15 resultados para art evaluation

em CentAUR: Central Archive University of Reading - UK


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Earth system models are increasing in complexity and incorporating more processes than their predecessors, making them important tools for studying the global carbon cycle. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes, with coupled climate-carbon cycle models that represent land-use change simulating total land carbon stores by 2100 that vary by as much as 600 Pg C given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous model evaluation methodologies. Here we assess the state-of-the-art with respect to evaluation of Earth system models, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeo data and (ii) metrics for evaluation, and discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute towards the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but it is also a challenge, as more knowledge about data uncertainties is required in order to determine robust evaluation methodologies that move the field of ESM evaluation from "beauty contest" toward the development of useful constraints on model behaviour.

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Earth system models (ESMs) are increasing in complexity by incorporating more processes than their predecessors, making them potentially important tools for studying the evolution of climate and associated biogeochemical cycles. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes. For example, coupled climate–carbon cycle models that represent land-use change simulate total land carbon stores at 2100 that vary by as much as 600 Pg C, given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous methods of model evaluation. Here we assess the state-of-the-art in evaluation of ESMs, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeodata and (ii) metrics for evaluation. We note that the practice of averaging results from many models is unreliable and no substitute for proper evaluation of individual models. We discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute to the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but also presents a challenge. Improved knowledge of data uncertainties is still necessary to move the field of ESM evaluation away from a "beauty contest" towards the development of useful constraints on model outcomes.

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While state-of-the-art models of Earth's climate system have improved tremendously over the last 20 years, nontrivial structural flaws still hinder their ability to forecast the decadal dynamics of the Earth system realistically. Contrasting the skill of these models not only with each other but also with empirical models can reveal the space and time scales on which simulation models exploit their physical basis effectively and quantify their ability to add information to operational forecasts. The skill of decadal probabilistic hindcasts for annual global-mean and regional-mean temperatures from the EU Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project is contrasted with several empirical models. Both the ENSEMBLES models and a “dynamic climatology” empirical model show probabilistic skill above that of a static climatology for global-mean temperature. The dynamic climatology model, however, often outperforms the ENSEMBLES models. The fact that empirical models display skill similar to that of today's state-of-the-art simulation models suggests that empirical forecasts can improve decadal forecasts for climate services, just as in weather, medium-range, and seasonal forecasting. It is suggested that the direct comparison of simulation models with empirical models becomes a regular component of large model forecast evaluations. Doing so would clarify the extent to which state-of-the-art simulation models provide information beyond that available from simpler empirical models and clarify current limitations in using simulation forecasting for decision support. Ultimately, the skill of simulation models based on physical principles is expected to surpass that of empirical models in a changing climate; their direct comparison provides information on progress toward that goal, which is not available in model–model intercomparisons.

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Very large scale scheduling and planning tasks cannot be effectively addressed by fully automated schedule optimisation systems, since many key factors which govern 'fitness' in such cases are unformalisable. This raises the question of an interactive (or collaborative) approach, where fitness is assigned by the expert user. Though well-researched in the domains of interactively evolved art and music, this method is as yet rarely used in logistics. This paper concerns a difficulty shared by all interactive evolutionary systems (IESs), but especially those used for logistics or design problems. The difficulty is that objective evaluation of IESs is severely hampered by the need for expert humans in the loop. This makes it effectively impossible to, for example, determine with statistical confidence any ranking among a decent number of configurations for the parameters and strategy choices. We make headway into this difficulty with an Automated Tester (AT) for such systems. The AT replaces the human in experiments, and has parameters controlling its decision-making accuracy (modelling human error) and a built-in notion of a target solution which may typically be at odds with the solution which is optimal in terms of formalisable fitness. Using the AT, plausible evaluations of alternative designs for the IES can be done, allowing for (and examining the effects of) different levels of user error. We describe such an AT for evaluating an IES for very large scale planning.

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Under the Public Bodies Bill 2010, the HFEA, cornerstone in the regulation of assisted reproduction technologies (ART) for the last twenty years, is due to be abolished. This implies that there is no longer a need for a dedicated regulator for ART and that the existing roles of the Authority as both operational compliance monitor, and instance of ethical evaluation, may be absorbed by existing healthcare regulators. This article presents a timely analysis of these disparate functions of the HFEA, charting reforms adopted in 2008 and assessing the impact of the current proposals. Taking assisted conception treatment as the focus activity, it will be shown that the last few years have seen a concentration on the HFEA as a technical regulator based upon the principles of Better Regulation, with little analysis of how the ethical responsibility of the Authority fits into this framework. The current proposal to abolish the HFEA continues to fail to address this crucial question. Notwithstanding the fact that the scope of the Authority's ethical role may be questioned, its abolition requires that the Government consider what alternatives exists - or need to be put in place - to provide both responsive operational regulation and a forum for ethical reflection and decision-making in an area which continues to pose regulatory challenges

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An extensive off-line evaluation of the Noah/Single Layer Urban Canopy Model (Noah/SLUCM) urban land-surface model is presented using data from 15 sites to assess (1) the ability of the scheme to reproduce the surface energy balance observed in a range of urban environments, including seasonal changes, and (2) the impact of increasing complexity of input parameter information. Model performance is found to be most dependent on representation of vegetated surface area cover; refinement of other parameter values leads to smaller improvements. Model biases in net all-wave radiation and trade-offs between turbulent heat fluxes are highlighted using an optimization algorithm. Here we use the Urban Zones to characterize Energy partitioning (UZE) as the basis to assign default SLUCM parameter values. A methodology (FRAISE) to assign sites (or areas) to one of these categories based on surface characteristics is evaluated. Using three urban sites from the Basel Urban Boundary Layer Experiment (BUBBLE) dataset, an independent evaluation of the model performance with the parameter values representative of each class is performed. The scheme copes well with both seasonal changes in the surface characteristics and intra-urban heterogeneities in energy flux partitioning, with RMSE performance comparable to similar state-of-the-art models for all fluxes, sites and seasons. The potential of the methodology for high-resolution atmospheric modelling application using the Weather Research and Forecasting (WRF) model is highlighted. This analysis supports the recommendations that (1) three classes are appropriate to characterize the urban environment, and (2) that the parameter values identified should be adopted as default values in WRF.

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We have used the BIOME4 biogeography–biochemistry model and comparison with palaeovegetation data to evaluate the response of six ocean–atmosphere general circulation models to mid-Holocene changes in orbital forcing in the mid- to high-latitudes of the northern hemisphere. All the models produce: (a) a northward shift of the northern limit of boreal forest, in response to simulated summer warming in high-latitudes. The northward shift is markedly asymmetric, with larger shifts in Eurasia than in North America; (b) an expansion of xerophytic vegetation in mid-continental North America and Eurasia, in response to increased temperatures during the growing season; (c) a northward expansion of temperate forests in eastern North America, in response to simulated winter warming. The northward shift of the northern limit of boreal forest and the northward expansion of temperate forests in North America are supported by palaeovegetation data. The expansion of xerophytic vegetation in mid-continental North America is consistent with palaeodata, although the extent may be over-estimated. The simulated expansion of xerophytic vegetation in Eurasia is not supported by the data. Analysis of an asynchronous coupling of one model to an equilibrium-vegetation model suggests vegetation feedback exacerbates this mid-continental drying and produces conditions more unlike the observations. Not all features of the simulations are robust: some models produce winter warming over Europe while others produce winter cooling. As a result, some models show a northward shift of temperate forests (consistent with, though less marked than, the expansion shown by data) and others produce a reduction in temperate forests. Elucidation of the cause of such differences is a focus of the current phase of the Palaeoclimate Modelling Intercomparison Project.

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Enterprise Architecture (EA) has been recognised as an important tool in modern business management for closing the gap between strategy and its execution. The current literature implies that for EA to be successful, it should have clearly defined goals. However, the goals of different stakeholders are found to be different, even contradictory. In our explorative research, we seek an answer to the questions: What kind of goals are set for the EA implementation? How do the goals evolve during the time? Are the goals different among stakeholders? How do they affect the success of EA? We analysed an EA pilot conducted among eleven Finnish Higher Education Institutions (HEIs) in 2011. The goals of the pilot were gathered from three different stages of the pilot: before the pilot, during the pilot, and after the pilot, by means of a project plan, interviews during the pilot and a questionnaire after the pilot. The data was analysed using qualitative and quantitative methods. Eight distinct goals were recognised by the coding: Adopt EA Method, Build Information Systems, Business Development, Improve Reporting, Process Improvement, Quality Assurance, Reduce Complexity, and Understand the Big Picture. The success of the pilot was analysed statistically using the scale 1-5. Results revealed that goals set before the pilot were very different from those mentioned during the pilot, or after the pilot. Goals before the pilot were mostly related to expected benefits from the pilot, whereas the most important result was to adopt the EA method. Results can be explained by possibly different roles of respondents, which in turn were most likely caused by poor communication. Interestingly, goals mentioned by different stakeholders were not limited to their traditional areas of responsibility. For example, in some cases Chief Information Officers' goals were Quality Assurance and Process Improvement, whereas managers’ goals were Build Information Systems and Adopt EA Method. This could be a result of a good understanding of the meaning of EA, or stakeholders do not regard EA as their concern at all. It is also interesting to notice that regardless of the different perceptions of goals among stakeholders, all HEIs felt the pilot to be successful. Thus the research does not provide support to confirm the link between clear goals and success.

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Is the concept of a work of art an evaluative concept: does its application imply a positive evaluation? I shall discuss this question by considering two opposing attempts at defining art, namely the Institutional Theory and the view that art is a functional concept. I shall argue that the concept of art does not imply an unconditionally positive evaluation, but that art is a prestige concept. Moreover, it will be shown that functional definitions of art are flawed.

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We report a straightforward methodology for the fabrication of high-temperature thermoelectric (TE) modules using commercially available solder alloys and metal barriers. This methodology employs standard and accessible facilities that are simple to implement in any laboratory. A TE module formed by nine n-type Yb x Co4Sb12 and p-type Ce x Fe3CoSb12 state-of-the-art skutterudite material couples was fabricated. The physical properties of the synthesized skutterudites were determined, and the module power output, internal resistance, and thermocycling stability were evaluated in air. At a temperature difference of 365 K, the module provides more than 1.5 W cm−3 volume power density. However, thermocycling showed an increase of the internal module resistance and degradation in performance with the number of cycles when the device is operated at a hot-side temperature higher than 573 K. This may be attributed to oxidation of the skutterudite thermoelements.

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The evaluation of forecast performance plays a central role both in the interpretation and use of forecast systems and in their development. Different evaluation measures (scores) are available, often quantifying different characteristics of forecast performance. The properties of several proper scores for probabilistic forecast evaluation are contrasted and then used to interpret decadal probability hindcasts of global mean temperature. The Continuous Ranked Probability Score (CRPS), Proper Linear (PL) score, and IJ Good’s logarithmic score (also referred to as Ignorance) are compared; although information from all three may be useful, the logarithmic score has an immediate interpretation and is not insensitive to forecast busts. Neither CRPS nor PL is local; this is shown to produce counter intuitive evaluations by CRPS. Benchmark forecasts from empirical models like Dynamic Climatology place the scores in context. Comparing scores for forecast systems based on physical models (in this case HadCM3, from the CMIP5 decadal archive) against such benchmarks is more informative than internal comparison systems based on similar physical simulation models with each other. It is shown that a forecast system based on HadCM3 out performs Dynamic Climatology in decadal global mean temperature hindcasts; Dynamic Climatology previously outperformed a forecast system based upon HadGEM2 and reasons for these results are suggested. Forecasts of aggregate data (5-year means of global mean temperature) are, of course, narrower than forecasts of annual averages due to the suppression of variance; while the average “distance” between the forecasts and a target may be expected to decrease, little if any discernible improvement in probabilistic skill is achieved.

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The objective of this article is to study the problem of pedestrian classification across different light spectrum domains (visible and far-infrared (FIR)) and modalities (intensity, depth and motion). In recent years, there has been a number of approaches for classifying and detecting pedestrians in both FIR and visible images, but the methods are difficult to compare, because either the datasets are not publicly available or they do not offer a comparison between the two domains. Our two primary contributions are the following: (1) we propose a public dataset, named RIFIR , containing both FIR and visible images collected in an urban environment from a moving vehicle during daytime; and (2) we compare the state-of-the-art features in a multi-modality setup: intensity, depth and flow, in far-infrared over visible domains. The experiments show that features families, intensity self-similarity (ISS), local binary patterns (LBP), local gradient patterns (LGP) and histogram of oriented gradients (HOG), computed from FIR and visible domains are highly complementary, but their relative performance varies across different modalities. In our experiments, the FIR domain has proven superior to the visible one for the task of pedestrian classification, but the overall best results are obtained by a multi-domain multi-modality multi-feature fusion.

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Despite the importance of dust aerosol in the Earth system, state-of-the-art models show a large variety for North African dust emission. This study presents a systematic evaluation of dust emitting-winds in 30 years of the historical model simulation with the UK Met Office Earth-system model HadGEM2-ES for the Coupled Model Intercomparison Project Phase 5. Isolating the effect of winds on dust emission and using an automated detection for nocturnal low-level jets (NLLJs) allow an in-depth evaluation of the model performance for dust emission from a meteorological perspective. The findings highlight that NLLJs are a key driver for dust emission in HadGEM2-ES in terms of occurrence frequency and strength. The annually and spatially averaged occurrence frequency of NLLJs is similar in HadGEM2-ES and ERA-Interim from the European Centre for Medium-Range Weather Forecasts. Compared to ERA-Interim, a stronger pressure ridge over northern Africa in winter and the southward displaced heat low in summer result in differences in location and strength of NLLJs. Particularly the larger geostrophic winds associated with the stronger ridge have a strengthening effect on NLLJs over parts of West Africa in winter. Stronger NLLJs in summer may rather result from an artificially increased mixing coefficient under stable stratification that is weaker in HadGEM2-ES. NLLJs in the Bodélé Depression are affected by stronger synoptic-scale pressure gradients in HadGEM2-ES. Wintertime geostrophic winds can even be so strong that the associated vertical wind shear prevents the formation of NLLJs. These results call for further model improvements in the synoptic-scale dynamics and the physical parametrization of the nocturnal stable boundary layer to better represent dust-emitting processes in the atmospheric model. The new approach could be used for identifying systematic behavior in other models with respect to meteorological processes for dust emission. This would help to improve dust emission simulations and contribute to decreasing the currently large uncertainty in climate change projections with respect to dust aerosol.

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This paper describes the development and basic evaluation of decadal predictions produced using the HiGEM coupled climate model. HiGEM is a higher resolution version of the HadGEM1 Met Office Unified Model. The horizontal resolution in HiGEM has been increased to 1.25◦ × 0.83◦ in longitude and latitude for the atmosphere, and 1/3◦ × 1/3◦ globally for the ocean. The HiGEM decadal predictions are initialised using an anomaly assimilation scheme that relaxes anomalies of ocean temperature and salinity to observed anomalies. 10 year hindcasts are produced for 10 start dates (1960, 1965,..., 2000, 2005). To determine the relative contributions to prediction skill from initial conditions and external forcing, the HiGEM decadal predictions are compared to uninitialised HiGEM transient experiments. The HiGEM decadal predictions have substantial skill for predictions of annual mean surface air temperature and 100 m upper ocean temperature. For lead times up to 10 years, anomaly correlations (ACC) over large areas of the North Atlantic Ocean, the Western Pacific Ocean and the Indian Ocean exceed values of 0.6. Initialisation of the HiGEM decadal predictions significantly increases skill over regions of the Atlantic Ocean,the Maritime Continent and regions of the subtropical North and South Pacific Ocean. In particular, HiGEM produces skillful predictions of the North Atlantic subpolar gyre for up to 4 years lead time (with ACC > 0.7), which are significantly larger than the uninitialised HiGEM transient experiments.