dataesr/scientific-paragraphs-categorization huggingface dataset:
3979× + 8170. 1), (2) the mussel stocks in the Schleswig-Holstein part (x) and the total biomass for the two parts (y) (y = 3. 1258×-13,157). These equations are used to estimate missing values for the total mussel stocks during 1986-1997 using eq. (  1 ) and during 2008-2015 using Eq. (2). Data from 2009 to 2014 were used in the current study.
In this survey about 1800 eider wings are annually sexed and aged, and a breeding index was estimated (number of young/number of adult females). The wing survey data and the breeding index estimated at the wintering grounds in Denmark are considered representative for the Baltic-Wadden Sea eider population  (Lehikoinen et al. , 2006 (Lehikoinen et al. , , 2008)). A comparison between breeding success at Tvärminne,  Finland, during 1990 Finland, during -1991 Finland, during and 1994 Finland, during -2013  and the breeding index obtained from the Danish wing survey (1990-2013) at the main wintering grounds of the Baltic-Wadden Sea population showed a significant positive relationship (Estimate (S. E. ) = 2. 75 (1. 00); F = 7. 53, df = 1,20, P =. 01), taking year into consideration. This result indicates that breeding success of female eiders at the Tvärminne colony can be considered as representative of the breeding index measured in the eider population at the wintering grounds in Denmark.
Female body condition at hatching was measured during 2010-2014 according to established methodology  (Öst and Stele 2010). Females (N = 1033, range: 173-248 annually) were trapped at the nests during incubation. Upon capture, females were weighed, the length of the radius-ulna was measured, and incubation stage was estimated by egg floatation. Condition indices were derived as the standardized residuals of a regression of ln-transformed projected weight at hatching (response variable) on ln-transformed radius-ulna length. Indices for 2010-2014 were derived for the pooled data from a longer reference period (1990-2016, N = 26 years), and, therefore, mean condition index for the focal five-year period does not equal zero. A female's weight at hatching was estimated by subtracting an estimate of the weight they would be expected to lose during the remaining incubation time from their measured incubation weight.
Acknowledgements We thank the hunters who collected birds for the study,  Kai Eskildsen , Nationalpark und  Meeresschultz Schleswig-Holstein  together with  Heike Büttger  and  Georg Nehls ,  BioConsult SH , for data on mussel stocks in  Schleswig-Holstein  and  Jakob Strand ,  Aarhus University , for data on mussel flesh content collected during 1998-2013. Ako Osman Mirza  and  Michael Albert Schmidt  kindly assisted in the laboratory. We thank the  Eider Team members at Tvärminne , in particular  Kim Jaatinen , for their heroic efforts in the field, as well as the staff at  Tvärminne Zoological Station  for logistic support. This study received financial support from The  15 June Foundation in Denmark  (to KL) and the  Swedish Cultural Foundation in Finland  (to MÖ). Finally we thank  Steffen Oppel  for valuable comments.
38% of the mites of this group. The comparison of the distribution of the five most prevalent MLGs across populations and periods revealed highly significant differences across groups (Chi² test, p < 0. 001). Interestingly, the most predominant MLG in the historical samples (>58% in both populations) was not found in the current mite-resistant group but was found in the current susceptible group (18. 18%) (Fig. 2 ). Finally, to assess the spatio-temporal genetic changes in the four mite groups from the island of Gotland, an analysis of population structure was conducted using the software Instruct  30. Here, the samples from the mainland were excluded to focus on the changes due to the host colony phenotype (resistant vs susceptible) and exclude any background noise from a potential island effect.
The results of the genetic structure analysis using the software Instruct did not show a pure segregation of the two genetic clusters across locations or dates, which can be explained by the fact that a unique lineage of V. destructor invaded A. mellifera colonies of Europe a few decades ago only  7, 25, 35. Yet, despite this unique origin, significant genetic variation can now be observed, and the number of individuals belonging to the two genetic clusters detected here differed significantly across time and across the resistant and susceptible colonies in the current sampling period but not in the historic one. This temporal population structure was confirmed by the analysis of Multi-Locus Genotypes. The latter analysis permitted to assess the diversity and structure of V. destructor populations in a subtler way.
All mite samples were kept at -20 °C until DNA extraction. From hereinafter, the 2017 and 2018 samples will be refered to as the temporally "current" samples while the 2009 samples are refered to as the "historic" samples for streamlining the presentation and discussion of the data (Table  1 ). Genotyping. Total mite DNA was extracted using a Chelex protocol  48. Mites were genotyped using a set of microsatellite markers. In all, 20 markers were tested to determine optimal PCR conditions (ie multiplexing) and polymorphism on a subset of 24 individuals from three colonies of the susceptible historic group. From these 20 markers, nine were polymorphic and worked well under our lab conditions (Table  S1 ). PCR products were sent to Genoscreen (Lille, France) to run on a 3730XL sequencer (Applied Biosystems ® ).
Once received, the genotypes were scored manually using Peak Scanner v 1. 0 (Applied Biosystems ® ). Analyses. For the following analyses, the mite samples from Gotland were grouped according to their population of origin (resistant or susceptible) and the date of sampling (historic or current). The mite samples from Uppsala were included in the analyses labeled as "mainland". Only individuals with genotypes available for five or more loci were kept in this dataset. Along these lines, a total of 432 mites were kept in the analyses (Table  1 ). First, the linkage disequilibrium between pairs of markers was tested along with the Hardy-Weinberg equilibrium for each group using Fstat v 2. 9. 3  49. Then, to compare the genetic diversity across the five groups of mites, the number of alleles (N A ) and the level of observed heterozygosity (Ho) were calculated using the Microsatellite Toolkit  50  and compared using Kruskal-Wallis tests in R v.
3. 5. 2  51. We used the software ADZE  52  to conduct a rarefaction analysis on the allelic richness over markers for every mite groups to estimate whether the lowest sample size we used was sufficient to grasp accurately the genetic diversity of the group considered. The software GenAlex v. 6. 5  53  was then used to assess genetic differentiation in the populations of V. destructor of in the four groups from Gotland and between the current mainland and Gotland samples by calculating two distinct estimates of population differentiation (F ST and D est ) as recommended by  54. F ST reflects the probability that two alleles drawn at random from within a group are identical by descent  55  and is commonly used to compare the genetic differentiation between two or more group of samples  54, 56.
Generally, F ST values below 0. 05 suggest low genetic differentiation across groups, whereas when this estimates ranges between 0. 05 to 0. 25, moderate genetic differentiation is suggested  37. In parallel, D est complements this estimate and better reflects differences in allele frequencies when markers are highly polymorphic  56, 57. In addition, to explore the distribution of V. destructor genotypes, the package poppr 58 for R v. 3. 5. 2  51 was used to compare the prevalence of Multi-Locus Genotypes (MLGs) in the five mite groups. All individuals with missing data were excluded from this analysis (Table  3 ). Finally, an analysis of genetic structure without a priori was performed using the software Instruct 30 in order to infer the optimal number of genetic clusters underlying the mite sample (called "K") from Gotland (parameters set as following: admixture model, 20 chains of each K varying from 1 to 8, 50'000 burn-in and 100'000 iterations).
The methods from Evanno et al. (2005) was then used to determine the most likely K in the overall dataset. The results of the Instruct analysis were pooled using the software CLUMPP  60  and a figure corresponding to the most likely number of K was prepared with the software Distruct  61. In order to test whether the probabilities of belonging to a cluster differed among the different groups, a Chi-squared test was used to evaluate the relationship between the inferred clusters and the mite groups. To do so, the number of individuals in the different genetic clusters for each group was obtained from the ancestry values of the populations obtained with the CLUMPP software and was compared using a contingency table. In parallel, the differences between naturally-surviving and treated colonies for each sampling date was tested using the same method  59,62,63.
Acknowledgements The authors wish to thank  Åke Lyberg  for his long-standing support in maintaining the Gotland mite-resistant honeybee population. Cato Östmoe is also deeply thanked for providing the samples from the mite-susceptible honeybee colonies on Gotland in 2018. The study was financially supported by the  Swedish Research Council FORMAS  (grant diarienr. 2016-00481 ), the  Ricola Foundation Nature and Culture , the  Vinetum Foundation and The Persephone Charitable and Environmental Trust. Financial support for maintaining the Gotland miteresistant honeybee population was provided by the  EC funded National Program  through the  Swedish Board of Agriculture.
Since we aim to study past productivity using the OED as a diachronic source, using a raw frequency count will produce data that is relevant, i. e. the number of words that have existed over time (total output). Productivity can also be measured by the transparency of the pattern. Trips [2009: 31]  after  Hay & Baayen [2002]  show that "there is a relationship between relative frequency, parsing and morphological productivity", i. e. they are interrelated phenomena. A further issue appears when looking at a morphological pattern which is borrowed from a donor language. In the case of loan words, the output does not automatically correlate to internal formation if the morphological structure is not transparent. Bauer [2004: 144]  addresses this problem specifically with reference to the number of -able adjectives in the OED, underlining the confusion between loan words and internal formation.
Following  Bauer [2004: 159] ,  Schulte [2015] , Smith  [2016], [2018] , the OED is viewed as the most appropriate source of data for morphological lexical study and can be used in conjunction with a corpus analysis case study as a second step. Despite the limitations related to the use of a dictionary versus a corpus, the use of the OED is motivated by the diachronic nature of the dictionary and the inclusion of obsolete and rare words (possible hapaxes) which provides insight to relevant past productivity. Consulting a corpus will allow rare, easily-interpretable, words and nonce words to be included, but the coverage is likely to be less complete than that of a dictionary -at least, unless the corpus is extremely large indeed, or the dictionary very small. Given this difference, the use of a dictionary as a starting point and the use of a (suitably large) corpus as a point of comparison could be a positive compromise.
Using the OED as data and method The present section deals with -some derivatives in the OED. The suffix -some originates from Old English with Germanic roots, according to the OED etymology, see also  Jespersen [1942], and Marchand [1960]. The suffix initially forms denominal adjectives Nsome (first attested winsome [900] "pleasing or attractive, handsome, comely"), but we shall see that Vsome output considerably outperforms Nsome in the 19 th century with no fewer than 85 new words between 1800-1900, i. e. one third of total output, although this performance may be short-lived 12. As mentioned previously, the literature on -some derivation is scant. Jespersen [1942: 456]  gives a description of the -some suffix in a section called "final batch of suffixes" which include -ship, -dom, and -hood.
However, in contrast with Jespersen, Marchand does comment on the change of morphological patterning and between the 17 th and 19 th century output in terms of meaning, concluding the -some suffix has "lost its productivity for deadjectival coinages while the deverbal type has grown in importance, the derivatives meaning 'apt to or apt to cause to…'". Dixon [2014: 254-255]  describes -some adjectives as having the sense 'apt to', and notes the "gradual" decline in frequency of use over time as well as the lack of productivity today. In this section, we aim to collect data to track patterns of output. The question we aim to answer: is -some no longer morphologically productive (actively available) in all patterns? If so, what explains the loss of productivity? How can historical productivity be measured, i.
This revision has led to a number of antedatings as the OED revision process benefits from the increased availability of large diachronic databases with reliable annotations, see Durkin [2016a: 406]: [W]hat is reported in historical dictionaries is based on analysis of the evidence available at time of publication of the dictionary entry, and may well be subject to review if and when further evidence comes to light. First dates of attestation are particularly subject to change, as new evidence becomes available, and as the dating of existing evidence is reconsidered. In particular, the increased availability of electronic text databases in recent years has swollen the flow of new data to a torrent. Although the OED is not unproblematic, the use of a diachronic corpus would produce the same if not more limitations; not all the vocabulary of English can be found in one place with completely homogeneous data for each period.
For reference, EHBC contains over 800 million words of text (published words) and covers the older periods of English from 1473 to 1820 (Texts are from EEBO Phase I, ECCO and Readex's Evans projects). COHA contains more than 400 million words of text and covers a later period of English from 1810s-2000s and the corpus is balanced by genre decade by decade. Project Gutenberg English contains 400 million words of text from all English e-books available in the Gutenberg database. As for the contemporary corpora, COCA contains more than one billion words of text and covers a contemporary period from 1990-2020. The second contemporary corpus, the OEC, contains 2 billion words of text covering the 21 st century (mainly websites chosen in the way of presenting all types of English, from literary novels to everyday newspapers and the language of blogs and even social media, see also  Brinton [2016: 204] ).
Semantic shift and active/passive instability of -some adjectives Out of the OED data collected regarding the 261 -some adjectives, most belong to the TENDENCY TO category with an active sense. There are a much smaller number of -some adjectives given as having a passive sense INTENDED FOR. We count 9 Vsome adjectives which are classified as "intended for": wieldsome, ticklesome, relishsome, furthersome, metesome, clipsome, tewsome, cuddlesome, hugsome. If we consider e. g. the case of ticklesome, there are two distinct senses provided in the OED that correspond to diachronic shift. Ticklesome [1585] is defined as "That tends to tickle; difficult, critical, delicate, precarious, ticklish. Now dialect. ", then [1844] "Easily tickled; tickly; ticklish; suitable or fitted for tickling or laughter", as a consequence of an active-passive shift.
(c) Because most subjectivized items are polysemous and polyfunctional, a paraphrase must be found that will distinguish the subjectivized from the less subjectivized meaning. In order to assess the validity of a subjectification hypothesis, further large-scale qualitative analysis of Vable / Vsome competition is required. Furthermore, as underlined in  Fernandez-Dominguez [2017: 112]  the difficulty of assessing competition "seems to largely involve exploiting partial or incomplete datasets, insofar as it requires tracking down not only existing lexemes (i. e. the prevailing ones), but also derivatives that do not succeed in competition, and which often fall into disuse". Given the limited frequency of usage of -some adjectives, the task of studying suffixal competition cannot be merely quantitative but requires sufficient comparable data as to allow for a reliable comparison of semantic and distributional behaviours.
Experimental Design The experimental design and statistical procedure (see below) for Experiment 2 was preregistered (https://osf. io/peycm/; January 2018) prior to data collection (in February and March 2018). Data for Experiment 1 was collected prior to the pre-registration of Experiment 2 (in August and September 2017). The experimental design was identical for Experiments 1 and 2, apart from the electrical stimulation protocol, described in the next section. Both experiments consisted of adaptive runs (A) in which task difficulty was adapted to individual performance, tACS runs (Stim), and sham stimulation (Sham) runs, explained in the following. The order of runs (Fig. 1C ) was A-Stim-Sham-Sham-Stim-A-Sham-Stim-Stim-Sham, repeated twice. This design led to 4 adaptive runs, 8 Stim runs, and 8 Sham runs for each participant.
5 that each of the three words in a given trial were identified correctly. This was achieved using the threshold estimation procedure that is part of the Psychtoolbox  (Brainard, 1997)  in MATLAB (The MathWorks), building on a method described by  Watson and Pelli (1982). No stimulation was applied in these adaptive runs. The estimated mixing proportion was kept constant in the four (two Stim and two Sham) runs following the adaptive run and then updated in the next adaptive run, in order to account for any learning or fatigue effects. The outcome of our adaptation procedure, i. e. mixing proportions for individual participants and for each of the four adaptive runs, is shown in Fig. 1D. In Stim runs, tACS was applied at 3. 125 Hz such that neural oscillations should follow the alternating current  (Herrmann et al.
the pre-registration of Experiment 2, the pre-registered analysis had not been applied to the data at that time. The optimized analysis was selected exclusively based on results from simulations  (Zoefel et al. , 2019)  and had not been applied to any of the datasets prior to selection. The following analysis protocol was pre-registered for Experiment 2: (1) Performance in the word report task (proportion correct) was calculated, separately for each target word. In the Stim condition, this was done separately for the 8 phase relations between tACS and speech rhythm. In the Sham condition, word report accuracy was averaged across the 8 (pseudo-) phase relations, as word report accuracy cannot depend on tACS phase (since no tACS was applied). (2) Despite our use of adaptive runs to ensure that word report probability (averaged across target words) was 0.
3A ) Performance (the LOR between Stim and Sham conditions) was quantified as a function of the phase relation between tACS and speech rhythm, as described above. Most previous studies average data across participants by aligning maximal performance at a certain phase bin (e. g. , the center bin) and subsequently phase-wrapping the remaining data for each participant. However, given previous results concerning the suppression of the BOLD response by tACS  (Zoefel, Archer-Boyd, et al. , 2018) , it is equally plausible that tACS disrupts rather than enhances word report accuracy; in this case, it might be more appropriate for the minimum performance to be aligned over participants. Thus, our pre-registered approach was that for each participant, we would designate as the center bin the phase bin that most strongly deviated from a LOR of 0 (i.
Subsequently, and in parallel with data collection, we ran Monte-Carlo simulations designed to determine the optimal analysis for this and similar scientific questions  (Zoefel et al. , 2019). In this study, we simulated 2 x 1000 datasets with and without a phase effect on binary perceptual outcomes ("hit" vs "miss"). We then applied different analyses and tested which of these was best able to detect true effects, and reject absent phase effects on perceptual report. The optimal analysis was determined for combinations of different parameters that either concern the nature of the underlying phase effect on perception (effect size, effect width, effect asymmetry) or the experimental paradigm used to measure the phase effect (number of trials, number of phase bins tested etc. ). Based on these simulation results, we were able to determine the analysis optimally suited to analyze the data collected in the current study.
To test for a disruptive effect, Riecke and colleagues extracted performance from the two phase bins adjacent to the bin opposite to the one used for alignment, and compared it with sham. They reported a disruption of speech perception in those phase bins. Importantly, although the approach taken by Riecke et al (  2018 ) is immune to the analytic bias identified by  Asamoah et al. (2019) , the exclusion of the phase bin corresponding to maximal performance, as well as the bin opposite to it, makes it difficult to test for a tACS-induced enhancement or disruption of performance, respectively. Our optimized approach (see Section Data Analyses) avoids this issue by testing for enhancement and disruption using phase bins that are opposite to that used to align data over participants. Aligning to minimum performance and maximum performance allows testing of enhanced and disrupted perception, respectively.
The next step was to assess the different options for deriving PNECs that would be protective against the spread of AMR. Approaches to reduce AMR risk presented in the literature  (Bengtsson-Palme and Larsson 2016; Gullberg et al. 2011; Kümmerer and Henninger 2003; Le Page et al. 2017)  as well as discussions at key scientific meetings on the topic (AMR Industry Alliance 2018b; EDAR Environmental Dimension of Antibiotic Resistance, 2017;  Jones et al. 2018)  were considered. In the following list, we summarize the different approaches: 1) A key performance measure of antibiotic effectiveness is the minimum inhibitory concentration (MIC). The MIC is the lowest concentration of an antibiotic that inhibits 100% of the visible growth of a given strain of bacteria after 24-h incubation. The MICs are measured in clinically relevant bacteria and documented in the European Committee on Antimicrobial Susceptibility Testing database (EUCAST European Committee on Antimicrobial Susceptibility Testing 2013).
These data are useful in extrapolation to a PNEC for resistance, although it is recognized that resistance may occur at concentrations below the MIC  (Gullberg et al. 2011). Bengtsson-Palme and Larsson (2016) recommend an approach for deriving PNECs by extrapolating MIC data from the EUCAST database and applying an additional safety factor to derive PNECs for resistance. To date, this is the sole source of published PNECs for antibiotics that are specifically derived as a proxy to address resistance. We designate these as PNEC-MIC. Comparison of these values to the available literature for microbial populations in natural conditions suggests that these published PNECs are a conservative estimate of the target for protection against resistance. However, it can be noted that NOECs or EC10 values derived for some species, especially cyanobacteria used to derive a PNEC-ENV, may be lower than proposed criteria designed to be protective of AMR (i.