dataesr/scientific-paragraphs-categorization huggingface dataset:
Assumption of model case Considering the effects of housing asset value deflation on the elderly, we adopted a case study method to clarify the effects of deflation by commute time. To conduct the case study, we needed the following data so that we could divide their effects by commute time. Data included income, expenditures, savings, and housing prices in elderly households. However, these statistical data are not measured in terms of commute time. Surveys have provided these actual statistics for the Tokyo metropolitan area, and therefore, we used the average elderly household in Tokyo as a model household. Assuming that the weight of housing expenditure is roughly constant, houses in central Tokyo will be smaller than those in the suburbs, which will be comparatively larger with gardens.
Statistical Analysis Data analysis was performed using one-way analysis of variance (ANOVA) and least significance difference (LSD) at p < 0. 05 to test the differences among the nutritional value of the blends. The design of the experiment for blends was achieved using completely randomized design (CRD). Mean separation was calculated using Duncan's new multiple range test using SPSS version 23. 0 computer software  [28]. The experiments were performed in duplicate.
A C T EU member countries and the UK are currently installing numerous offshore windfarms (OWFs) in the Baltic and North Seas to achieve decarbonization of their energy systems. OWFs may have adverse effects on birds; however, estimates of collision risks and barrier effects for migratory species are notably lacking, but are essential to inform marine spatial planning. We therefore compiled an international dataset consisting of 259 migration tracks for 143 Global Positioning System-tagged Eurasian curlews (Numenius arquata arquata) from seven European countries recorded over 6 years, to assess individual response behaviors when approaching OWFs in the North and Baltic Seas at two different spatial scales (i. e. up to 3. 5 km and up to 30 km distance). Generalized additive mixed models revealed a significant small-scale increase in flight altitudes, which was strongest at 0-500 m from the OWF and which was more pronounced during autumn than during spring, due to higher proportions of time spent migrating at rotor level.
, 2006). Although radar recordings have provided some important insights into the behavioral responses of migrating birds at single wind farms  (Masden et al. , 2009 (Masden et al. , , 2012;; Fijn et al. , 2015; Skov et al. , 2018) , they cannot usually record species-specific data and importantly cannot track the bird's entire migration route across a given sea area (which might include multiple OWFs). High-resolution Global Positioning System (GPS) telemetry now offers a valid tool for recording individual data at high temporal and spatial resolutions, thus enabling individual responses and avoidance/attraction behaviors to be recorded. This approach has been applied successfully in previous studies of foraging seabirds  (Hull and Muir, 2013; Fijn and Gyimesi, 2018; Schaub et al. , 2020; Vanermen et al.
The overall aim of this study was to quantify the potential horizontal and vertical responses of curlews approaching OWFs separately for their spring and autumn migrations as behavior and spatio-temporal migration patterns might differ between seasons  (Hüppop et al. , 2006). We also aimed to quantify the distance from OWFs at which potential behavioral responses occurred. This is an important prerequisite for modelling energetic constraints for migrating birds related to the barrier effects of vertical structures at sea  (Fox et al. , 2006). Moreover, we aimed to quantify the proportion of birds that showed avoidance behavior versus individuals that crossed the OWFs (and were consequently exposed to higher collision risks). Finally, the large high-resolution, long-term GPS dataset used in this study enabled us to explicitly assess the temporal and spatial patterns (i.
Study area and curlew tagging We compiled an international set of curlew tracking data ranging from the southernmost wintering grounds in the southern Iberian Peninsula to the Arctic breeding grounds in western Russia (Fig. S1 ). Crossings of the North and Baltic Seas by curlews during spring and autumn migrations were recorded in an area south of 66 • 0 ′ 0 ′′ N, north of 50 • 0 ′ 0 ′′ N, east of 02 • 0 ′ 0 ′′ W, and west of 30 • 0 ′ 0 ′′ E (Fig. 1 ). All birds were equipped with solar-powered GPS Global System for Mobile Communications data loggers (Ornitela, Lithuania). The entire dataset contained 259 migration tracks recorded between 2017 and 2022, comprising an offshore portion (90 during spring and 169 during autumn migration), for 143 individuals equipped in seven countries (Germany: n = 103, France: n = 16, UK: n = 10, Estonia: n = 6; Finland: n = 6, the Netherlands: n = 1, Russia: n = 1) during either the breeding or wintering period (see Table  1  for an overview of the number of tracks and individuals per tagging location and season; note that many individuals recorded data in consecutive years).
The 143 tagged birds included 136 adults and seven juveniles as well as 78 males and 65 females. The devices weighed 10 g (n = 115), 15 g (n = 22), and 20 g (n = 6), respectively. Different types of data loggers were used as lighter versions became available during the course of the study. None of the tags was >3. 5% of the bird's body mass  (Phillips et al. , 2003). The data loggers recorded geographical position, time (UTC), speed (m/s), and flight altitude (m above sea level). Data were stored in the online portal Movebank  (Kays et al. , 2021). Curlews were caught in their wintering grounds using mist nets or cannon nets, or during the breeding period while incubating using walkin-traps or clap nets. All devices deployed in Germany, Estonia, and Russia were fitted with breast harnesses  (Guillaumet et al.
Compilation of OWF data Data on the statuses of OWFs (as of May 2022) in the North and Baltic Seas were obtained from  Offshore, 2021, Helcom -Baltic Marine Environment Protection Commission and Helsinki Commission (2021) , and  Zhang et al. (2021) , as well as from multiple sources provided by the different North and Baltic Sea countries (Table  S1 ). On the basis of single turbines, we used the tool "Concave Hull (k-nearest neighbor)" in QGIS  (version 3. 2. 4. 0; QGIS Development Team, 2022)  to convert the area of each windfarm to polygons, using the positions of the turbines at the edge of the respective windfarm. We also added a buffer of 85 m (i. e. maximum rotor radius of all wind turbines in the dataset), because the rotors might extend the border of the windfarm to a maximum of their radius.
Data preparation All maps were created using QGIS. All statistical analyses were conducted using R (version 4. 2. 2; R Development Core Team, 2022). Each GPS track was visualized in the GIS and all fixes that were not recorded in flight (i. e. positions at stopovers, breeding or wintering grounds that could be identified by flight speed and low distances between consecutive fixes) were removed. Flight speeds were recorded using doppler shift which is known to be a very precise measurement enabling easy classification between stop-overs and in-flight positions. The remaining in-flight positions were assigned as recorded over sea or land using the R package spData  (Bivand et al. , 2021)  and R package sf  (Pebesma, 2018). Furthermore, each position was classified as "day" (i. e. from calculated morning to evening civil twilight) or "night" (i.
Responses in the vertical plane Wind farm avoidance reactions by birds take place at different spatial scales (i. e. micro-, meso-and macro-avoidance;  May, 2015; Skov et al. , 2018). While our tagging data are not appropriate to analyze the immediate risk reaction (i. e. micro-avoidance), we focus on meso-and macro-avoidance. Therefore, we analyzed potential avoidance responses in the vertical plane on two different spatial scales: (1) within 1. 5 km surrounding the OWFs (subsequently referred to as small-scale approach) and (2) within 30 km surrounding the OWFs (subsequently referred to as large-scale approach). In the small-scale approach, the relative intensity of use at distances of 0-1 km was compared with that at 1-1. 5 km, while for the large-scale approach, we compared the relative intensities of use at 0-15 km and 15-30 km.
, 2022). We included the variable sea/land as a binary variable as curlews are known to abruptly change their flight altitudes when crossing the coastline  (Schwemmer et al. , 2022). To avoid problems caused by negative values of flight altitudes during regression analyses (in the context of negative-binomial distributed altitude data), all flight altitude data were increased by a constant to make all altitude values ≥ 0. Because regression techniques only evaluate relative changes, this shift affected the intercept but not the regression coefficients related to the investigated predictors. We analyzed the flight altitudes of curlews approaching OWFs using generalized additive mixed models (GAMMs;  Hastie and Tibshirani, 1990; Zuur et al. , 2009; Wood, 2017)  using the R package mgcv  (Wood, 2022).
g. , the area of each distance class quadratically increases with the distance to the OWF, which is even more complicated in a scenario with multiple turbines). More details are given in  Mercker et al. (2021b). Given that data recorded at 1 s intervals comprised too few individuals close to the OWFs, we only included individuals with data that could be regularized to 60 s and 300 s intervals for small-scale, and 300 s and 1500 s for large-scale analyses. The interval size was thus adapted to the spatial scale of the considered problem; too-small intervals (such as 1 s data) would lead to an underestimation of effects (since available steps will fall into similar OWF-distance classesc. f. , below), whereas too-large intervals would lead to sparse data and strong stochasticity. Regarding iSSM predictors, we used the logarithm of the spatial distance to the previous tracking point log(dx) (as a proxy proportional to flight speed) as well as the cosine of the turning angle cos(ta)  (Avgar et al.
, 2016). These two movement-dependent predictors integrate the possibility to allow the movement characteristics to feed back on habitat selection, extending "step selection models" to "integrated step selection models". Indeed, a simulation-based study reveals that this further increases the statistical power respectively minimizes type I error rates  (Mercker et al. , 2021b). Data regularization and generation of available steps (n = 10 per tracking point, were carried out based on the R-package amt using the issf function  (Signer et al. , 2019). To consider OWF avoidance, the binomial predictor Dist OWF was also included, defining a ring/belt with X ± 50 m distance (small-scale) or X ± 250 m (large-scale) from the nearest OWF, and contrasting the relative selection strength (i. (2018).
Avoidance responses in the horizontal plane In the small-scale analysis, all the investigated combinations of methods (i. e. iSSM and INLA-based mixed additive iSSM analyses) and datasets (i. e. 60 s and 300 s GPS fix schedules) showed a generally consistent picture, with the relative intensity of use becoming <1. 0 between 0 m and approximately 300 m from the OWFs (indicating a significant small-scale horizontal avoidance behavior). This was followed by values > 1 at approximately 450 m distance, indicating that birds were locally concentrated by aiming to circumvent the turbines (Fig. 6 ). The only difference between the two methods was an increase in selection strength in the 300 s dataset compared with a decrease in the 60 s dataset. Notably, the relative selection strength decreased continuously down to about 30% from approximately 450 m onwards with decreasing distance to the OWFs (Fig.
Avoidance responses in the horizontal plane Horizontal avoidance of wind farms by different bird species has previously been demonstrated by non-species-specific radar studies  (Masden et al. , 2009 (Masden et al. , , 2012;; Fijn et al. , 2015; Skov et al. , 2018) , for foraging raptors at terrestrial sites  (Hull and Muir, 2013; Schaub et al. , 2019) , and for foraging seabirds (e. g. Fijn and Gyimesi, 2018; Vanermen et al. , 2020; Johnston et al. , 2022). The current study demonstrated similar effects for a long-distance migrant during active migration bouts. The small-scale iSSM approach using different dataset and method combinations consistently revealed a higher density of curlews at about 450 m from OWFs, with a strong peak in selection length reflecting evasion at this distance class followed by avoidance at 0-350 m.
However, we observed two cases of curlews that abruptly changed their flight courses within or in the vicinity of OWFs, resulting in a flight path of non-directed circles (Fig. S4 ), but whether this behavior was caused by the presence of the OWF is unknown. Similar to the avoidance response in the vertical plane, we found no clear evidence for horizontal avoidance patterns of curlews on larger spatial scales. The decay in selection strength in the direct surroundings of the wind farm observed in both datasets (i. e. data thinned out to 300 s and 1,500 s) again indicates a small-scale horizontal avoidance response close to the OWFs. The approach using 1,500 s data revealed a reduced selection strength of about 30% within the closest (0-500 m) radius of the OWFs, which matched well with the orders of magnitude estimated in the small-scale analysis, where both strong decays and local increases in birds due to evasion of OWFs occurred within the 500 m radius (see above).
Conclusions The current study demonstrates the existence of intense small-scale horizontal and vertical avoidance behaviors of OWFs in a migratory species using a large and unique international tracking dataset. Our findings clearly show that the majority of the curlew population avoids OWFs both, during the night and day. However, as an important result for marine spatial planning we also found that a considerable proportion of the curlew population might cross OWFs at rotor heights and that the overall proportion of curlews migrating at risk is far higher during autumn than during spring migration. Thus, temporal curtailments of OWFs during periods of most intensive migration and within areas of highest bird migration are a likely mitigation measure for conservation management. Furthermore, the extensive expansion plans for OWFs in the North and Baltic Seas  (Rusu, 2020; Offshore, 2021)  are likely to increase the overall risk for migrating bird species such as curlews.
Our long-term dataset offered the option to check for repeatability of behavioral reactions of the same individuals during consecutive approaches to OWFs. However, we were not able to detect any consistencies which might indicate the lack of habituation to OWF. Finally, our data clearly show that curlews react strongly to OWFs by altering their flight tracks vertically or horizontally, thus highlighting the significant barrier effects of OWFs. The current and future increases in OWFs in the Baltic and North Seas means that it is necessary to quantify the energetic costs to birds to assess the cumulative barrier effect of OWFs, particularly for long-distance migrants. Using an extended set of tagging data in the future, will allow to draw conclusions on behavioral reactions to OWFs during different weather situations which is needed to assess the options for temporal turbine curtailments during phases of severe weather when avoidance reactions of birds might be reduced.
Acknowledgements Catching, handling and tagging of curlews complied with European laws. GPS-tagging of curlews in Germany was approved by the  Ministerium für Energiewende, Klimaschutz ,  Umwelt und Natur of the federal state of Schleswig-Holstein  (file numbers V  312-7224. 121-37 ( 42-3/13 )and V  241-35852/2017 ( 88-7/17) ) as well as by the  Lower Saxony State Office for Consumer Protection and Food Safety (LAVES)  (file number  33-19-42502-04-17/2699 ). Approval for tagging Curlews (NW Germany) was obtained from  LAVES, Senator for Labour, Women's Affairs, Health, Youth and Social Affairs in Bremen  (both AZ 33. 19-42502-04-20/3373 , dated March 13, 2020, May  06 ,  2020 ) and the  State Agency for Nature, Environment and Consumer Protection North Rhine-Westphalia (LANUV ,  AZ 81. April 02 ,  2020.
Similarly, at an urban background site in Barcelona (Spain), both Oxygenated OA (OOA) fractions dominate the OA total mass over the two one-year study periods  (Via et al. , 2021) , increased by air masses coming from inland Europe and the Mediterranean, especially the more oxidized OOA. Whereas the PMF analysis applied on multiannual ACSM datasets hitherto yielded important results, notably allowing the identification of primary versus secondary OA (POA and SOA, respectively) sources across multiple seasons, the inherent limitation of fixed factors on traditional PMF method forces longer datasets to be split, typically into three months sub-sets (e. g. Canonaco et al. , 2015; Zhang et al. , 2019). This method has the drawback of requiring multiple independent PMF analyses, being labor-intensive, and prone to discrepancies between individual runs.
Recently a rolling PMF method has been developed  (Canonaco et al. , 2021; Parworth et al. , 2015; Chen et al. , 2022) , allowing the study of long-term datasets in a single iteration via a sliding time window, thus enabling inter and intra-seasonal variations of OA source profiles. The recently developed method has already been applied at several sites across Europe, although limited to one-year datasets  (Atabakhsh et al. , 2023; Canonaco et al. , 2021; Chazeau et al. , 2021; Chen et al. , 2021; Tobler et al. , 2021; Zografou et al. , 2022). In this context, we have studied PM 1 composition and OA sources since 2016 at a suburban site in Lille, known as the ATOLL (ATmospheric Observations in liLLe) platform. Recent studies at this site have shown a dominating effect of ammonium nitrate and organics on the aerosol optical properties  (Velazquez-Garcia et al.
, 2023) , as well as intense gas-to-particle conversion during spring and summer  (Crumeyrolle et al. , 2023). Thus, the objectives of this study are i) to apply for the first time the rolling PMF method on a multiannual dataset of organic aerosol at this site, and ii) to better understand PM 1 sources at the ATOLL platform. The results aim at better understanding aerosol sources via long-term analysis and quantify their contribution in the North-Western European region, therefore supporting the development of effective emission reduction strategies.
Wind analysis and back-trajectories for cluster calculations Pollution roses and the diel plots by wind sectors are plotted considering the median concentrations using MATLAB. A wind speed threshold above 0. 3 m s -1 was imposed for these plots and analyzed over 22. 5 • wind sectors. We used the back-trajectories within the NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT; Draxler, 1999) model using 1 • × 1 • resolution Global Data Assimilation System (GDAS) files. The back-trajectories were ending at ATOLL from 2016 to 2019 for an arrival altitude of half the planetary boundary layer height at 1-h intervals, 72 h back in time. 4750 back-trajectories were clustered every 6 h using the HYSPLIT model. The method used for clustering analysis is based on the relative variation of the total spatial variance (TSV).
If a priori source chemical information is known, the a-value approach allows overcoming this issue by constraining the expected factor profiles or time series using ME-2 according to Eq. (3) and Eq. (  4 ): f solution = f reference (1 ± a), (3) g solution = g reference (1 ± a), (4) This implies that an a-value of 0. 1 allows each variable of a mass spectrum to vary by ±10% compared to the reference spectrum. Furthermore, to identify stable PMF solutions and estimate the statistical uncertainty of the analysis, the bootstrap technique is used. It allows a resampling of the original data, i. e. making sub-samples and checking that with these samples we can reach the same solutions and the same contributions of the factors as previously identified  (Efron, 1979). To perform a multi-annual analysis, standard PMF is first performed by seasons in a reference year to (i) determine the number of expected factors throughout the database and (ii) retrieve reference spectra.
Afterward, a rolling PMF is applied combining spectra constraints on the primary factors and bootstrapping. The rolling method consists of breaking down the database into shorter periods (e. g. one-month windows, shifting by seven days), having the advantage of allowing some variability in the factor profiles throughout the observation period, across seasons and years. The standard PMF analysis was performed on the reference year from October 2016 to August 2017, broken down by seasons (Autumn -September, October, November, SON; Winter -December, January, February, DJF; Spring -March, April, May, MAM; and Summer -June, July, August, JJA). This analysis allowed identifying four factors at ATOLL, namely HOA (Hydrocarbon-like OA) related to road traffic, BBOA (Biomass Burning OA) related to biomass combustion, and two OOA factors, including a Less-Oxidized OOA (LO-OOA) and a More-Oxidized one (MO-OOA).
At the end of this standard analysis, the primary factor profiles, HOA and BBOA, were extracted from the winter bootstrap PMF solution to be used as a priori information for the rolling PMF analysis (further details are given in the SI, section S2). An initial one-year (2016-2017) rolling PMF analysis has been performed using a 14-day window and one-day step, which was integrated in a multi-site rolling PMF study  (Chen et al. , 2022) , with a suggested criteria list and standardized protocol for this method. Based on the results of the one-year analysis, rolling PMF was then conducted for the full dataset (October 2016 through December 2020) presented here, using the same reference spectra for the primary factors (HOA and BBOA), however with 28-day windows and seven-day shifting for increased computational efficiency.
Section S3 of the SI shows comparable results of the rolling PMF time windows (14-day, shifting by one day vs. 28-day shifting by seven days). Bootstrap was performed with 50 PMF iterations of the rolling window, representing a total of 050 different PMF analyses for this four-year dataset. Using the selection criteria defined in Section S3, the best PMF solutions were chosen and then averaged. Rolling PMF was conducted using SoFi Pro software V8. 0. 6. 0 (Source Finder Professional, Datalystica Ltd. , Switzerland) with the ME-2 solver in the Igor Pro software environment (Igor Pro version 8. 04, Wave Metrics, Inc. , USA).
Conclusion Aerosol composition and sources in the newly established ATOLL site, near Lille in Northern France, are analyzed here from October 2016 to December 2020. During the studied period, PM 1 had a mean concentration of 10. 6 μg m -3 , composed of OA (41. 8%), NO 3 (28. 9%), NH 4 (12. 3%), SO 4 (8. 6%), and BC (8. 0%). For such a multi-annual dataset, source apportionment of organic aerosols has been performed using rolling PMF, allowing for a robust analysis through a clear set of criteria and constraints across seasons and years, in contrast to the season-byseason individual analysis method. A 28-day rolling PMF window shifted by one week over the four-year dataset yielded four factors, consisting of two primary OA, namely HOA (11. 8% of total OA) mainly related to traffic, BBOA (ranging from 8.
Acknowledgments The authors are grateful to the staff at LOA, in particular  Prof. P. Goloub ,  F. Auriol , and  R. De Filippi , for supporting the technical and logistical implementation of the instruments, and to  Dr. L. -H. Rivellini  ( now at the University of Toronto ) for her early involvement. We thank  T. Podvin  ( LOA ) for providing the weather data at ATOLL, and the air quality monitoring network  Atmo Hauts-de-France  for providing PM 2. 5 concentrations at the Lille Fives monitoring station.