dataesr/scientific-paragraphs-categorization huggingface dataset:
2010). Pollen assemblages from the same sites where the megafossils were collected belong to the zone T-03b-Foveotricolpites perforatus of  Jaramillo et al. (2011) , indicating Middle to Late Paleocene age (c. 60-58 million years ago). Fossil specimens are deposited at the paleontological collections of the Colombian Geological Survey (SGC) and the Colombian Petroleum Institute (ICP). Additional information about samples and localities can be accessed through the Smithsonian Tropical Research Institute (STRI) sample database (http://biogeodb. stri. si. edu/jaramillosdb/web/ login, accessed 30 April 2019). The leaf morphological descriptions are based on the terminology of shape and venation characters of  Ellis et al. (2009). The terminology of the fruit morphological descriptions is mostly based on  Gunn (1984 Gunn ( , 1991)) ,  Kirkbride et al.
Laboratory analysis and bioinformatics analysis After DNA extraction and metagenomic shotgun sequencing (Illumina HiSeq 3000, 50 million paired-end reads per sample), the cleaned reads were mapped to the ResFinder antimicrobial resistance gene (ARG) database (accessed 17 November 2016) of the Centre for Genomic Epidemiology. 19 The output was clustered at an ARG sequence identity level of 90%. The unit of outcome is a normalized read count FPKM (fragments per kb reference per million bacterial fragments). FPKM was calculated by dividing the mapped resistance fragments by the length of the respective resistance gene and the total number of bacterial fragments per sample and multiplying by 10 9. In the analyses, the following outcomes per flock were used: (i) the sum of FPKM of all resistance gene clusters; (ii) the sum of FPKM per antimicrobial class; and (iii) the FPKM per 90% identity-level gene cluster for two antimicrobial classes.
Data analysis A country effect appears in both AMR and AMU data and has been described before. 7, 18 For example, the country of origin of the samples is significantly associated with the resistome and explains roughly 25% of the variation observed. To address this effect, considering the total number of farms per country included in the analysis (18 or 20), we used countryspecific models as input for a random-effects meta-analysis (R package Metafor, DerSimonian-Laird heterogeneity estimator). 20 Meta-analysis allows results to be obtained and visualized in a transparent way. 21 utcome and AMU data were log 10 transformed because of skewness (1 was added to keep zeros), and the outcome was standardized (mean 0, SD 1). Thus, associations were first calculated with linear regression per country and subsequently a meta-or overall association was calculated across countries.
Concurrent usage and observations of the corresponding resistance did not occur at all farms and occasionally not in each country, resulting in specific meta-analyses with data from fewer than nine countries. The analysis was performed stepwise: first the association between AMU and ARGs was calculated for each corresponding antimicrobial class (e. g. tetracycline resistance versus tetracycline use), followed by non-corresponding classes. Confounding by biosecurity status of the farm and sampling age of the broilers was tested. A sensitivity analysis was performed by calculating AMU as a determinant for the resistome in broilers JAC the association between corresponding ARGs and AMU as a binary variable, with 0 meaning no AMU reported at flock or farm level and 1 meaning (any) AMU reported at flock or farm level.
24 In the    AMU as a determinant for the resistome in broilers JAC frequent-and expected-case that not all classes of antibiotics were used in a specific flock in its lifespan, when this occurred countrywide this country was not included in the meta-analysis. The results per antibiotic class were therefore often based on fewer than nine countries. b-Lactams, quinolones and polymyxins were the classes used most in this study. 7 For quinolones and polymyxins, no significant relation was found with their corresponding ARGs. For polymyxins, this is probably due to relatively low gene abundances in the samples (Figure  1 ). For quinolones, usage is reported in almost all countries and an association with resistance has been described before (though not with metagenomic analysis). 13, 25 One likely reason for this lack of association is that quinolone resistance is partly due to point mutations that could not be detected sufficiently with the resistance gene database used here, and is difficult to detect in metagenomic studies altogether.
Acknowledgements Parts of the results have been presented orally at the 5th International One Health Congress in Saskatoon,  Canada ,  June 2018  (abstract number  4 AMR 02 ). We thank all broiler-herd owners and their veterinarians who participated in this study, and all researchers of the  EFFORT consortium  for close collaboration, especially those involved in visiting and sampling the participating farms and laboratory work:  M. Schlepers  (Belgium);  T. Ivanova ,  N. Cholakov ,  E. Gurova-Mehmedova  and  K. Penchev  (Bulgaria);  F. Nienhaus (Germany) ;  C. L. Nielsen ,  P. Ryt-Hansen ,  B. Hølstad ,  B. Rasmussen  and  K. Nielsen  (Denmark);  C. Jenna ,  D. Virginie ,  E. Florent ,  E. Eric ,  S. Le Bouquin ,  L. Denis  and  T. Rodolphe  (France);  M. Gherpelli ,  M. Pegoraro  and  V.
As OH-reactivity data is not available during IBAIRN we cannot assess which fraction of OH-reactivity could potentially be missing for the much cooler autumn conditions, but expect this to be less than the 58% reported for the warmer summer months. We show below that unattributed OH-reactivity at the 50% level, does not significantly modify our conclusions. We estimate an uncertainty in the measured OH reactivity of ≈ 70% with contributions of 50% from [OH], 30% from β, 15% from  i OH , 30% from α i RO 2 and 25% from[Ci].
, 2014a; Lee et al. , 2016)  we find that in total only 10% of the organic nitrates were C5, whereas C6-C10 accounted for 85% of the signal. This is indicated in Fig. S3  of the supplementary information. In Figure  7 , we compare the TD-CRDS measurement of the ΣANs measurement to signal of the I-CIMS, the latter separated into C1-C5 and C6-C10 species. As the I-CIMS data are available as counts only, we have normalised the datasets in Figure  6  to the values at 12:00 UTC. and C10 nitrates provided the greatest signals on average, followed by C9 and then C7 and C8. As seen from the diel average (Fig. 7 ), the organic-nitrate signals were significantly larger during day than at night-time, the lowest day-to-night ratios being found among the C9 and C10 species and the largest day-night ratios found for C5 and smaller.
The C1-C5 and C6-C10 trace gases identified by the I-CIMS display very similar diel cycles, with daytime maxima and low values at night-time, as also seen for the ΣANs data. The I-CIMS data do however indicate a much more pronounced diel cycle, which is related to the different positions of the inlets of the instruments. Whereas the TD-CRDS sampled at a height of 8. 5 m above the ground, the I-CIMS inlet was located at a height of just 1. 5 m. As mentioned already, high relative humidity was frequently accompanied by ground-level fog at night-time during IBAIRN, which would have significantly impacted the I-CIMS dataset, resulting in lower, mean night-time signals. https://doi. org/10. 5194/acp-2019-463 Preprint. Discussion started: 24 May 2019 c Author(s) 2019. CC BY 4. 0 License.
Data collection Fieldwork took place on Bird Island (Algoa Bay, South Africa) during December 2015. Twenty chick-rearing Cape gannets were deployed with devices to record their behaviour and vocal communication while foraging at sea. Nests were observed unceasingly until a change-over (characterised by a display dance) between two partners was observed. The bird on departure to sea was captured near the nest using a pole with a hook on the end (thus staying as far from the colony as possible). Only one parent (of either sex since the species is monomorphic) was captured per nest and devices were attached for one foraging trip only, while the partner was on the nest guarding the chick. Nests were then monitored every hour from sunrise to sunset, so the deployed birds were captured again soon after their return to the colony and the devices were retrieved.
Only the data from the acoustic recorders were used in this study. Audio recorders (Edic-mini Tiny+ B80, frequency response 100 Hz-10 kHz ± 3 dB, 65 dB dynamic range, TS-Market Ltd. , Russia, fitted with a CR2450 battery) were set up to record sound in mono at a 22. 05 kHz sampling frequency. They recorded continuously, hence collecting data during the whole foraging trips of the birds (one to two days). Five different units were deployed successively on the 20 individuals. The main challenge for collecting such acoustic data was to ensure high quality recordings on board of a flying and diving bird. To limit disturbance from the wind, we placed the audio recorder on the lower back of the bird, under feathers and facing backward. In addition, a thin layer of foam was added after the first deployment to reduce flow and background noise.
The nest monitoring was undertaken in the shortest time possible (usually within five minutes) and at the largest distance possible from the nests (just close enough to identify markings) in order to limit potential observer effects. The effect of being a control bird, a deployed bird, or a partner of a deployed bird on the duration of trips was tested using linear mixed-effects models (package 'lme4' in R software, R Core Team,  Bates et al. 2014). A null model including only individual birds as a random effect was compared to a model including the group (control, deployed, partner) as a fixed effect in addition to the random effect using an analysis of variance. Among the deployed birds, the effect of carrying devices was tested in a similar way. Residuals of all models were checked graphically for normality and heteroscedasticity.
Measure of acoustic variables Sound data were resampled at 12 kHz to increase the accuracy of frequency measurements and because no call was observed to contain energy at frequencies higher than 5 kHz. All the vocalisations were analysed using Avisoft-SASLab Pro (ver. 5. 2. 09, Avisoft Bioacoustics, Germany). The spectrogram of each recording (Hamming function, FFT 512 points window size, 75% overlap) was visualised over a sliding window of 20 s length to identify and label all the calls. Calls were produced in sequences, and sometimes composed of two parts (with only one occurrence of three parts), identifiable by a drop in amplitude (a call made of two parts is illustrated on Fig. 1d ). Calls were selected for measurements wherever the quality of the recordings allowed (i. e. low background noise, good signal to noise ratio, no overlap with other vocalisations, no clipping).
Classification procedure The RF was chosen because it does not require assumptions on the distribution of predictor variables, and for its capacity to deal with imbalanced data (when some classes, contexts in our case, are represented more than others). The RF is based on the classification and regression tree algorithm  (Breiman et al. 1984)  where a tree is constructed by recursively splitting the dataset into two subsets. RF adds two levels of randomization  (Breiman 2001 ): several trees are grown from bootstrap samples of the dataset (random selection with replacement), and at each node a subset of variables is randomly selected to split the data. The accuracy of prediction is estimated intrinsically using the bootstrap process. For each tree grown, the class of data that were left out of the bootstrap sample (out-of-bag data) are predicted using the constructed tree, so their predicted classes are compared to their correct known classes.
The difference in accuracy of prediction using the original and the randomised values gives a measure of the importance of the given variable (the larger the difference is, the more important the variable was for prediction). The RF was run in R software using the package 'random-Forest'  (Liaw et al. 2014). The number of trees to be grown from bootstrap samples of the dataset (parameter 'ntree') was set at 1000 for the analysis per contexts, and at 2000 for the analysis per individual. This ensures convergence of the results (Supplementary material Appendix 1 Fig. A2, A3 ) as well as robustness in the measure of variable of importance  (Genuer et al. 2008 ). To set the number of variables to be randomly selected at each node (parameter 'mtry') we used the default value proposed in the R package: the square root of the total number of variables, so three in our case.
This setting provides the best results for datasets where the number of individuals is largely bigger than the number of variables  (Genuer et al. 2008). Our dataset is imbalanced, with the context TOF representing 2% of all the observations. We used a mix of over-sampling the minority class and down-sampling the majority classes to deal with the issue. The over-sampling increases the accuracy of prediction of the minority class, but also of the other classes since it allows to keep in more data when down-sampling. The down-sampling ensures that data from the minority class are present at each bootstrap, and thus increases accuracy of prediction for this class. We over-sampled the minority class TOF to twice its size using the package 'unbalanced' in R  (Dal Pozzolo et al. 2015). We then used the balanced random forest algorithm proposed by  (Chen et al.
2004 ), which down-samples the major observational classes according to the occurrence of the minor class. For the analysis comparing the calls defined as WAT and in the air (FLY&DIV), the imbalance of the dataset was not as extreme with calls in the air representing 40% of the observations, so only the down-sampling technique was used  (Chen et al. 2004 ). The analysis comparing the calls defined as FLY and DIV was not imbalanced. Finally for the analyses on individuality we used the down-sampling method  (Chen et al. 2004 ).
Results We recorded and analysed a total of 214 h 50 min and 43 s of sound data, from 18 foraging Cape gannets. No data could be retrieved from the audio recorders deployed on the other two individuals (the recorders could not connect to the laptop and software, possibly due to water damage). The number of call sequences recorded for each individual varied between seven and 276 (median 75), irrespective of the ± 2. 7 [1-23] 32 7. 2 ± 4. 3 [1-15] 269 2. 3 ± 3. 0 [1-19] 271 3. 4 ± 2. 1 [1-13] < 0. 001 < 0. 001 Nparts 776 1. 2 ± 0. 4 [1-3] 32 1. 1 ± 0. 2 [1-2] 269 1. 2 ± 0. 4 [1-2] 271 1. 3 ± 0. 5 [1-2] < 0. 001 < 0. 001 duration of the trips and recordings (Supplementary material Appendix 1 Table  A1 ). In total, vocalisations were recorded on 1718 occasions (957 times when the birds were resting on the water, 30 during taking off, 412 when flying and 319 just before diving).
Acknowledgements -We thank  South African National Parks  for logistical support during fieldwork. We thank  Rabi'a Ryklief ,  Jonathan Botha  and  Melanie Wells  for their help in the field. Permits -Fieldwork was undertaken under a permit from  SANParks :  PISTP1238. All experimental protocols were approved by both  SANParks  and the  Nelson Mandela Univ. Ethics Committee  (reference:  A10-SCI-ZOO-008 ), and were carried out in accordance with the approved guidelines.
Here we present a global dataset of the distributions of organic carbon activation energy and corresponding radiocarbon ages in soils, sediments, and dissolved organic carbon; we find that activation energy distributions broaden over time in all mineral-containing samples. This result requires increasing bondstrength diversity, consistent with the formation of organo-mineral bonds 8 but inconsistent with selective preservation. Radiocarbon ages further reveal that high-energy, mineralbound organic carbon persists for millennia relative to low-energy, unbound organic carbon. Our results provide globally coherent evidence for the proposed 7 importance of mineral protection in promoting organic carbon preservation. We suggest that similar studies of bond-strength diversity in ancient sediments may elucidate how and why organic carbon preservation-and thus atmospheric composition and climate-has varied over geologic time.
physicochemical interactions with a mineral matrix. Mineral protection is evidenced by laboratory incubation experiments  15  , non-selective OC preservation in sinking marine particles  16  , and strong observed correlations between OC content, iron content  17, 18  , and mineral surface area  7, 8, 19  in soils and sediments. Although both selectivity and protection must play some role across a range of environments and timescales, directly measuring their relative importance using traditional geochemical techniques has proven difficult. To address this issue, first we develop a conceptual framework that predicts how chemical compositions, as recorded by OC bond strength, and preservation timescales evolve when OC is exposed to each mechanism. We then compare predictions to results from a global dataset.
g. , Arabian Sea sediments). This global dataset includes samples analyzed using slightly different protocols (e. g. , pyrolysis vs. oxidation), but an uncertainty assessment indicates that methodological variability is small and that complex kinetic behavior during sample heating is unlikely to explain observed trends (Methods)  22, 23. Furthermore, because our focus is the persistence of biospherically produced OC, we excluded samples containing carbonates, anthropogenic OC, and/or rock-derived OC, resulting in a final dataset of 62 samples. This screening procedure removes permafrost-dominated soils since these samples exhibit anomalously old  14  C ages due to protracted storage under frozen conditions. It is possible that some retained samples contain minor rock-derived OC inputs. However, such contributions never exceed approximately 5% of total OC and cannot explain resulting E and  14  C trends (Methods).
We treat each OC component as decaying according to a rate constant defined by its bond strength. Because high-E OC is generally older than low-E OC in our dataset, we assume a decreasing rate constant with increasing E. High-E material is therefore selectively preserved, as expected if stronger bonds are inherently more resistant to degradation  4. The distribution p(E) can then be obtained at each point in time by solving the first-order differential equation that describes the evolution of p(E) as %OC decreases (Methods). Using a Gaussian distribution for the initial condition, we obtain the results shown in Fig. 3a. In contrast, Fig. 3b  shows the observed average p(E) for all mineralcontaining samples binned by %OC. Although selective preservation is consistent with the observed narrow DOC distribution  9  , it fails to reproduce observed p(E) trends for all mineralcontaining samples, which broaden with decreasing %OC and suggest increasing bond-strength diversity.
Published data compilation. We compiled all published RPO results that include  14  C measurements  25, [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47]. All thermograms and blank-corrected RPO fraction data (temperatures, masses,  13  C contents, and  14  C activities) can be accessed in the online database  48. Samples included in this database span five continents and a latitude range between 70. 5° N and 64. 5° S (Extended Data Fig. 1 ). Samples were categorized as either: DOC, soil, riverine POC, or marine sediment. Sample collection and pre-treatment methods are described in detail in each of the original publications and are summarized in Extended Data Table  1. New samples analyzed for this study. In addition to previously published results, we included in our database five samples presented for the first time in this study.
Because all  14  C decays at the same rate (i. e. , according to the 14 C half-life), R is conserved with the passage of time  55. Finally, R can also be expressed in "fractional" notation analogous to Fm according to: F 14 R = e -R/8033. (6) All OC aging trends discussed in this manuscript are reported in  14  C yr and R notation, whereas sample screening and end-member mixing calculations utilize Fm and F 14 R notation for mathematical convenience (see "Sample screening," below). Finally, all  14  C results reported here were corrected for instrument-specific RPO procedural blank contamination  33, 56. Uncertainty assessment. Because our dataset contains samples analyzed using multiple methods, we assessed the influence of sample pre-treatment and RPO instrumental conditions on resulting E distributions.
This was done by subjecting the same samples to various experimental conditions (Extended Data Table  2 ). Specifically, there are three methodological variables that might influence OC thermal stability within our dataset: (i) whether or not a sample was acidified  38, 39  , (ii) the type of acid treatment, and (iii) whether the RPO instrument was operated in pyrolysis or oxidation mode. First, the overall influence of acidification was determined using 4 carbonate-free samples: one natural Canadian soil  38  (sample CaSa_soil in Extended Data Table  2 ), two Hawaiian soils  25, 33  (PuuEke4 and Pololu1), and one Amazon River suspended sediment  39  (AmRi_ju2014_54m). PuuEke4 was analyzed in both pyrolysis and oxidation mode, leading to 5 acidified vs. non-acidified pairs. For these samples, acidification increased µE values by 5.
9 ± 4. 8 kJ mol -1 and sE values by 2. 1 ± 2. 3 kJ mol -1 relative to oxidation mode (n = 7). Additionally, Grant et al. 25  report only minor differences between pyrolysis and oxidation modes in a set of Hawaiian soil samples. This similarity indicates that complex kinetic reactions such as charring  23  occurring within the RPO instrument cannot fully explain our observed trends. If charring were driving RPO results, then we would expect significantly higher µE values when the RPO instrument is operated in pyrolysis mode due to the lack of atmospheric O2; however, this is not observed. Furthermore, charring would act to shift low-E, 14 C-enriched OC toward higher E values, thus artificially dampening E vs. 14  C signals and weakening observed trends. Thus, none of these methodological differences can explain the signals observed in our dataset.
Sample results span a µE range of 80 kJ mol -1 , significantly broader than both the largest bias (6. 7 kJ mol -1 ) and the widest uncertainty (±10. 9 kJ mol -1 ) observed in this assessment. Similarly, observed sE values in our dataset span a range of 35 kJ mol -1 , much broader than the maximum methodological bias (±3. 8 kJ mol -1 ) and uncertainty (±4. 0 kJ mol -1 ). We therefore assume a conservative uncertainty of ±10. 9 kJ mol -1 for µE and ±4. 0 kJ mol -1 for sE, resulting in a minimum signal-to-noise ratio above 7 for both metrics. Sample screening. Because our focus was on the preservation of naturally produced biospheric OC that has been continuously exposed to heterotrophic respiration since initial formation, we screened the database for samples containing other carbon sources or alternate diagenetic histories.
This resulted in the exclusion of permafrost-containing samples from the Colville River basin  43, 47  as well as paleosol samples from Louisiana  45. Still, we note that inclusion of these samples does not substantively change observed E or 14 C trends. In total, our screening procedure removed 56 potential samples from our analysis (mostly due to OCpetro contributions), resulting in a final database of 62 samples (6 DOC, 11 soils, 27 fluvial sediments, and 18 continental margin and coastal marine sediments). Retained samples are denoted as such in Extended Data Table  1. OCpetro sensitivity analysis. We performed a back-of-the-envelope sensitivity analysis to assess the influence of small OCpetro contributions (i. e. , less than 5 % of total OC) on E distributions of retained samples. Biospheric OC E was assumed to be Gaussian with µE = 140 kJ mol -1 and sE = 15 kJ mol -1 , the minimum observed values for non-DOC samples in our dataset.