dataesr/scientific-paragraphs-categorization huggingface dataset:
Instrumentation The CO 2 datasets were continuously collected with a time step of 5 s at each station on the time periods given in Table  2 , using CRDS (Cavity Ring Down Spectroscopy) analyzers. Three of these analysers were also monitoring atmospheric CO (carbon monoxide), a useful tracer of uncomplete combustion processes, at OHP, SME et ERSA (PICARRO G2401 model) sites. At CAV, the CRDS analyser was not monitoring CO   The CRDS analyzer worked continuously at a flow rate of 150 mL min -1 to pump air through a Synflex® ¼ inch line. Filters (cutoff diameters: 2 μm and 0. 5 μm) were placed at the entrance of the air inlet to trap anthropogenic and natural particles (dust, sea salt …) in order to protect the instrumentation parts from particle deposition. The sampling cell of the PICARRO CRDS analyzers was automatically regulated in temperature (45 • C) and pressure (140 Torr).
During the quality control process, the CO 2 and CO datasets were filtered in temperature and pressure according to the ICOS procedure  (Hazan et al. , 2016) : only the data respecting the ICOS thresholds (T = 45 ± 0. 004 • C et P = 140 ± 0. 1 Torr) have been kept for our scientific analysis. Following PICARRO and ICOS procedures, a correction on the CO 2 concentration function of the atmospheric water vapor content was performed automatically by the ICOS database for the OHP and ERSA datasets  (Hazan et al. , 2016)  and by the analyzer PICARRO software for the CAV and SME ones  (Rella et al. , 2010). Furthermore, data related to local perturbations, such as human respiration during maintenance operations, were removed manually. After these two filtering processes, more than 95% of the raw datasets were validated.
The tank CRDS mean values were drawn function of the concentration of the tanks measured on the WMO scale at LSCE to calculate the calibration equation, which has then been applied to the air datasets. One further tank, called "target", was used to calculate the accuracy and precision of the datasets. The target gas was run every 47 h for 15 min. The mean concentration of the target tank was calculated on the last 5 min of these 15 min. The accuracy of the CRDS datasets was calculated as the difference between the average concentration of the target measured on site and the one attributed at LSCE on the WMO scale: it is less than 0. 1 ppm (resp. -0. 2 ppm) for CO 2 and less than 1 ppb for CO at OHP, ERSA and CAV (resp. SME). The precision of the CRDS datasets, reported as the standard deviation of the onsite target average concentration, is less than 0.
1 ppm for CO 2 and less than 11. 5 ppb for CO at the four sites. The CO 2 growth rate values at OHP and ERSA have been calculated using the data comprised between the first and the third quartiles of the datasets, as in  Vermeulen et al. (2011). The mean annual values of each subset have been computed. Since there are only 3 full years of data common to  OHP and ERSA (2015 , 2016 , 2017) , it was not much relevant to perform a linear regression. The growthrate of CO 2 at each site was rather estimated by substracting the 2016 subset mean to the 2015 subset one and the 2017 subset mean to the 2016 subset one; then, the average and standard deviation of both means were calculated and provided as the growth rate estimate and the growth rate variability at the site, respectively.
Wind datasets, boundary layer height and backtrajectories For each station, datasets of wind speed (ws), wind direction (wd) and atmospheric boundary layer height (ABLH) were extracted from 1 July 2016 to 31 January 2018 corresponding to the period when the 4 sites produced data almost all the time (see Table  2 ), from the regional WRF model (Weather Research and Forecasting) version 3. 7 released on 17 April 2015. As there were no validated ABLH WRF data available for the ERSA site on this period for the months of January to March, we extracted additional data from 1 January 2016 to 30 June 2016 to get a full seasonal cycle of the ABLH at all sites (section 3. 3). Our domain covers the SUD-PACA region and the North-East Mediterranean Sea (43. 78 • N, 41. 27 • S, 9. 62 • E, 2. 90 • E). The model framework runs at a 1h and 2 km resolution and is forced by ECMWF meteorological fields (European Centre for Medium-Range Weather Forecasts: https://www.
ecmwf. int/fr) at a 3h and 9 km resolution. Regarding the land surface model, the default Noah LSM scheme is used  (Chen and Dudhia,79 2001; Tewari et al. , 2004). For atmospheric boundary layer physics, the Mellor-Yamada-Nakanishi-Niino Level 2. 5-level (MYNN2) TKE scheme was used together with the corresponding built-in urban canopy model and the ABLH was defined as the height at which the TKE falls below a critical value, as detailed in  Banks et al. (2016). For this study, the wind datasets were classified in five classes depending on wind speed inspired by the Beaufort scale (Table  3 ). Marseille and the SUD-PACA region are characterised by local meteorological features, mostly due to their uneven topography and their proximity to the Mediterranean Sea. Fig. 3  shows the mean wind roses at each site, computed on the 2016-2018 period from our WRF/ ECMWF modelling framework wind fields.
, 2005)  and is therefore rather included in the calm-light breeze class. These conclusions are in agreement with the analysis of our dataset: in Summer, the "medium breeze" class in the SW sector at CAV represents 24% of winds during daytime but only 14% at nighttime; the same wind class in the NE sector is less frequent than in the SW sector, but as expected more frequent at nighttime (5%) than during daytime (3%). Similar results are obtained for the SME site. Regarding the light breeze class, the SW sector represents, at CAV and SME respectively, 24% and 15% of the data during daytime vs 14% and 10% during nighttime, while the NE sector accounts for 3% at both sites during daytime vs 5% and 6% during nighttime. To assess the spatial representativity of each site, mean airmass backtrajectories were computed for each season between 1 July 2016 and 23 February 2018 using the NOAA HYSPLIT model  (Stein et al.
Growth rate and distribution of atmospheric CO 2 at the four sites Fig. 4  shows the timeseries of hourly atmospheric CO 2 concentrations collected at the four sites for different periods, the longest being from 25 April 2013 to 5 February 2018. In this section, the 10 m AGL dataset is used for OHP. The observation period is not long enough to determine a trend. However, the two longer series show an annually average CO 2 increase of 2. 2 ± 0. 8 ppm/year over the 3 full years covered by the OHP dataset and 2. 9 ± 0. 2 ppm/year at ERSA over the same period. These values obtained at OHP and at ERSA are a bit lower and slightly higher respectively to the northern hemisphere mean growth rate observed over the same period at the Mauna Loa ESRL/ Fig. 3. Wind roses at the four sites on the 1 July 2016-31 January 2018 period, extracted from the WRF model data forced by ECMWF wind fields (see text).
The wind speed scale is given accordingly to the wind classes defined in Table  3. NOAA reference site (2. 45 ± 0. 55 ppm/yr, with an uncertainty of 0. 11 ppm/yr); https://www. esrl. noaa. gov/gmd/ccgg/trends/gr. html). The OHP and ERSA datasets are influenced by different local, regional and remote emissions (see section 3. 4. 2) which can explain these differences. In the case of OHP,  Lelandais et al. (2022)  showed that about 16% of the data collected at this site are contaminated by local to remote sources, mostly traffic and residential heating. A deeper analysis of the annual CO 2 growth rate variability at OHP and its comparison with the one monitored at Mauna Loa can be found in  Lelandais et al. (2022). Table  4  gives some descriptive statistics of atmospheric CO 2 at each site.
Both urban sites are characterized by higher CO 2 third quartiles and interquartiles that at the background sites as they are closer to the places where urban emissions outcome form, especially CAV. The Yule-Kendall index (Table  4 ), which provides information on the skewness of the datasets, is positive at CAV (almost equals to 0. 5) which is consistent with the influence of local urban sources that generate higher CO 2 concentrations at that station. At SME, OHP and ERSA, the Yule-Kendall index is slightly negative but most of all close to zero. Atmospheric carbon monoxide (CO) is a tracer of combustion often used to better identify the impact of anthropogenic CO 2 sources on atmospheric CO 2 (e. g. Ammoura et al. , 2016; Turnbull et al. , 2015; Lopez et al. , 2013). The CO timeseries show similar temporal features to the CO 2 ones, with frequent and rapid variations in Marseille.
The urban timeseries are characterized by numerous concentration spikes with enhancements up to 750 ppb of CO at SME and up to 100 ppm of CO 2 at SME and CAV above the concentration levels measured at the OHP and ERSA background sites (Fig. 4 ). Unfortunately, CAV was not equipped with any CO instrumentation (Table  2 ). According to Table  2 , on the period common to all sites, SME is characterized by the highest CO concentration mean and variability (161. 5 ± 87. 4 ppb), the highest median (136. 8 ppb) and the highest CO maximum (885. 3 ppb), which is explained by the proximity of this site to fresh urban combustion sources (traffic, heating). The interquartile is also the highest at SME as expected from this proximity, as is the Yule Kendall index (2. 245) which shows the influence of high CO concentration values on the station dataset.
Before averaging, corrections were applied on the datasets, consisting of subtracting the annual rate of increase observed at each site and interpolated by month. The datasets have also been deseasonalized by applying the following method, citing Xueref-Remy et al. (  2018 ): "(1) computing the annual mean of the dataset; (2) computing the monthly seasonal index for each month by calculating the ratio between the monthly mean and the annual mean of the dataset; (3) interpolating the monthly seasonal indexes at an hourly scale over the full period of study; and (4) dividing the CO 2 hourly dataset by the hourly seasonal index". A mean daily gradient of several ppm between the sites that increases with their exposure to anthropogenic emissions can be observed, reaching about 25 ppm between ERSA and CAV.
-Seasonal variations The CO 2 seasonal cycles are presented in Fig. 7a  for the four observation sites. The data used to calculate these cycles are based on the hourly dataset detrended from the annual increase, then averaged by month. Let us recall that due to several instrumental issues, there is no data at SME for the months of March to July. Several factors are known to control the atmospheric CO 2 seasonal cycle (e. g. Xueref-Remy et al. , 2018) , which is mostly variable over the continents: (1) the proximity of the site to vegetation fluxes and the seasonal activity of the biosphere, (2) the proximity of the site and the variability of anthropogenic emissions; (3) the seasonal cycle of the ABLH at the site; and (4) meteorological and especially wind variations. The impact of these latter is analyzed in Section 3.
5 ppm in August to 21. 5 ppm in January) likely from the impact of anthropogenic emissions as indicated by the larger CO 2 variability in the cold months. Thus, the annual mean, the monthly means, the amplitude and the variability of the seasonal cycles are controlled partly by their proximity to biospheric fluxes and to urban emissions. Such seasonal features have also been observed in previous urban studies (e. g. Turnbull et al. , 2015; Xueref-Remy et al. , 2018). The impact of the ABLH seasonal dynamics is addressed further below. In order to better constrain the role played by natural fluxes and anthropogenic emissions on atmospheric CO 2 seasonal variability, the mean diurnal cycles of CO 2 are shown by season (Fig. 7e-h ), calculated from the datasets detrended from the annual increase observed at each site and then seasonally normalized (cf section 3.
The quantification of the urban dome/plume depends on the background site location, which should be, when the best suited, located at the rural/peri-urban border upwind of the studied urban area in order to avoid the influence of CO 2 fluxes between the city and the background location, for example from the biosphere (e. g. Turnbull et al. , 2018; Xueref-Remy et al. , 2018). Furthermore, several background sites can be needed to cover the direction of the different dominant winds, as airmasses can be influenced by different remote fluxes before their arrival upwind of the studied city, depending on their trajectory, possibly leading to noticeable differences of several ppm in CO 2 background levels (e. g. Turnbull et al. , 2018; Xueref-Remy et al. , 2018; Karion et al. , 2021). In this study, although we did not have the funding to install such dedicated urban background sites, our datasets allow us to infer the strength and behavior of the urban signal depending on wind speed relatively to regional continental and marine background concentrations at OHP and ERSA, respectively.
This information is especially important to provide for designing efficient intensive campaigns dedicated to inferring CO 2 emission sources from any atmospheric CO 2 urban dome/plume with the use of isotopes (e. g. Lopez et al. , 2013)  or CO 2 co-emitted species such as Volatile Organic Compounds (e. g. Ammoura et al. , 2016). The CO 2 mean concentration for each wind class and each site is given in Table  5  (calculated from the detrended datasets, see previous section). At CAV and SME, the mean concentration decreases from 423. 0 ppm to 415. 6 ppm-402. 7 ppm and 403. 7 ppm respectively when wind speed increases from 0 to 2 m s -1 to >17 m s -1 , with the strongest CO 2 decrease for wind speed higher than 6 m s -1. At OHP, the mean concentration for wind speeds <2 m s -1 (408. 2 ppm) is lower of ~15 ppm and ~7 ppm than at CAV and SME, respectively, but it does not decrease much when wind speed increases and even increases for strong and very strong winds, converging towards the concentration observed at the urban sites and then reaching higher concentrations for the largest wind speeds.
Local, regional and remote contributions to atmospheric CO 2 at the four sites To better assess how wind regimes control the CO 2 variability, Fig. 9a  to Fig. 9d  show the hourly mean concentrations of CO 2 in function of wind speed and direction by bins of 2 m s -1 and 10 • at the four sites (so-called « CO 2 roses »), the associated standard deviation (Fig. 9a-9d' ) and the associated frequency of each bin (Fig. 9a '' to Fig. 9d'' ). To avoid biases due to CO 2 seasonal variability (Section 3. 3), the CO 2 datasets have first been seasonally adjusted according to the procedure explained in  Xueref-Remy et al. (2018). Furthermore, to better assess the role of anthropogenic sources, Fig. 10  presents the CO roses obtained at ERSA, OHP and SME, and the NO x rose at CAV, at the same bin resolution as the CO 2 roses.
Influence of a local meteorological feature in Marseille: NE land/SW sea breezes In the Marseille city, the CO 2 roses (Fig. 9 ) show that the NE sector is most often characterised by higher concentrations than the SW sector. These wind sectors are characteristic of well established land/sea breezes, for wind speed below 6 m s -1. Breeze regimes have been studied within the ESCOMPTE program (e. g. Bastin et al. , 2005; Drobinski et al. , 2007) , as well as their impact on the variability of some pollutants, but never on atmospheric CO 2 and only for the Summer season. Our meteorological dataset indicates that breezes accounted for a few percents of winds from the SW sector (sea breezes) and from the NE sector (land breezes) during the period of study. To deeper infer the role of NE/SW breezes on atmospheric CO 2 in Marseille, we analyse the normalized and seasonally adjusted mean diurnal cycles of CO 2 by season on the SME site, in the conditions of NE land/SW sea breeze processes for wind speed comprised between 2 and 6 m s -1 (Fig.
Acknowledgments This work was financed by the  LABEX OT-MED Aix-Marseille Carbon Pilot Study (AMC)  project and the  COoL-AMmetropolis  project funded by the  Agence National de la Recherche  (grant number  ANR-19-CE03-0008 ) and supported through the  Service National d'Observation ICOS-RAMCES of CNRS  and the ATMOSUD regional air quality agency. The authors thank the  SIP (Service Informatique PYTHEAS)  for their technical support. We acknowledge  M. Ramonet ,  L. Hazan  and  D. Combaz  from the  ICOS-RAMCES network  for providing us the ERSA data and for technical support at OHP. The authors thank the ATMOSUD technical team and especially  L. Lanzi  for their technical support at the  CAV (Marseille Longchamp ) station. We are grateful to  M. Fornier  and  M. Lafond  for their technical help at the  Station Marine d'Endoume (SME).
Data collection The demographic data (age, gender, BMI, etiologies) and a pre-operative harris hip score (HHS) were collected. Postoperative clinical assessment was evaluated using the HHS and by collecting complications by an independent observer. The results were classified as: poor (HHS < 70), fair (HHS = 70-79), good (HHS = 80-89), and excellent (HHS = 90-100)  [28]. On the postoperative pelvic AP views, the measurements of implant positioning were performed by an independent observer. Cup abduction was the angle between the cup axis and the parallel between the inter-teardrop line. The anteversion was calculated using Widmer's method  [29]. The cup positioning was considered in the safe zone if abduction was between 30°and 50°and anteversion between 10°and 30°  [30]. The cup overhang was evaluated on a Lequesne profile view.
Data and Methods The Chl-a dataset used here consists of the Level-3 "GlobColour daily MODIS product," version 2018. 4, which is an estimate of the near-surface Chl-a concentration in units of mg/m 3 obtained from MODIS-Aqua data using a semi-analytical model (the Garver-Siegel-Maritorena (GSM) product;  Maritorena and Siegel 2005; Maritorena et al. 2010). The GSM product is based on representing the spectrum of outgoing radiation with contributions due to Chl-a, dissolved organic matter, and suspended particular matter, and estimating these quantities from a weigthed average of spectral data. The product provides daily estimates; although, the constituent measurements may occassionally fall outside of the calendar date of the product. For the purpose of harmonic analysis, the measurements are treated as if they occur at 12:00 GMT of product date.
Code and data availability. The Level-3 GlobColour product may be obtained from https://oceancolor. gsfc. nasa. gov/. Julia-language scripts for harmonic analysis of these data are available from the software repository at https://ingria. ceoas. oregonstate. edu/fossil/CHL. Author contributions. Zaron drafted the manuscript, and implemented and performed the harmonic analysis. Capuano provided the Chl-a dataset and contributed to revising the manuscript. Koch-Larrouy conceived of the research, guided the selection of significant geographic regions for study, and contributed to revising the manuscript.
, 2018). Calibration of the system was performed using a signal generator and using the sensitivity declared by the manufacturer. Acoustic data were automatically preprocessed in situ by the valvometer (electronic card) in the frequency range [10-700 Hz]. Data were processed in R (R core Team 2022) using custom scripts and the seewave library A1 B 1 A2 B 2 FIGURE 1 Schematic diagrams of the experimental setup and tanks, each tank containing 16 oysters equipped for HFNI valvometry. (A1-2) in the standard laboratory condition with human presence and running analysis devices; (B1-2), in an unoccupied and quiet room, without human presence around. In the diagrams, (A1, B1) provide an overview of the setups, while (A2, B2) offer detailed views of the respective water treatment systems. The numbered components are as follows: 1, standard glass aquarium; 2, aquarium bubbler; 3, air stone; 4, empty plastic bottle used to absorb the vibrations of the air pump; 5, water pump; 6, hydrophone; 7, camera; 8, tiled lab bench; 9, floor tiling; 10, coolbox; 11, wooden board; 12, tennis ball; 13, sand box; 14; thick carpet; 15, airlift; 16, rubber band.
(Sueur, 2018). The sampling rate was 4096 Hz. A fast Fourier transform (fft) was applied every 500 ms during recording, resulting in a step size of 2 Hz. For the band between 10 Hz and 90 Hz, the valvometer adds 5 fft values to give a single output frequency, while for the bands between 100 Hz and 700 Hz, 50 fft values are added. Then, an hourly average of the sound level was calculated in the frequency range of 10-700 Hz to obtain the evolution of the hourly average sound level during the whole experiment. The light intensity was measured by using Lux Lightmeter Pro software made for the iPad. The measurements were repeated for 9 days spread over the experimental period at two times, 07:40-10:40 and 01:45-02:40. On working days, in the laboratory room, the light was turned on at approximately 8 a.
Behavioral analysis by high-frequency noninvasive valvometry Two HFNI valvometers were used to simultaneously record the valve activity of the two groups of 16 oysters. The method is based on the measurement of voltage variations associated with the electromagnetic field produced between two lightweight electromagnets designed to minimize bivalve disturbance. An electromagnetic current between the electrodes was generated, allowing measurement of the amplitude of the valve aperture or valve gap. The signal was recorded using a laboratory-made acquisition card every 0. 1 s. The data were automatically and continuously transmitted daily to a data processing center in Bordeaux using an internet network  (Andrade et al. , 2016). They were processed automatically on a daily basis. Each collected value was associated with a valve opening amplitude, expressed as a percentage.
Other examples of application are reviewed in  Andrade et al. (2016). To study biological rhythms, double-plotted actograms were made, starting at 00:00 h. Each line is a barcode that summarizes the hourly activity of the groups of oysters studied for 2 days. By convention, each 2-day period was first represented from 00 h to 48 h (Day 1 + Day 2). Day 2 was then repeated on the next line from 0 to 24 h. The dark blue and pale blue sections of each line represent hourly behavioral values below and above the mean behavior of the day. Statistical chronobiological analyses were performed using R software and cosinor package. Daily valve-opening amplitude (expressed in %) was analyzed individually and then presented as box plots. The values of 0% and 100% correspond to the minimum and maximum opening amplitude recorded over the previous 6 rolling days, respectively.
0005). It was followed by a short transition period from day 28 to 32 (period 3) followed by stopping the expansion for 16 days (period 4; y = 0. 45x + 1243; R 2 = 0. 038; p = 0. 61). During period 4, the changes in surface area were not significantly different (p = 0. 83). Then, in period 5, the dispersal of the oysters resumed until Day 74 (y = 9. 8x + 845; R 2 = 0. 93; p < 5,1 ˑ 10 -6 ), and the change in surface area was again significantly different (p < 5. 1 ˑ 10 -6 ). As a whole, the above dataset showed that, depending on the environment, activity levels were used for opposite objectives, scattering or grouping. We now switch to a behavioral analysis at the individual level by analyzing the data obtained by quantifying the valve activity. Displacements were observed to be more intense during nighttime demonstrating their nocturnal habits and the influence of light in their behavior.