dataesr/scientific-paragraphs-categorization huggingface dataset:
The authors rescale these scores to arrive at a composite score that measures progress in each application between 2010 and 2015. Felten et al. (2018, 2019) then link these AI applications to abilities in the US Department of Labor's O * NET database. Abilities are defined as "enduring attributes of the individual that influence performance, " e. g. , "peripheral vision" or "oral comprehension. " They enable workers to perform tasks in their jobs (such as driving a car or answering a call), but are distinct from skills in that they cannot typically be acquired or learned. Thus, linking O * NET abilities to AI applications means linking human to AI abilities. The link between O * NET abilities and AI applications (a correlation matrix) is made via an Amazon Mechanical Turk survey of 200 gig workers per AI application, who are asked whether a given AI application-e.
g. , image recognition-can be used for a certain ability-e. g. , peripheral vision 14. The correlation matrix between applications and abilities is then calculated as the share of respondents who thought that a given AI application could be used for a given ability. These abilities are subsequently linked to occupations using the O * NET database. This indicator is available for the US for 2010-2015  15. Similarly, the Suitability for Machine Learning indicator developed by Brynjolfsson and Mitchell (2017), Brynjolfsson et al. (2018) assigns a suitability for machine learning score to each of the 2,069 narrowly defined work activities from the O * NET  12  The indicator is calculated at the division level (19 industries) according to the Australian and New Zealand Standard Industrial Classification Level (ANZSIC).
, 2021). 15 At the six digit SOC 2010 occupational level, this can be aggregated across sectors and geographical regions, see Felten et al. (2021). BOX 1 | Burning Glass Technologies (BGT) online job postings data Burning Glass Technologies (BGT) collects data on online job postings by web-scraping 40 000 online job boards and company websites. It claims to cover the near-universe of online job postings. Data are currently available for Australia, Canada, New Zealand, Singapore, the United Kingdom, and the United States for the time period 2012-2020 (2014-2020 for Germany and 2018-2020 for other European Union countries). BGT extracts information such as location, sector, occupation, required skills, education, and experience levels from the text of job postings (deleting duplicates) and organizes it into up to 70 variables that can be linked to labor force surveys, providing detailed, and timely information on labor demand.
Despite its strengths, BGT data has a number of limitations: • It misses vacancies that are not posted online. Carnevale et al. (2014)  compare vacancies from survey data according to the Job Openings and Labor Turnover Survey (JOLTS) from the US Bureau of Labor Statistics, a representative survey of 16,000 US businesses, with BGT data for 2013. They find that roughly 70% of vacancies were posted online, with vacancies requiring a college degree significantly more likely to be posted online compared to jobs with lower education requirements. • There is not necessarily a direct, one-to-one correspondence between an online job ad and an actual vacancy: firms might post one job ad for several vacancies, or post job ads without firm plans to hire, e. g. , because they want to learn about available talent for future hiring needs.
• BGT data might over-represent growing firms that cannot draw on internal labor markets to the same extent as the average firm. • Higher turnover in some occupations and industries can produce a skewed image of actual labor demand since vacancies reflect a mixture of replacement demand as well as expansion. In addition, since BGT data draws on published job advertisements, it is a proxy of current vacancies, and not of hiring or actual employment. As a proxy for vacancies, BGT data performs reasonably well, although some occupations and sectors are over-represented. Hershbein and Kahn  (2018) show for the US that, compared to vacancy data from the U. S. Bureau of Labor Statistics' Job Openings and Labor Turnover Survey (JOLTS), BGT over-represents health care and social assistance, finance and insurance, and education, while under-representing accommodation, food services and construction (where informal hiring is more prevalent) as well as public administration/government.
These differences are stable across time, however, such that changes in labor demand in BGT track well with JOLTS data. Regarding hiring, they also compare BGT data with new jobs according to the Current Population Survey (CPS). BGT data strongly over-represents computer and mathematical occupations (by a factor of over four, which is a concern when looking at growth in demand for AI skills as compared to other skills), as well as occupations in management, healthcare, and business and financial operations. It under-represents all remaining occupations, including transportation, food preparation and serving, production, or construction. Cammeraat and Squicciarini (2020) argue that, because of differences in turnover across occupations, countries and time, as well as differences in the collection of national vacancy statistics, the representativeness of BGT data as an indicator for labor and skills demand should be measured against employment growth.
They compare growth rates in employment with growth rates in BGT job postings on the occupational level in the six countries for which a BGT timeline exists. They find that, across countries, the deviation between BGT and employment growth rates by occupation is lower than 10 percentage points for 65% of the employed population. They observe the biggest deviations for agricultural, forestry and fishery workers, as well as community and personal service workers, again occupations where informal hiring may be more prevalent. database that are shared across occupations (e. g. , "assisting and caring for others, " "coaching others, " "coordinating the work of others"). For these scores, they use a Machine Learning suitability rubric consisting of 23 distinct statements describing a work activity.
The authors define 14 cognitive abilities (e. g. , visual processing, planning and sequential decision-making and acting, communication, etc. ) from the psychometrics, comparative psychology, cognitive science, and AI literature  16. They link these abilities to 328 different AI benchmarks (or applications) stemming from the authors' own previous analysis and annotation of AI papers as well as from open resources such as Papers with Code 17. These sources in turn draw on data from multiple verified sources, including academic literature, review articles etc. on machine learning and AI. They use the research intensity in a specific benchmark (number of publications, news stories, blog entries etc. ) obtained from AI topics 18. Tasks are measured at the worker level using the European Working Conditions Survey (EWCS), PIAAC and the O * NET database.
They also exclude any personality traits that do not apply to machines. The abilities are: Memory processing, Sensorimotor interaction, Visual processing, Auditory processing, Attention and search, Planning, sequential decision-making and acting, Comprehension and expression, Communication, Emotion and self-control, Navigation, Conceptualisation, learning and abstraction, Quantitative and logical reasoning, Mind modelling and social interaction, and Metacognition and confidence assessment. 17 Free and open repository of machine learning code and results, which includes data from several repositories (including EFF, NLPD progress etc. ). 18 An archive kept by the by the Association for the Advancement of Artificial Intelligence (AAI). constructed to be country-invariant (as it combines data covering different countries).
Webb (2020) constructs his exposure of occupations to any technology indicator by directly comparing the text of patents from Google patents public data to the texts of job descriptions from the O * NET database to quantify the overlap between patent descriptions and job task descriptions. By limiting the patents to AI patents (using a list of key-words), this indicator can be narrowed to only apply to AI. Each particular task is then assigned a score according to the prevalence of such patents that mention this task; tasks are then aggregated to occupations.
26 This paper uses BGT data for additional results for the countries for which they are available. progress in AI, as opposed to research activity as the indicator proposed by Tolan et al. (2021). While the preferred measure for this analysis is the AI occupational exposure measure proposed by Felten et al. (2018,  2019), the paper also presents additional results using Agrawal's, Gans and Goldfarb (2019) job-posting indicator (an indicator based on job postings), as well as robustness checks using taskbased indicators by Webb (2020) and Tolan et al. (2021)   27. This section describes the construction of the main indicator, and some descriptive statistics.
Construction of the AI Occupational Exposure Measure The AI occupational exposure measure links progress in nine AI applications to 52 abilities in the US Department of Labor's O * NET database (see Section What Do These Indicators Measure? for more details). This paper extends it to 23 OECD countries by mapping the O * NET abilities to tasks from the OECD's Survey of Adult Skills (PIAAC), and then back to occupations (see Figure  2  for an illustration of the link). Specifically, instead of using the O * NET US-specific measures of an ability's "prevalence" and "importance" in an occupation, country-specific measures have been developed based on data from PIAAC, which reports the frequency with which a number of tasks are performed on the job by each surveyed individual. This information was used to measure the average frequency with which workers in each occupation (classified using two-digit ISCO-08) perform 33 tasks, and this was done separately for each country.
It indicates relative  27  While the three task-based indicators point to the same relationships between exposure to AI and employment, the results are less clearcut for the relationship between exposure to AI and average working hours. 28 The 33 tasks were then grouped into 12 broad categories to address differences in data availability between types of task. For example, "read letters, " "read bills, " and "write letters" were grouped into one category ("literacy-business"), so that this type of task does not weight more in the final score than tasks types associated with a single PIAAC task (e. g. , "dexterity" or "management"). For each ability and each occupation, 12 measures were constructed to reflect the frequency with which workers use the ability in the occupation to perform tasks under the 12 broad task categories.
Occupational Exposure to AI The kind of abilities AI has made the most progress in are disproportionately used in highly-educated, white-collar occupations. As a result, white-collar occupations requiring high levels of formal education are among the occupations with the highest exposure to AI: Science and Engineering Professionals, but also Business and Administration Professionals, Managers; Chiefs Executives; and Legal, Social, and Cultural Professionals (Figure  4 ). By contrast, occupations with the lowest exposure include occupations with an emphasis on physical tasks: Cleaners and Helpers; Agricultural Forestry, Fishery Laborers; Food Preparation Assistants and Laborers 32. The occupational intensity of some abilities is poorly estimated due to PIAAC data limitations. In particular, the 33 PIAAC tasks used in the analysis include only two non-cognitive tasks, and some of the O * NET abilities are not related to any of these tasks.
Labor Market Outcomes The analysis links occupational exposure to AI to a number of labor market outcomes: employment 37 , average hours worked 38 , the share of part-time workers, and the share of job postings that require AI-related technical skills. This section presents some descriptive statistics on labor market outcomes for the period 2012 and 2019. Two thousand twelve is chosen as the first year for the period of analysis because it ensures consistency with the measure of occupational exposure to AI, for two reasons. First, the measure of exposure to AI is based on the task composition of occupations in 2012 for most countries 39. Second,  37  Employment includes all people engaged in productive activities, whether as employees or self-employed. Employment data is taken from the Mexican National Survey of Occupation and Employment (ENOE), the European Union Labour Force Survey (EU-LFS), and the US Current Population Survey (US-CPS).
Science and Engineering Professionals experienced the largest increase, but growth was also substantial for Managers, Chief Executives, Business and Administration Professionals, and Legal, Social, Cultural Professionals. That being said, the share of job postings that require AI skills remains very low overall, with an average across occupations of 0. 24% in 2019 (against 0. 10% in 2012). These orders of magnitude are in line with Acemoglu et al. (2020) and Squicciarini and Nachtigall  (2021). 43 United Kingdom and the United States are the only countries in the sample with 2012 Burning Glass Technologies data available, thereby allowing for the examination of trends over the past decade. 44 Job postings that require AI-related technical skills are defined as those that include at least one keyword from the following list: Machine Learning, Computer Vision, Machine Vision, Deep Learning, Virtual Agents, Image Recognition, Natural Language Processing, Speech Recognition, Pattern Recognition, Object Recognition, Neural Networks, AI ChatBot, Supervised Learning, Text Mining, Support Vector Machines, Unsupervised Learning, Image Processing, Mahout, Recommender Systems, Support Vector Machines (SVM), Random Forests, Latent Semantic Analysis, Sentiment Analysis/Opinion Mining, Latent Dirichlet Allocation, Predictive Models, Kernel Methods, Keras, Gradient boosting, OpenCV, Xgboost, Libsvm, Word2Vec, Chatbot, Machine Translation, and Sentiment Classification.
The specifications that include 1digit occupational dummies only exploit variation within broad occupational groups, thereby controlling for any factors that are constant across these groups. To control for the effect of non-AI technologies, the analysis includes measures of exposure to software and industrial robots developed by Webb (2020) based on the overlap between the text of job descriptions provided in the O * NET database and the text of patents in the fields corresponding to each of these technologies 47. Offshoring is proxied by an index of offshorability developed by Firpo et al. (2011) and made available by Autor  and Dorn (2013), which measures the potential offshoring of job tasks using the average between the two variables "Face-to-Face Contact" and "On-Site Job" that Firpo et al.
(2011) derive from the O * NET database 48. This measure captures the extent to which an occupation requires direct interpersonal interaction or proximity to a specific work location 49. 47 To select software patents, Webb uses an algorithm developed by Bessen and  Hunt (2007) which requires one of the keywords "software, " "computer, " or "programme" to be present, but none of the keywords "chip, " "semiconductor, " "bus, " "circuity, " or "circuitry. " To select patents in the field of industrial robots, Webb develops an algorithm that results in the following search criteria: the title and abstract should include "robot" or "manipulate, " and the patent should not fall within the categories: "medical or veterinary science; hygiene" or "physical or chemical processes or apparatus in general".
48 Firpo et al. (2011) define "face-to-face contact" as the average value between the O * NET variables "face-to-face discussions, " "establishing and maintaining interpersonal relationships, " "assisting and caring for others, " "performing for or working directly with the public", and "coaching and developing others. " They define "on-site job" as the average between the O * NET variables "inspecting equipment, structures, or material, " "handling and moving objects, " "operating vehicles, mechanized devices, or equipment, " and the mean of "repairing The three above indices are occupation-level taskbased measures derived from the O * NET database for the United States; this analysis uses those measures for all 23 countries, assuming that the cross-occupation distribution of these indicators is similar across countries 50.
Finally, the prevalence of creative and social tasks is derived from PIAAC data. PIAAC data include the frequency with which a number of tasks are performed at the individual level. Respondents' selfassessment are based on a 5-point scale ranging from "Never" to "Every day. " This information is used to measure the average frequency with which workers in each occupation perform creative or social tasks, and this is done separately for each country  55. While employment grew faster in occupations more exposed to AI, this relationship is not robust. There is stronger evidence that AI exposure is positively related to employment growth in occupations where computer use is high. Table  2  displays the results of regression equation (1) without controls. When looking at the entire sample, the coefficient on AI exposure is both positive and statistically significant (Column 1), but the coefficient is no longer statistically significant as soon as any of the controls described in Section Empirical Strategy are included (with the exception of offshorability)  56.
High-skill occupations include: Managers; Professionals, and Technicians; and Associate Professionals. 55 In line with Nedelkoska and Quintini (2018) , creative tasks include: problem solving-simple problems, and problem solving-complex problems; and social tasks include: teaching, advising, planning for others, communicating, negotiating, influencing, and selling. For each measure, occupation-country cells are then classified into three categories depending on the average frequency with which these tasks are performed (low, medium, and high). These three categories are calculated by applying terciles across the full sample of occupation-country cells. Data are from 2012, with the exception of Hungary (2017), Lithuania (2014), and  Mexico (2017). 56 These results are not displayed but are available on request.
ACKNOWLEDGMENTS Special thanks must go to  Stijn Broecke  for his supervision of the project and to  Mark Keese  for his guidance and support throughout the project. The report also benefitted from helpful comments provided by colleagues from the  Directorate for Employment, Labour and Social Affairs  ( Andrew Green ,  Marguerita Lane ,  Luca Marcolin , and  Stefan Thewissen ) and from the  Directorate for Science, Technology and Innovation (Lea Samek). Thanks to  Katerina Kodlova  for providing publication support. The comments and feedback received from participants in the February 2021 OECD Expert Meeting on AI indicators ( Nik Dawson ,  Joe Hazell ,  Manav Raj ,  Robert Seamans ,  Alina Sorgner , and  Songul Tolan ) and the  March 2021 OECD Future of Work Seminar  are also gratefully acknowledged.
4; P-value < 0. 001) and reached a 5. 3% (-0. 43 m 2 m -2 ) decline by late July (t = -4. 2; P-value < 0. 001). The PAI then increased to a full recovery (+0. 43 m 2 m -2 ) in September (t = -1. 2; P-value = 0. 21). By contrast, the upper canopy layer b Maximum daily temperatures in the edge understory (red) and interior (dark grey) and c mean daily soil moisture (as volumetric water soil content: cm 3 water/cm 3 soil) measured continuously every 15 min in the edges (red) and in the interior of fragments (dark grey). Fifth order polynomial models were fit to the microclimate data for visualisation purposes. The shaded area corresponds to the dry season, defined as the period with accumulated monthly precipitation <200 mm month -1. showed an inverse seasonal pattern to the understory; the upper canopy PAI remained relatively stable from April-September, but experienced a 7.
The loss of upper canopy leaves in Amazonian forests at the end of the dry season is consistent with the importance of water availability for leaf development  38  , and suggests that canopy trees in these forests may be vulnerable to periods of high evaporative demand  2, 22. These results challenge the paradigm of Amazonian large trees being necessarily capable of accessing deep water and hence being primarily lightlimited  44, 45. This study presents a dataset of fine-scale, high-frequency LiDAR, elucidating the magnitude and timing of forest phenology and impact of fragmentation from one of the most important experiments on tropical forest fragmentation (BDFFP). However, the generality of our findings across years and sites, particularly across large-scale Amazonian gradients in seasonality, edaphic properties, and soil moisture, remains to be tested.
We selected a 100-ha forest fragment to investigate phenological responses within transects varying in distance from the fragment edges (0-500 m). At the community level, the forest edges of our study are dominated by a high density of early successional, fast-growth species, because of the elevated tree mortality near forest edges and seed dispersion from degraded neighbouring habitats, while the centre of the fragment comprises undisturbed primary forest  25, 39. Terrestrial laser scanning: data acquisition, registration and PAI estimation. The TLS data were acquired using a RIEGL VZ-400i system between April and October 2019 every 15 days, except between the end of April and early June when the duration between measurements was 40 days (we clarify in the analysis section how we addressed artefacts attributed to sampling effort).
9, provided by RIEGL. Given that the RIEGL VZ-400i uses onboard sensor data with an algorithm to align scans without the use of reflectors, automatic registration was done before a final adjustment of scans. We used the LAStools (rapidlasso, GmbH; Gilching, Germany) suite of computational tools to process the data. To minimise errors in the fusion of the repeated scans, we first created a common digital terrain model (DTM) at 0. 5 m resolution using a combination of ground returns from the first survey. Using an inverse distance weighting algorithm in the function grid_terrain from the "lidR" package in R, a common DTM was constructed from LiDAR ground returns. Plant area density (PAD) for all transects was then calculated using a voxel-based approach (with a 5 m buffer around each transect to maximise the PAD data).
The volume occupied by vegetation within each transect was divided into 1 m 3 voxels, and the PAD calculated for each of these voxels (Supplementary Fig. 1d ). This procedure was done in the LiDAR data voxelization software AMAPVox  75, 76. AMAPvox tracks every laser pulse through a 3D grid (voxelised space) to the last recorded hit. The effective sampling area of each laser pulse (or fraction of pulse in case of multiple hits) is computed from the theoretical beam section (a function of distance to laser and divergence of laser beam) and the remaining beam fraction entering a voxel. In case more than one hit is recorded for a given pulse, the beam section is equally distributed between the different hits of the pulse. This information is combined with the optical path length of each pulse entering a voxel to compute the local transmittance or equivalently the local attenuation per voxel.