Finding refactorings via change metrics

Conformational Change Metrics of the Ryanodine Receptor Studied by CryoEM

Microscopy and Microanalysis ◽

10.1017/s1431927611001346 ◽

2011 ◽

Vol 17 (S2) ◽

pp. 94-95

Author(s):

T Steele ◽

M Samsó

Keyword(s):

Conformational Change ◽

Ryanodine Receptor ◽

Change Metrics

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

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Enablers of Change: Metrics

Encyclopedia of Supply Chain Management ◽

10.1081/e-escm-120048116 ◽

2015 ◽

pp. 379-380

Keyword(s):

Change Metrics

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From ranking algorithms to ‘ranking cultures’

Convergence The International Journal of Research into New Media Technologies ◽

10.1177/1354856517736982 ◽

2018 ◽

Vol 24 (1) ◽

pp. 50-68 ◽

Cited By ~ 31

Author(s):

Bernhard Rieder ◽

Ariadna Matamoros-Fernández ◽

Òscar Coromina

Keyword(s):

Qualitative Analysis ◽

Social Power ◽

Causal Explanation ◽

Search Results ◽

Ranking Algorithms ◽

Online Platforms ◽

Change Metrics ◽

Empirical Approaches ◽

Over Time

Algorithms, as constitutive elements of online platforms, are increasingly shaping everyday sociability. Developing suitable empirical approaches to render them accountable and to study their social power has become a prominent scholarly concern. This article proposes an approach to examine what an algorithm does, not only to move closer to understanding how it works, but also to investigate broader forms of agency involved. To do this, we examine YouTube’s search results ranking over time in the context of seven sociocultural issues. Through a combination of rank visualizations, computational change metrics and qualitative analysis, we study search ranking as the distributed accomplishment of ‘ranking cultures’. First, we identify three forms of ordering over time – stable, ‘newsy’ and mixed rank morphologies. Second, we observe that rankings cannot be easily linked back to popularity metrics, which highlights the role of platform features such as channel subscriptions in processes of visibility distribution. Third, we find that the contents appearing in the top 20 results are heavily influenced by both issue and platform vernaculars. YouTube-native content, which often thrives on controversy and dissent, systematically beats out mainstream actors in terms of exposure. We close by arguing that ranking cultures are embedded in the meshes of mutually constitutive agencies that frustrate our attempts at causal explanation and are better served by strategies of ‘descriptive assemblage’.

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Assessing Landscape Fire Hazard by Multitemporal Automatic Classification of Landsat Time Series Using the Google Earth Engine in West-Central Spain

Forests ◽

10.3390/f10060518 ◽

2019 ◽

Vol 10 (6) ◽

pp. 518 ◽

Cited By ~ 4

Author(s):

Natalia Quintero ◽

Olga Viedma ◽

Itziar R. Urbieta ◽

José M. Moreno

Keyword(s):

Time Series ◽

Forest Fires ◽

Fire Hazard ◽

Sustainable Use ◽

Google Earth ◽

Burned Area ◽

Change Metrics ◽

Google Earth Engine ◽

The Stability

Annual Land Use and Land Cover (LULC) maps are needed to identify the interaction between landscape changes and wildland fires. Objectives: In this work, we determined fire hazard changes in a representative Mediterranean landscape through the classification of annual LULC types and fire perimeters, using a dense Landsat Time Series (LTS) during the 1984–2017 period, and MODIS images. Methods: We implemented a semiautomatic process in the Google Earth Engine (GEE) platform to generate annual imagery free of clouds, cloud shadows, and gaps. We compared LandTrendr (LT) and FormaTrend (FT) algorithms that are widely used in LTS analysis to extract the pixel tendencies and, consequently, assess LULC changes and disturbances such as forest fires. These algorithms allowed us to generate the following change metrics: type, magnitude, direction, and duration of change, as well as the prechange spectral values. Results and conclusions: Our results showed that the FT algorithm was better than the LT algorithm at detecting low-severity changes caused by fires. Likewise, the use of the change metrics’ type, magnitude, and direction of change increased the accuracy of the LULC maps by 4% relative to the ones obtained using only spectral and topographic variables. The most significant hazardous LULC change processes observed were: deforestation and degradation (mainly by fires), encroachment (i.e., invasion by shrublands) due to agriculture abandonment and forest fires, and hazardous densification (from open forests and agroforestry areas). Although the total burned area has decreased significantly since 1985, the landscape fire hazard has increased since the second half of the twentieth century. Therefore, it is necessary to implement fire management plans focused on the sustainable use of shrublands and conifer forests; this is because the stability in these hazardous vegetation types is translated into increasing fuel loads, and thus an elevated landscape fire hazard.

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