1,721,058 research outputs found
Machine learning for weather and climate are worlds apart
Full text linkModern weather and climate models share a
common heritage, and often even components,
however they are used in different ways to answer
fundamentally different questions. As such, attempts
to emulate them using machine learning should
reflect this. While the use of machine learning
to emulate weather forecast models is a relatively
new endeavour there is a rich history of climate
model emulation. This is primarily because while
weather modelling is an initial condition problem
which intimately depends on the current state of the
atmosphere, climate modelling is predominantly a
boundary condition problem. In order to emulate the
response of the climate to different drivers therefore,
representation of the full dynamical evolution of the
atmosphere is neither necessary, or in many cases,
desirable. Climate scientists are typically interested in
different questions also. Indeed emulating the steadystate climate response has been possible for many
years and provides significant speed increases that
allow solving inverse problems for e.g. parameter
estimation. Nevertheless, the large datasets, nonlinear relationships and limited training data make
Climate a domain which is rich in interesting machine
learning challenges
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Why all emergent constraints are wrong but some are useful - a machine learning perspective
No full textOpinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective
Full text linkGlobal climate change projections are subject to substantial modelling uncertainties. A variety of emergent constraints, as well as several other statistical model evaluation approaches, have been suggested to address these uncertainties. However, they remain heavily debated in the climate science community. Still, the central idea to relate future model projections to already observable quantities has no real substitute. Here, we highlight the validation perspective of predictive skill in the machine learning community as a promising alternative viewpoint. Specifically, we argue for quantitative approaches in which each suggested constraining relationship can be evaluated comprehensively based on out-of-sample test data – on top of qualitative physical plausibility arguments that are already commonplace in the justification of new emergent constraints. Building on this perspective, we review machine learning ideas for new types of controlling-factor analyses (CFAs). The principal idea behind these CFAs is to use machine learning to find climate-invariant relationships in historical data which hold approximately under strong climate change scenarios. On the basis of existing data archives, these climate-invariant relationships can be validated in perfect-climate-model frameworks. From a machine learning perspective, we argue that such approaches are promising for three reasons: (a) they can be objectively validated for both past data and future data, (b) they provide more direct – and, by design, physically plausible – links between historical observations and potential future climates, and (c) they can take high-dimensional and complex relationships into account in the functions learned to constrain the future response. We demonstrate these advantages for two recently published CFA examples in the form of constraints on climate feedback mechanisms (clouds, stratospheric water vapour) and discuss further challenges and opportunities using the example of a rapid adjustment mechanism (aerosol–cloud interactions). We highlight several avenues for future work, including strategies to address non-linearity, to tackle blind spots in climate model ensembles, to integrate helpful physical priors into Bayesian methods, to leverage physics-informed machine learning, and to enhance robustness through causal discovery and inference
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective
Full text linkAppropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
