dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, it cannot be excluded that isolated Douglas-fir trees, especially seedlings and saplings, were missed in this census, and that the occurrence of Douglas-fir stands rather than Douglas-fir trees was detected. As a consequence, our native model calibrated with this data set is not a species distribution model in the narrower sense. We nevertheless use the term "species distribution model (SDM)" hereafter because it is very commonly employed in literature to refer to niche models, including niche models calibrated on forest inventory data sets of species occurrence  (Gibson et al. 2014 , Henderson et al. 2014). We used a European forest inventory database compiled as part of the FunDivEurope research project to validate the projections of the "native" Douglas-fir niche model in Europe (functional significance of forest diversity; UE collaborative project reference 265171, FP7-Environment).
This database comprised national forest inventories from Finland, Sweden, Germany, Spain, Wallonia, France, and Romania (Appendix S1). Plot latitudes and longitudes were fuzzed up to 500 m. The data base contains 3788 plots where Douglas-fir is present for 227 671 absence plots. The FunDiv data set is, to our knowledge, the most comprehensive data set merging national forest inventories at the European scale. However, several European countries where Douglas-fir is present are not covered by its extent. Therefore, we used growth data from Douglas-fir provenance trials that were set by the IUFRO in Europe  (Kleinschmit and Bastien 1992 ) for a complementary visual validation of model predictions. Our assumption was that native Douglas-fir populations show higher growth rates in sites of high habitat suitability where climate is optimal  (Hirzel and Le Lay 2008).
Therefore, we expected growth increment to be positively correlated with climatic suitability. The IUFRO database is composed of growth data from 108 plantations that were established in the late 1960s in 30 European countries where a total of 180 provenances were planted  (Kleinschmit and Bastien 1992). The data base is characterized by an unbalanced design: not all native populations have been planted at all the trial sites, and measurements have been made at different ages. This is potentially problematic because the provenance can impact growth performance independently of the climatic conditions at the sites of introduction. Indeed, for a similar level of climatic suitability, a site where many interior populations have been planted will show a lower average growth-rate than another one where coastal populations are more represented  (Kleinschmit and Bastien 1992).
Species distribution model Presences/absences of Douglas-fir in the native range were related to the above-mentioned subset of 13 descriptors of the North American climate by means of a Random Forest algorithm. This unsupervised machine learning technique is constructed from multiple regression trees derived from bootstrap samples of the original data. Each node of each decision tree is obtained by splitting the data according to a randomly selected explanatory variable  (Breiman 2001). This method has shown utility for mapping species distributions  (Evans and Cushman 2009). We used the RandomForest 4. 6-10 R package  (Liaw and Wiener 2002). To prepare the presence/absence data set, we followed the sampling protocol from  Worrall et al. (2013). Twenty data sets were constructed, each containing 100 000 observations in order to build 20 "forests" of 500 trees.
However, the European niche had a much reduced extent compared to the North American native niche. Average temperature and precipitation encountered in the introduced range of Douglas-fir had analogues in a limited portion of the native range (Fig. 2 ). However, the seasonal variability of temperature and the distribution of precipitation differed completely from the native to the introduced range. Very limited overlap between the native and introduced niches was similarly observed when the analysis was repeated with the CRU TS3. 10 data set (Appendix S2). The inertia percentage between the North American and European ranges reached 36% and was highly significant, consistent with the visual observation of climatic niche shift.
2015)  have been related to latitudinal and elevational gradients. On the other hand, a large proportion of the European Douglas-fir forests recorded in the FundivEurope data set were planted and subjected to silvicultural management intended to ensure productive stands. Due to these silvicultural practices, the opportunities for natural selection that occur during germination and establishment were circumvented  (Aitken et al. 2008). Therefore, it is unlikely that rapid genetic adaptation of Douglas-fir under natural selection would suffice to explain the niche shift observed in the introduced range. However, genetic improvement and selection for vigor, shape and flushing lateness could have played an important role in widening the range of environmental conditions suitable for Douglas-fir.
75 µm remained in the range 10-15 %/100 nm and 6-12 %/100 nm during the time interval corresponding to the 13 and 14 Sept. VIRTIS-H data, respectively  (Rinaldi et al. 2017a) , with the lowest values corresponding to the outburst material. However, no data are available concerning the dust colour from 1 to 2 µm. Therefore, checks were done by comparing the extrapolated F scatt (586 nm) to measurements obtained at 586 nm using Figure  3. VIRTIS-H spectrum acquired on 13 September from 13. 7125 to 13. 7968 h UT. In the top spectrum, radiance values measured in the range 4. 2-5 µm were corrected as described in Sect. 3. 2, with s=0. 2. This correction is not applied in the bottom spectrum. Overplotted, in red, are the best model fits. VIRTIS-M at the same location and time than VIRTIS-H. We concluded that the bolometric albedos derived by our method are correctly estimated.
Data analysis Model fitting was applied to spectra obtained by averaging several acquisitions (6 to 11 at times showing outburstrelated excess signal, up to 50 during quiescent states). VIRTIS-H spectra are sampled over 3456 spectral channels, 432 in each grating order. Since adjacent orders cover common wavelength ranges, we have carefully selected the most reliable channels. In particular, the short wavelength range of each order is often contaminated by instrumental straylight (Bockelée-Morvan et al. 2016) and was not considered. Resulting spectra present inconsistent radiance values in the overlapping regions of the diffraction orders. The discrepancies are within 15% for non-outburst data, and within 10% for outburst data, and cannot be only attributed to instrumental straylight. For example, the mean intensity between 2.
outburst data are consistent with the silicatic grain hypothesis. However, large colour temperatures (>600 K) are measured at the peak brightness of the 14 Sept. outburst, whereas computed temperatures are < 530 K for olivine grains. Several explanations are possible. First, we did not consider grains smaller than 100 nm in radius, whereas we cannot exclude their presence. These grains might reach higher temperatures than 100-nm grains, though this is not expected from model calculations  (Lasue et al. 2007 ). In our model, 100-nm grains are compact (P = 0). Considering instead that these grains are aggregates, their temperature would be higher than the temperature of the equivalent sphere  (Xing & Hanner 1997). Another possibility is that the spectral indices of amorphous olivine differ significantly from those of the refractory material of cometary grains.
Interestingly, the same strong line at 3. 37 µm (2965-2968 cm -1 ) is detected during the two 67P outbursts. Lines of C 2 H 6 and CH 3 OH fall within this range  (Dello Russo et al. 2006 ), but stronger lines from these species are not observed. We postpone the analysis of this band to a future pa- per, when data with an improved calibration will be available. The VIRTIS data suggest abundant release of organics during 67P outbursts. In 9P/Tempel 1 Deep Impact ejecta, enhanced C 2 H 6 infrared emission was observed, higher than for CH 3 OH and H 2 O lines  (Mumma et al. 2005; DiSanti et al. 2007 ).
SUMMARY AND DISCUSSION We have presented an analysis of the 2-5 µm dust continuum radiation from comet 67P measured at 1. 3 AU from the Sun with the VIRTIS-H instrument. We focused on data acquired on 13 and 14 September 2015 at the time of two important 67P outbursts, with the aim to characterise the properties of the dust ejecta, in addition to those of the quiescent coma. A discussion of the results obtained in this paper is given below. Comparison of the quiescent coma of 67P with other comets. The coma of 67P at 1. 3 AU from the Sun observed at a phase angle of ∼100 • exhibits a bolometric albedo of Sept. 13. 712-13. 792 h UT (middle) and 14 Sept. 18. 863-18. 897 h UT (bottom). The spectrum labelled quiescent is the average of 850 acquisitions acquired before the 13 Sept. outburst. The dust continuum emission has been removed.
3 AU from the Sun, having in mind model approximations and assumptions. Overall, assuming that compact grains are responsible for the superheat, a size index in the range 2. 5-3 provides satisfactory results for both the superheat, colour and bolometric albedo. The low value β = 2. 5 is obtained for a minimum particle-radius a min of 0. 2 µm, whereas β = 3 is obtained for a min = 0. 5 µm. A somewhat shallower size index, in the range 2-2. 5, is deduced when considering a mixture of compact particles and fractal aggregates, with ratio 75:25 in number. GIADA collections combined to OSIRIS data obtained at 2. 0 AU pre-perihelion are consistent with a size index of 2. 2-2. 6 (± 0. 3) in the size range 80 µm to 2 mm  (Rinaldi et al. 2017b). GIADA collections for the 3. 4-3. 6 AU pre-perihelion range show a similar size index (∼ 2,  Rotundi et al.
ACKNOWLEDGEMENTS The authors would like to thank the following institutions and agencies, which supported this work:  Italian Space Agency (ASI -Italy) ,  Centre National d'Etudes Spatiales (CNES-France) ,  Deutsches Zentrum für Luft-und Raumfahrt  ( DLR-Germany ),  National Aeronautic and Space Administration (NASA-USA ). VIRTIS was built by a consortium from  Italy, France and Germany , under the scientific responsibility of the  Istituto di Astrofisica e Planetologia Spaziali of INAF, Rome (IT) , which lead also the scientific operations. The VIRTIS instrument development for ESA has been funded and managed by  ASI , with contributions from  Observatoire de Meudon  financed by  CNES  and from  DLR. The instrument industrial prime contractor was former  Officine Galileo , now  Selex ES  ( Finmeccanica Group ) in  Campi Bisenzio , Florence, IT.
Methods Medical record and literature review was performed in accordance with local institutional review boards. This project obtained data (from January   19  PRACTALL consensus report,  20  or other published guidelines); (5) the criteria for a positive reaction; (6) the waiting period after the last dose of an OFC; (7) the use of the prick and prick (the use fresh food items vs standardized extracts) testing method used before OFC; (8) reaction characteristics; (9) the number of challenges in which epinephrine, antihistamines, or steroids was given; and (10) demographics of patients challenged (age, ethnicity, sex, and prior history of food reactions and anaphylaxis). Anaphylaxis was defined according to the criteria outlined by Sampson et al,  21  with some discretion based on clinical judgment of the supervising physician.
The proportion of OFCs that resulted in anaphylaxis did not change over time (P ¼. 45). Just as was seen in nonanaphylactic reaction rates, patients with anaphylaxis were more likely to be males (P ¼. 008) and white (P ¼. 007) compared with patients who did not have anaphylaxis. Meta-analysis was used to estimate the proportion of treatment (antihistamine, epinephrine, steroids) and 95% CI by year for all OFCs performed (Fig  3A-C ). Of the 6,377 OFCs, treatment data were obtained for 3,127 OFCs. There were 451 of 3,127 challenges that required treatment (14. 4%). Antihistamines were used most often when reactions occurred with OFC 76% of the time, whereas epinephrine was used 14% of the time. Steroids were only used 11% of the time. The overall percentage of OFCs that received antihistamines was 10%, and the overall percentages receiving epinephrine and steroids were each 1%.
Discussion The prevalence of food allergies has been increasing in the United States. 23 However, not many studies have determined whether this increase is accompanied by increased OFC-proven reaction rates. This study provides data from the largest number of OFCs in 5 geographically distributed locations in the United States. The numbers of overall OFCs performed during a specific period and foods challenged, along with the associated outcomes and interventions, were examined. The severity of OFC reactions in this setting is low, with 2% of OFCs resulting in anaphylaxis and 14% in mild to moderate allergic reactions, requiring no epinephrine. Food challenges increase the quality of life of food allergic patients,  24  even if they test positive. 25 There is great cost to delaying the performance of OFCs, up to $4,184 per 12 months of delay.
V. and D. R. N. , personal communication, 2017). Prick and prick testing of food before OFC has been recommended in the most recent practice parameters as a valid way to determine food allergy for fresh fruits and vegetables or in other foods if extracts are not available. 29 he pooled data indicate an overall estimated rate of nonanaphylactic reactions of 14% during the study period. Reactions noted in the study population included hives, abdominal pain, shortness of breath, and emesis. These reactions were mild to moderate and most of the time treated with diphenhydramine. Because the rate of these mild reactions that resulted in use of diphenhydramine is approximately 10%, it is reasonable to consider the OFC as a relatively safe procedure. Even though shortness of breath was encountered as a symptom of mild reaction, this is a symptom that, in the absence of wheezing, cough, or stridor, could be subjective and is not considered a severe symptom by the PRACTALL guidelines.
Information retrieval from textual data focuses on the construction of vocabularies that contain weighted term tuples. Such vocabularies can then be exploited by various text analysis algorithms to extract new knowledge, e. g. , top-k keywords, top-k documents, etc. Topk keywords are casually used for various purposes, are often computed on-the-fly, and thus must be efficiently computed. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present a top-k keywords benchmark, T 2 K 2 , which features a real tweet dataset and queries with various complexities and selectivities. T 2 K 2 helps evaluate weighting schemes and database implementations in terms of computing performance.
Yet, the term weighting process remains very costly, because each time a query is issued, at least one pass through all documents is needed. To compare combinations of weighting schemes, computing strategies and physical implementations, benchmarking is customary. However, to the best of our knowledge, there exists no benchmark for this purpose. Hence, we propose in this paper the Twitter Top-K Keywords Benchmark (T 2 K 2 ), which features a real tweet dataset and queries with various complexities and selectivities. We designed T 2 K 2 to be somewhat generic, i. e. , it can compare various weighting schemes, database logical and physical implementations and even text analytics platforms  [18]  in terms of computing efficiency. As a proof of concept of T 2 K 2 's relevance and genericity, we show how to implement the TF-IDF and Okapi BM25 weighting schemes, on one hand, and relational and document-oriented database instantiations, on the other hand.
Data Model The base dataset we use is a corpus of 2 500 000 tweets that was collected using Twitter's REST API to read and gather data. Moreover, we applied preprocessing steps to the raw corpus to extract the additional information needed to build a weighted vocabulary: 1) extract all tags and remove links; 2) expand contractions, i. e. , shortened versions of the written and spoken forms of a word, syllable, or word group, created by omission of internal letters and sounds  [2] , e. g. , "it's" becomes "it is"; 3) extract sentences and remove punctuation in each sentence, creating a clean text; 4) for each sentence, extract lemmas and create a lemma text; 5) for each lemma t in tweet d, compute the number of co-occurrences f t,d and term frequency T F (t, d), which normalizes f t,d. T 2 K 2 database's conceptual model (Figure  1 ) represents all the information extracted after the text preprocessing steps.
Information about tweet Author are a unique identifier, first name, last name and age. Information about author Gender is stored in a different entity to minimize the number of duplicates of gender type. Documents are identified by the tweet's unique identifier and store the raw tweet text, clean text, lemma text, and the tweet's creation date. Writes is the relationship that associates a tweet to its author. Tweet location is stored in the Geo Location entity to avoid duplicates again. Word bears a unique identifier and the actual lemma. Finally, weights f t,d and T F (t, d) for each lemma and each document are stored in the Vocabulary relationship. The initial 2 500 000 tweet corpus is split into 5 different datasets that all keep an equal balance between the number of tweets for both genders, location and date.
Workload Model The queries used in T 2 K 2 are designed to achieve two goals: 1) compute different term weighting schemes using aggregation functions and return the top-k keywords; 2) test the performance of different database management systems. T 2 K 2 queries are sufficient for achieving these goals, because they test the query execution plan, internal caching and the way they deal with aggregation. More precisely, they take different group by attributes into account and aggregate the information to compute weighting schemes for top-k keywords. T 2 K 2 features four queries Q1 to Q4 that compute top-k keywords w. r. t. constraint(s): c 1 (Q1), c 1 ∧ c 2 (Q2), c 1 ∧ c 3 (Q3), c 1 ∧ c 2 ∧ c 3 (Q4.
Performance Metrics and Execution Protocol We use each query's response time t(Q i ) as metrics in T 2 K 2. Given scale factor SF , all queries Q1 to Q4 are executed 40 times, which is sufficient according to the central limit theorem. Average response times and standard deviations are computed for t(Q i ). All executions are warm runs, i. e. , either caching mechanisms must be deactivated, or a cold run of Q1 to Q4 must be executed once (but not taken into account in the benchmark's results) to fill in the cache. Queries must be written in the native scripting language of the target database system and executed directly inside said system using the command line interpreter.
T 2 K 2 Proof of Concept In this section, we aim at illustrating how T 2 K 2 works and at demonstrating that it can adequately benchmark what it is designed for, i. e. , weighting schemes and database implementations. For this sake, we first compare the TF-IDF and Okapi BM25 weighting schemes in terms of computing efficiency. Second, we seek to determine whether a document-oriented database is a better solution than in a relational databases when computing a given term weighting scheme.
Document-oriented Implementation Database In a Document Oriented Database Management System (DODBMS), all information is typically stored in a single collection. The many-to-many Vocabulary relationship from Figure  1  is modeled as a nested document for each record. The information about user and date become single fields in a document, while the location becomes an array. Figure  3  presents an example of the DODBMS document. Queries In DODBMSs, user-defined (e. g. , JavaScript) functions are used to compute top-k keywords. The TF-IDF weight can take advantage of both native database aggregation (NA) and MapReduce (MR). However, due to the multitude of parameters involved and the calculations needed for the Okapi BM25 weighting scheme, the NA method is usually difficult to develop. Thus, we recommend to only use MR in benchmark runs.
3 : Sample DODBMS Document various text analytics tasks, we think T 2 K 2 fulfills this criterion. We also show in Section 4 that our benchmark achieves what it is designed for. Portability: The benchmark must be reusable to test the performances of different database systems. We successfully instantiated T 2 K 2 within two types of database systems, namely relational and document-oriented systems. Simplicity: The benchmark must be feasible and must not require too many resources. We designed T 2 K 2 with this criterion in mind (Section 3), which is particularly important for reproducibility. We notably made up parameters that are easy to setup. Scalability: The benchmark must adapt to small or large computer architectures. By introducing scale factor SF , we allow users to simply parameterize T 2 K 2 and achieve some scaling, though it could be pushed further in terms of data volume.
i=1 T F IDF (t, d i , D) of all TF-IDFs and the sum S Okapi(t, d, D) = N i=1 Okapi(t, d i , D) of all Okapi BM25 weights constitute the term's weights that are used to construct the list of top-k keywords. 4. 2 Relational Implementations Database The logical relational schema used in both relational databases management systems (Figure 2) directly translates the conceptual schema from Figure 1.
N i=1 T F IDF (t, d i , D) of all TF-IDFs and the sum S Okapi(t, d, D) = N i=1 Okapi(t, d i , D) of all Okapi BM25 weights constitute the term's weights that are used to construct the list of top-k keywords. 4. 2 Relational Implementations Database The logical relational schema used in both relational databases management systems (Figure 2) directly translates the conceptual schema from Figure 1.
Introduction: To develop occupational therapy's evidence base and improve its clinical outcomes, occupational therapists must increase their research involvement. Barriers to research consumption and leadership are well documented, but those relating to delivering research interventions, less so. Yet, interventions need to be researched within practice to demonstrate their clinical effectiveness. This study aims to improve understanding of challenges and enablers experienced by occupational therapists who deliver interventions within research programmes. Method: Twenty-eight occupational therapists who participated in the Valuing Active Life in Dementia (VALID) research programme reported their experiences in five focus groups. Data were analysed thematically to identify key and subthemes.
This development phase involved occupational therapists participating in a number of research activities (detailed in Table  1 ). Firstly, occupational therapists were trained in the COTiD intervention,  (Graff et al. , 2006 (Graff et al. , , 2007)). Secondly, they delivered this intervention to people living with dementia and their family carers. In some sites this also involved occupational therapists recruiting research participants and seeking informed consent. Finally, occupational therapists provided data about their activities within, and experiences of, the VALID research study. This included video recording intervention sessions, completing questionnaires about their own skill acquisition, detailing the time and content of the occupational therapy sessions provided and participating in a focus group.