dataesr/scientific-paragraphs-categorization huggingface dataset:
Gender Across all datasets, female speakers have significantly higher mean pitch levels than male speakers (read: 63%, spont. : 62% higher; Table  6 ). Additionally, female speakers consistently show higher mean values for the pitch parameters expressing variability, particularly for (variability in) rate of pitch change but somewhat less for pitch range and dynamism and especially tone rate. All else being equal, female speakers have a higher rate of change (read: 74%, spont. : 67% higher), more variability in this rate (read: 81%, spont. : 78% more variable), a wider range (read: 16%, spont. : 42% wider), more dynamic pitch (read: 18%, spont. : 15% more dynamic), and slightly higher tone rates (read: 7%, spont. : 7% higher).
7 Hz) and southern England (2. 5 Hz) to Grenada (1. 5 Hz) were significant (p <. 001). For variability in this rate, only the difference between Dominica (12. 9 Hz) and Grenada reached significance (8. 8 Hz, p <. 001). Trinidad has the highest tone rate (7. 9 tones/s); the other datasets mostly show lower and undifferentiated rates. The patterns in male spontaneous speech match those in read speech. Few significant differences were found for pitch range and dynamism. Grenada has a significantly lower rate of change (1 Hz) than all other datasets (p <. 05) except Dominica (1. 4 Hz). The same pattern exists for variability in rate of pitch change. With regard to tone rate, together with India (7. 6 tones/s), Trinidad (7. 2 tones/s) again ranks highest compared to the other datasets.
6 Hz) than all other datasets (Trinidad: 2. 7 Hz, southern England: 2. 4 Hz, Grenada: 2 Hz, Dominica: 1. 7 Hz, each at least at p <. 05). Trinidad shows a significantly higher rate of change than both Grenada and Dominica (each at p <. 001). The significant pairwise differences and the overall pattern for variability in rate of pitch change match those of the rate proper (India: 19. 7 Hz, Trinidad: 15. 1 Hz, southern England: 13. 8 Hz, Grenada: 11. 9 Hz, Dominica: 10. 4 Hz; see Figure  2 ). Little between-territory variation exists in terms of tone rate. After exploring variation in each pitch parameter on its own, we now move on to the analysis of potential covariation of the parameters, for which the PCA revealed a two-component solution (eigenvalues above Kaiser's criterion of 1). Together, both components explain 79.
F I G U R E 3 Cross-territorial covariation patterns in read and spontaneous speech across male and female speakers. Speaker-specific factor scores for the components extracted in the principal components analysis, micro-variability (C1) and macro-variability in pitch (C2), with point labels providing information on gender. Quadrant numbers shown [Colour figure can be viewed at wileyonlinelibrary. com] few between-territory differences exist among male speakers. Relatively independently of speaking style, most male speakers across all datasets are located in quadrant III and thus show low micro-and macro-variability in pitch. Some male speakers from southern England further form part of quadrant IV, suggesting that a greater number of male speakers in this territory have higher micro-variability in pitch.
DISCUSSION This study set out to provide a comparative phonetic analysis of pitch parameters in English language data from Dominica, Grenada, and Trinidad, drawing on a corpus of speech from 243 speakers. spontaneous speech recordings of students and teachers in secondary (and tertiary) education contexts across the three territories. Additionally, the study offered a comparison to standardized southern BrE and standardized IndE as well as an exploratory analysis of sociophonetic variation in prosody in the Caribbean datasets. The findings provide empirical insights on whether a wide pitch range, or more generally a high degree of variability in pitch, are characteristic features of English in the Caribbean compared to non-Caribbean Englishes (RQ1), as has been claimed based on anecdotal observations (for example,  Wells, 1982, pp.
573-575). These observations are not confirmed by our analysis, which did not indicate an overall wider pitch range or more variability in pitch in the Caribbean than the southern British or Indian data. Indeed, for none of the parameters expressing variability in pitch were there cross-territorial differences that would indicate a clear patterning of Caribbean vs. non-Caribbean datasets. Rather, the results suggest a high degree of between-territory prosodic differences. Across the parameters, the highest variability in pitch was generally found in the Indian and Trinidadian (and, to a somewhat lesser degree, southern British) data, in most cases without substantial differences between the former two. The observed similarities between the Indian and Trinidadian data might be linked to shared aspects in the intonational phonologies of both varieties and related former substrate influence  (Gooden & Drayton, 2017; Maxwell, 2014 ; see Section 2.
2). Dominican and Grenadian speakers tended to show smaller degrees of pitch variation, for instance with regard to pitch range or dynamism, but overall differences compared to Indian and Trinidadian (and southern British) speakers were in most cases only significant for rate of pitch change, variability in this rate, and, to a lesser degree, tone rate in spontaneous speech. The observed differences between the Trinidadian data, on the one hand, and the Dominican and Grenadian datasets, on the other (RQ2), might be related to the popular stereotype of TrinE having a distinct 'sing-song' prosody among varieties of English in the region  (Ferreira & Drayton, forthcoming, p. 30; Youssef & James, 2008, p. 334). The between-territory differences concerning parameters expressing variation in pitch may potentially be explained by understanding them as phonetic consequences of different underlying intonational phonologies in English spoken in the three islands: Accentual phrasing and frequent alternation between L* pitch accents and H boundary tones -a feature specific to TrinE (and TEC) likely due to historical crosslinguistic influence from Bhojpuri -may lead to higher variability in pitch on the phonetic level in English in Trinidad compared to Grenada and Dominica  (Gooden & Drayton, 2017).
Thus, while the characteristic intonational phonology of TrinE might be a pre-condition, the observed patterns highlight an additional sociophonetic component in female Trinidadian speakers leading in pitch variability. The Caribbean data further show distinct types and degrees of sociolinguistic variation in prosody (RQ3). English in Trinidad is not only distinct in its high pitch variability (among female speakers), but also the socioprosodic patterns of variation that exist there. By and large, significant age-stratified variation did not occur in Dominica and Grenada; the apparent-time change toward decreased pitch variability in terms of range, dynamism, and (variability in) rate of change was found to be limited to Trinidad. Similarly, urban-rural differences regarding the above parameters and tone rate were only significant in Trinidad but not Grenada.
The present study has its limitations, and future complementary research would be needed to further elucidate the findings. The study observed an effect of LIVED_ABROAD in read speech but possible reasons for this are not clear. Apart from this, the findings on age-stratified variation are restricted by the limited number of speakers in the older age group. Furthermore, it would be desirable for future studies to analyze the intonational phonologies of English in Dominica, Grenada, and Trinidad (in line with previous research on selected Caribbean Creoles, see  Drayton, 2013; Gooden & Drayton, 2017) ; this would provide a basis to verify (or falsify) the hypothesis that differences in the datasets from the different territories in terms of pitch variability at the phonetic level might be related to underlying differences in the intonational phonologies.
However, as the present research has been conducted on the island of Trinidad and cannot be generalized to Tobago given its different sociolinguistic situation, we will only refer to Trinidad in the remainder of this paper. 2 Another important factor is ethnicity  (Leung & Deuber, 2014; Meer & Fuchs, 2021). However, ethnicity-based prosodic variation is not described here given that (i) the variable is specific to Trinidad and (ii) ethnic differences can almost only be observed regarding pitch level and much less so for measures of pitch variability  (Meer & Fuchs, 2021) , the latter being in focus in the present study. For a large-scale analysis, see  Meer and Fuchs (2021). 3 Virtually the same reading passage was used for elicitation of read speech across all datasets, but the passage in Dominica included slightly fewer words.
Additionally, a few younger male students in the Dominican set were given a different, shorter reading passage in order not to overwhelm younger speakers and ensure reading fluency. This reading passage solely included declarative sentences except for one question. 4 Dagmar Deuber and Véronique Lacoste recorded the Dominican, Eva Hänsel the Grenadian, and Philipp Meer the Trinidadian data. 5 An analysis of the Trinidadian data compared to the British and Indian datasets is provided in  Meer and Fuchs (2021). 6 The younger group almost exclusively features students, but also seven (out of 188) teachers (two from Dominica, four from Grenada, one from Trinidad). 7 Some students, especially in the Dominican and Grenadian (but not the Trinidadian) datasets spent the first months after birth (up until early toddlerhood) abroad.
metric, the pattern differs. The datasets from all territories have very similar and non-significantly different mean rates of change (3. 2 Hz each) and associated variability in this rate (India: 15. 4 Hz, Dominica: 15. 2 Hz, southern England: 15. 2 Hz, and Trinidad 15. 1 Hz). The only exception is the Grenadian data, whose mean rate of change (2. 8 Hz) and associated variability (13. 5 Hz) are significantly lower than those of the Dominican and Trinidadian data (each at least at p <. 05).
(read: 238 Hz, spont. : 226 Hz) has the highest pitch level, followed by southern England (read: 209 Hz, spont. : 205 Hz), Dominica (read: 209 Hz, spont. : 199 Hz), Grenada (read: 209 Hz, spont. : 197 Hz), and Trinidad (read: 199 Hz, spont. : 193 Hz) (see Figure 2 for p-levels of individual pairwise comparisons). The parameters on variation in pitch show pronounced cross-territorial differences, usually with Trinidad and/or India having the highest degree of variability. Compared to the other Caribbean datasets, Trinidad.
Estimated means for different age groups overall and across the Caribbean datasets for pitch parameters in read and spontaneous speech. Effects: AGE and TERRITORY*AGE. Trends in apparent time indicated for significant effects Not fully linear trend but difference between younger and middle-aged speakers not significant. ‡ Not fully linear trend; middle-aged speakers in the lead, with the difference between younger and middle-aged speakers being significant. § Overall AGE effect only. No significant interaction of TERRITORY*AGE.
ACKNOWLEDGEMENTS This study was conducted in the framework of the project ' Translocality in the anglophone Caribbean II: Sociophonetic variation and perception ' (PI:  Dagmar Deuber ) funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -259827343. We would like to thank  Plinio A. Barbosa  for help and recommendations concerning some of the pitch parameters computed in this study and  Leônidas José da Silva Junior  for helpful suggestions. Open Access funding enabled and organized by Projekt DEAL.
Estimated means for different age groups overall and across the Caribbean datasets for pitch parameters in read and spontaneous speech. Effects: AGE and TERRITORY*AGE. Trends in apparent time indicated for significant effects Not fully linear trend but difference between younger and middle-aged speakers not significant. ‡ Not fully linear trend; middle-aged speakers in the lead, with the difference between younger and middle-aged speakers being significant. § Overall AGE effect only. No significant interaction of TERRITORY*AGE. TA B L E 9 Older Middle Younger p Trend Overall Range read 10. 5 st 8. 3 st 9. 3 st * ↓  † spont. 10. 6 st 8. 9 st 9. 1 st. 241 Dynamism read 0. 231 0. 188 0. 185 ** ↓ spont. 0. 251 0. 211 0. 193 *** ↓ Rate of change  § read spont. 2. 1 Hz 2. 0 Hz 1. 7 Hz ** ↓ Variability in rate read of change  § spont.
Data overview In this study, we use several types of measurements. While (1) atmospheric trace gas concentrations and (2) eddy covariance (EC) fluxes are the main objects of our analysis, we additionally use (3) Atmospheric Boundary Layer (ABL) heights in combination with vertical profiles of potential temperature observed at the tower to both evaluate the atmospheric stability and    (2019) respectively. Therefore only a brief summary is provided here. The availability of the datasets is summarized in Fig. S2.
Eddy covariance (EC) measurements Another set of temporal highly resolved CO (3) Applying a 3-std filter (removing ∼ 1-2 %). All %-numbers are relative to the EddyPro processed data. The last step in the flux processing is a gap-filling procedure (software package REddyProc v1. 2;  Wutzler et al. , 2018) , basically filling gaps with fluxes of corresponding months, time of day, and similar global radiation regimes (Sec. 2. 2. 4). A more detailed description of the EC-flux measurements at Beromünster is available in Herrmann (2019).
Analysis of data subsets for high observations The main goal of our analysis is to recognize high concentration and flux observations (Sec. 2. 3. 1) of the various species and identify situations in which the percentage of these high observations is largest. Therefore, we analyze our dataset separated in various subsets to account for different atmospheric stability (which we associate with shorter/longer transport; Sec. 2. 3. 2) as well as the prevailing wind direction representing different cardinal direction of source regions (Sec. 2. 3. 3).
These diurnal-seasonal statistical values are used to flag observations as high if they are above the respective subgroup 90 th percentile. This means each observations is evaluated with the respective diurnal-seasonal subgroup statistics. Furthermore, we normalize deviations from the respective subgroup median by dividing the difference between the individual observation and the corresponding median by the respective MAD. In the next step we analyze subsets of the entire dataset, e. g. , separated by prevailing wind direction, and investigate the percentages of observations flagged as high in these "wind direction subsets". A respective subset is more prone to high observations if the percentage of observations flagged as high is greater than 10 % -this is an important concept of the analysis here.
We design our statistical method in this way to predominately remove natural variability caused by photosynthesis and respiration (seasonal and diurnal) -CO 2 concentrations are in general lower in summer (photosynthesis dominates over respiration) and higher in winter (respiration dominates), however also anthropogenic variability will be removed to some degree (e. g. , weekday/weekend variations of traffic induced anthropogenic CO 2 emissions). The same concentrations may therefore be flagged as high in summer, while in winter it may be below the 90 th percentile. Ideally exactly 10 % of data points should be above the 90 th percentile if we look at subsets of the entire dataset, e. g. , winter (DJF), summer (JJA), spring (MAM), and fall (SON) in Fig. A1. However, due to the partly small sample size in the diurnal-seasonal subgroups (when calculating the percentiles) the percentages deviate slightly from 10 % -exactly 10 % would only be reached with a sufficient sample size.
Separation by atmospheric stability (transport distance) We separate our dataset by atmospheric stability in an attempt to distinguish between long-and short-distant transport and evaluate if high concentration/flux observations (see Sec. 2. 3. 1) are more likely caused by distant or local sources. For this separation we use the prevailing ABL heights (Sec. 2. 2. 3) and local vertical potential temperature gradients ( ∂Θ ∂z thereafter). Assuming the ABL heights derived from ALC observations at Payerne provide a suitable characterisation of the regional-scale ABL dynamics, we combine these ABL heights with the local ∂Θ ∂z profile to ensure atmospheric stability effects on shallow layers (below EC sensor height) are represented accurately. The ∂Θ ∂z samples the very lowest surface layer (tower base to 212 m above ground) and can be used to identify strong surface temperature inversions.
4 ; e. g. , 1 pm refers to 1:00 pm to 1:59 pm). For the separation by time of day we choose time slots of equal length (4 hours) and try to avoid the day-night transition for day and night as good as possible. As a result we neglect some hours for the time of day subsets (used hours listed in Fig. 4 ). Since our statistical approach should eliminate diurnal and seasonal signals (e. g. , also incl. weekday/weekend variations of anthropogenic CO 2 emissions), deviations from 10 % of high observations in different atmospheric stability and wind direction subsets need to be related to changes in the sampling of source regions (Sec. 2. 3. 1). Performing the separation in DJF/JJA/MAM/SON and day/night/morning/evening for the entire dataset, i. e. , no separation by source region, show high observations close to 10 % (Fig.
8 ). The parameterization is derived from an ensemble of backward LPDM simulations and is valid for a broad range of boundary layer conditions and measurement heights  (Kljun et al. , 2015). Most input variables required to calculate the source area are taken from the EddyPro Software output (Sec. 2. 2. 2; mean wind speed, Obukhov length, standard deviation of the across-wind velocity, friction velocity) in addition to the wind direction, the measurement height, and measurements/estimates of the ABL height (Sec. 2. 2. 3). The FFP estimate can only be derived for ABL heights above the flux sensor height, since surface fluxes become decoupled from the surface once the ABL height is below the sensor (ABL acting as a lide;  Kljun et al. , 2015). Therefore, observations with a ABL height below the sensor are excluded from the shown FFP climatology estimate.
Transport distance In general, the DISTANT source subset is more likely to see high CO, CO 2 , CH 4 , and H 2 O concentrations and CO 2 fluxes (> 10% in most subsets in Fig. 5 top). The LOCAL source subset on the other hand shows predominately fewer high concentration and CO 2 flux observations (< 10% of observations in Fig. 5  bottom). For H 2 O fluxes there are no distinct differences between DISTANT and LOCAL source subsets. Fewer high observations in the LOCAL source subset indicate that pollution events at Beromünster are mostly not caused by local sources, but the pollution is transported from further away to the tower (also elevated percentages of high observations in the REGIONAL source subset; Fig. A4 ). While the seasonal signal is removed with our statistical method when averaging over the entire dataset, we still see a difference between winter (DJF) and summer (JJA) comparing DISTANT (Fig.
5 ; top) and LOCAL (Fig. 5 ; bottom) sources. Especially, CH 4 and CO concentrations are elevated in the LOCAL JJA subset by ∼ 30 and 60 % (∼ 13 to 16% compared to the expected 10%), respectively, while in the rest of the year high observations are below average (< 10%). While most species show slightly elevated numbers of high observations for all seasons in the DISTANT source selection. Similarly, the diurnal cycle is also removed with our statistical method when averaging over the entire dataset. However, again the diurnal cycle appears to be different between DISTANT (Fig. 5 ; top) and LOCAL (Fig. 5 ; bottom) sources. Particularly, the differences for morning and day in the two source subsets are large; ∼ 13-17 % of high CO, CO 2 , and CH 4 concentrations in the DISTANT subset vs. only ∼ 4-5 % in the LOCAL subset.
Wind direction However, the strongest variability in high observations we see depending on the prevailing wind direction (WindNE, WindSE, WindSW, WindNW in Fig. 5  top and bottom). CO, CO 2 , and CH 4 concentrations are more than twice as likely to be categorized as high during wind from the NE for DISTANT sources (∼ 23 % vs. expected 10 % if averaged over all wind directions; alldata in Fig. A1 ). For LOCAL sources this pattern is less pronounced, ∼ 15 % for CO and CH 4 , ∼ 11 % for CO 2 for WindNE. For the DISTANT WindNE CO 2 fluxes are also elevated (14. 4 %), while below average in the LOCAL WindNE (7. 3 %). Also the DISTANT WindSE subset shows elevated percentages of high concentrations, especially CH 4 with a value of ∼ 24 %. As a side note, the seasonal wind direction distribution remains relatively constant over the year.
Characterization of the Beromünster FFP As an addition for the interpretation of the EC-flux observations, we present climatologies of parameterized flux footprints (FFPs; Sec. 2. 4 and Fig. 1b ) of the highest Beromünster observation level (212 m agl). Averaged for the entire dataset the FFP shape (alldata; Fig. 8a ) is elongated in the main wind axis (NE-SW). In this along-wind direction the extent of the FFP is ∼ 50 km, while in the perpendicular across-wind direction it's ∼ 25 km -the extent referring to the 80 % isoline enclosing the area where 80 % of the observed EC-fluxes are estimated to originate from. The relatively large spatial extent of the FFP is a result of the great observation height. However, we want to emphasize that the relative contribution of emission sources to observed EC-fluxes decreases with the distance from the tower, i.
previously published Concentration Footprints (CFPs;  Oney et al. , 2015)  indicate a sensitivity of the observed concentrations to emissions from 100s of kilometers around Beromünster tower (all over Switzerland and beyond; limited by the Alps in the South). while the parameterized Flux Footprints (FFP) of Beromünster presented here, indicate a direct influence of emission fluxes in the surrounding 20-50 km of the tower (depending on season; smaller more round summer FFP, larger more elongated (NE-SW) winter FFP). Fluxes originating from close urban areas might reach the tower at least occasionally. the strongest enhancement of high CH 4 concentrations is found during SE wind (high concentrations ∼ 2. 5 times more likely than compared to entire dataset). the corresponding signal of high CO 2 fluxes is weaker (less enhanced than for CO 2 concentrations) and limited NE wind; probably a result of the long distance from the strongest CO 2 flux sources (likely located near/in Zurich).
An additional analysis could be done based on a separation by sunshine duration, since biospheric activity is highly correlated with available sunlight. Furthermore, an extension to the present study is planned to investigate the relationship between FFPs and CFPs to give an update and further analysis to the previously published CFPs  (Oney et al. , 2015; Rust et al. , 2022)  by investigating various meteorological input datasets (e. g. , different spatial resolution) in combination with a multitude of atmospheric transport model settings (Lagrangian atmospheric model FLEXPART;  Pisso et al. , 2019; Stohl et al. , 2005). We aim to use these atmospheric transport simulations in combination with earlier published Swiss CO 2 and CH 4 emission inventories  (Albrecht et al. , 2012; Hiller et al.