes a SO 2 plume height at 7 km because it is the closest to the 5 km average height considered in this case study. We illustrate the results for two days showing different behaviors. Figure  11  presents results of this comparison for 26th December at 11:00 UTC. The FL and SV panels of this figure can be compared to Figure  4  in order to see the impact of applying averaging kernels to the model results. We can note that the plume location remains the same but the values are lowered when applying the averaging kernels to the simulated data.
On the contrary, more data are available with the UV instruments (OMI and TROPOMI), but the use of two different algorithms leads to small differences in the SO 2 retrievals. This highlights the uncertainty of the observational data available to validate the model simulations. The analysis of the simulation results is based on a simple semi-quantitative method for the model-observation comparisons. To perform a more quantitative analysis, it would be useful to have the averaging kernels for each observation product of the SO 2 column and to apply them to the simulated SO 2 concentrations, because they take into account the characteristics of each instrument. This could be particularly important for the comparison with IR instruments that have a low sensitivity below 5 km altitude. For a fair comparison between the SO 2 columns from different satellite observations, the averaging kernels were not applied because they were only available for TROPOMI_OP and its model results.
However, we assessed the impact of using them. The distribution of the concentration values using averaging kernels provides an objective comparison between the simulations and the observations. The results with the averaging kernels are consistent with those of the semi-quantitative method. A promising method to improve the simulation of volcanic plumes is to perform a data assimilation of the quantity of SO 2 retrieved using the spaceborne instruments data into a model. This is currently under development and evaluation in MOCAGE. In the case of advanced data assimilation methods, both the observation and the model errors can be taken into account to provide an optimal model estimate of the SO 2 concentration where the observations are available (e. g. ,  [64] ).
In our dataset, in most larvae the left Hb neurons were similarly activated as the right counterparts by cadaverine (at 1 mM)-a difference that may be due to the higher stimulus concentration we used. The preoptic area (PO), a hypothalamic region known to mediate nocifensive behaviors  61  , also generated bilateral neuronal activity during unilateral OP stimulation. Sensory encoding becomes progressively weaker along the rostral-caudal axis in these regions. Beyond the forebrain regions, there is only direct sensory encoding by small subsets of neurons in the diencephalon (Di), mesencephalon (Me), cerebellum (Ce), and rhombencephalon (Rh). Notably, during bilateral OP stimulation, there was symmetrical activation with an increase in overall sensory information content across regions (Fig.
1d ). During experiments, the device was leveled and the outflow rates of the two outlets were identical. The two-dimensional (2D) flow velocity profile (Fig. 1b ) was obtained by computational fluid dynamics simulation using the CFDTool toolbox (Precision Simulation) in MATLAB (R2018b, MathWorks, USA), assuming incompressible Newtonian fluid flow and with a grid size of 1. 4 × 10 -4 m. The geometry and boundary conditions were set according to the aforementioned device parameters.
Chemosensory avoidance navigation and survival assays During a chemosensory avoidance navigation assay, three to six larvae aged 5-7 days d. p. f. were placed in a swimming arena and allowed to acclimate for 15 minutes. Swimming and navigational behaviors were then recorded by a high-speed camera at 240 fps (Mako G-030B PoE, Allied Vision, Germany) and the Streampix 7 (Norpix, Canada) image acquisition software under 850-nm IR illumination (ANGX-1000-CH1-24V, TMS, Malaysia) for 2 hours in the absence of visible light. Fluid temperature was maintained at ~28 °C throughout the assay. For the control group, all three fluid streams consisted of water. For the static arena control group, the arena is completely filled with water without flow. For the cadaverine avoidance assay groups, the rightmost stream contained 1 mM cadaverine (Sigma, USA) dissolved in water (Fig.
5, Andor, UK). Characterization of the point spread function (PSF) was performed by measuring sixteen 0. 1-μm fluorescent beads (F8800, Thermo Fisher Scientific, USA), consisting of four samples from each quadrant of the imaging field of view inside the head chamber of the µfluidic device. During an experiment, to correct for axial drift, a reference plane was calibrated with respect to the initial measurement at the beginning of each trial. All control units were synchronized using a multifunctional I/O device (PCIe-6323, National Instruments, USA) and custom code written in MATLAB (R2017b, MathWorks, USA).
For all experiments, larval subjects were only picked if they exhibited normal spontaneous swimming in the tank. Both right before and after imaging experiments, the larvae's heartbeat and response to tactile stimuli were examined. All larvae included in the datasets exhibited normal heart rate and responded to tactile stimuli. In an imaging experiment, a single larva was loaded (via the fluid outlet) and fitted into the trapping chamber of the PDMS-based microfluidic device using a syringe pump and monitored under a surgical microscope. The introduction of bubbles to the µfluidic system was carefully minimized during system set up and larva loading, to avoid unwanted fluid flow pattern changes or mechanical instability of the larval subject. The larva was allowed to acclimate for 15 minutes.
e. , left, right, or bilateral OP stimulation). During each trial, after a 30-second baseline recording period, the first (water) and the second (cadaverine) stream in front of the OP(s) were switched off sequentially with 10-or 25-second gap to allow the second (cadaverine) followed by the third stream (water) to contact the OP(s), resulting in the initiation and termination of nasal stimulation, respectively (Fig. 2f , left). Recording continued for 10 or 15 seconds after stimulus cessation. Simultaneous tail flipping behavior was imaged at 200 fps under IR illumination by a CCD camera (F032B, Pike, Germany) and the Streampix 7 (Norpix, Canada) image acquisition software.
Navigational behavior analysis Individual frames of the navigation behavioral tracking videos were first registered for translation, background-subtracted, and contrastadjusted. The heading orientation (10°resolution/accuracy) and xycoordinates (0. 1 mm resolution/accuracy) of every larva in each frame were extracted using a semi-automated template matchingbased tracking program custom-written in MATLAB (R2018b, Math-Works, US). All tracking results were manually verified on a frame-byframe basis and corrected when necessary. When larvae came in close proximity (≤ 4 mm) to either the arena boundary or with each other, the trajectories were excluded from analysis of turn behaviors to rule out potential social or mechanical cue-associated movements. A chemical-water border was defined with the aid of an IR dye (IR 806, Sigma, USA) in a separate assay (Fig.
Analysis of tail-flipping behavior Each frame of tail flipping behavioral imaging was backgroundsubtracted and the tail tip-waist angle was extracted by a custom MATLAB program. The tail tip-waist angle was defined as the angle between the long axis of the larval subject body and a straight line joining the tail tip and the waist. The extracted angles were manually verified on a frame-by-frame basis (by S. K. H. S. ) and used for the calculations of tail flick event frequency and angular velocity.
Image processing and calcium signal extraction Detailed larval zebrafish brain anatomical stacks were registered to the Z-Brain Atlas 70 using affine transformation followed by non-rigid image registration using custom scripts written in MATLAB (R2018b, Math-Works, US). Functional imaging planes were matched to the corresponding anatomical stack by maximization of pixel intensity crosscorrelation and manually verified. Stripe artifacts in the anatomical stacks and functional imaging frames were removed using the Variational Stationary Noise Remover (VSNR) algorithm  99. Functional imaging frames were motion-corrected using the NoRMCorrE algorithm  71. Trials with blurred frames due to in-frame drifts were discarded. Regions of interest (ROIs) corresponding to individual neurons were then extracted using the CaImAn package  72.
Identification of sensory-encoding, motor-encoding, and sensorimotor neurons The first 10. 5 seconds (21 imaging frames) of the imaging data of each trial were excluded from analysis to avoid the inclusion of transient activity evoked by the onset of light sheet illumination. The minimum fluorescence intensity in the subsequent 4. 5 seconds (9 imaging frames) of each ROI was used as the baseline fluorescence (F). The dF/F signals of remaining imaging frames were calculated as the calcium responses of each ROI. The left (C L,t ) and right (C R,t ) cadaverine stimulus profiles were extracted using the abovementioned ROIs directly in front of the respective OPs and normalized to the [0, 1] range. The cadaverine stimulus profiles used in bilateral OP-stimulated trials (C L+R,t ) were obtained by averaging the left and right stimulus profiles and normalizing them to the [0, 1] range.
Acknowledgements We thank  Tom Mrsic-Flogel  and Florian Engert for helpful comments on an earlier version of the manuscript;  Jan Schnupp  and  Vincent Cheung  for insightful discussions;  Florian Engert  for the provision of the Tg(e-lavl3:GCaMP6f) zebrafish. We thank  Becky Yung ,  Florence Yau ,  Dorothy Ieong ,  Anki Miu , and  Rebecca Chau  for administrative support to the project. This work was in part funded by a  Croucher Innovation Award  ( CIA20CU01 ) from the  Croucher Foundation  (H. K. ); the  General Research Fund  ( 14100122 ) (H. K. ), the  Collaborative Research Fund  ( C6027-19GF & C7074-21GF ) (H. K. ) and the  Area of Excellence Scheme  ( AoE/M-604/16 ) (H. K. ) of the  University Grants Committee of Hong Kong ; the  Excellent Young Scientists Fund  ( 82122001 ) from the  National Natural Science Foundation of China  (H.
Key points What is currently known? • The rapid evolution of artificial intelligence models has swiftly prompted much academic and media discourse regarding their potential for disruption as well as their transformative power impacting multiple facets of the economy, society, and environment. • Software tools like ChatGPT and other comparable ones utilizing generative AI models can produce incredibly human-like responses to queries, yet, they can also be profoundly erroneous, raising significant ethical and moral issues, and their adoption by HRM practitioners. What this perspectives editorial adds? • Provides a comprehensive summary of the advancements, constraints, and commercial applications of generative AI. • Offers 11 perspectives that advance scholarship in HRM and present a collection of unexplored research opportunities for HRM scholars.
ChatGPT represents a significant departure from conventional AI algorithms based on ML which can identify patterns in large datasets and make predictions. We can observe this predictive ability in search engines like Google, which offer autocomplete recommendations to enhance quality of search results. However, ChatGPT goes beyond mere prediction. It utilizes generative AI language models, enabling it to generate entirely new content based solely on the question prompts provided by a user. This textual content can take various forms, such as news articles, poetry, movie scripts, business plans, software codes, research manuscripts, and marketing campaigns. The quality of its output relies on the quality of the input it receives, considering both the training data it has been exposed to and the prompts provided by users to describe the task they want it to accomplish.
The language model ("brain" behind the ChatGPT) uses generative AI. Generative AI refers to integration of ML models to generate new content, including text, audio, video, images, software code and simulations, based on large datasets used to train the model. However, the contextual relevance of new content generated will depend on the quality, timeliness, and relevance of the training dataset (Boston Consulting Group Generative  AI, 2023; McKinsey & Featured Insights, 2023). For example, GPT4 is trained using around 45 terabytes of data gathered from all over the web only until 2021  (Open AI GPT 4 Technical Report, 2023). Therefore, the responses are unlikely to provide recent and up-to-date information. Let us uncover the history behind ChatGPT to better understand its evolution (Figure  1 ).
Large Language Models (LLMs) are trained using vast amount of textual data and they infer relationships between words within the text to predict the next word in a sequence of words to form a sentence. However, initial LLMs processed each word individually and sequentially in a block of text, limiting the context, meaningfulness, and efficiency of the outputs. This led to the development of transformers in 2017, which can process all words in a dataset simultaneously, giving varying weights to different parts of the text, making the outputs more meaningful and accurate  (Uszkoreit, 2017). The feature enabled processing significantly large datasets efficiently and led to the development of GPT models. OpenAI launched GPT-1 in 2018, followed by GPT-2 in 2019, GPT-3 in 2020, Instruct-GPT and ChatGPT in 2022, followed by GPT-4 in March 2023 (see Figure  1 ).