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  • 1
    ISSN: 1573-5117
    Keywords: algae ; phytoplankton ; Colorado River ; Grand Canyon
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract Phytoplankton distribution and abundance in eleven tributaries of the Colorado River within the Grand Canyon were investigated from April, 1975 to June, 1976. During this period a total of 56 genera and 156 species of phytoplankton was identified. Phytoplankton species of the individual tributaries were quite distinct, with only four diatom species, Diatoma vulgare, Navicula tripunctata, Nitzschia linearis and Synedra ulna, common to all the tributaries. Bright Angel Creek, Shinumo Creek and Elves Chasm were the tributaries with the most diverse algal flora, whereas Vaseys Paradise, Tapeats Creek, Deer Creek and Havasu Creek showed the lowest species richness. Elves Chasm and Diamond Creek had the highest phytoplankton numbers. Phytoplankton abundance and species richness appeared to be influenced by high turbidity, current velocity, fluctuating water levels and age of the water. Some of the dominant algal species, Biddulphia laevis, Cocconeis pediculus, Cymbella ventricosa, Epithemia sorex, Gomphonema parvulum and Synedra ulna, showed significant correlations with specific physico-chemical characteristics of the tributaries.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Annals of software engineering 1 (1995), S. 43-55 
    ISSN: 1573-7489
    Keywords: Design metrics ; design metrics analyzer ; metrics model ; software metrics ; software quality
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Metric monsters are stumbling blocks that prevent software metrics-guided methodologies from attaining product and process improvement. Metric monsters can occur during the identification, collection or application of software metrics. In our research, we have developed and tested our design metrics over a five-year period and have found them to be excellent predictors of error-prone modules. Based on this research, we will identify some of the monsters that occur in the quantitative analyses of software and its development processes, and present our approach in formulating a design metrics model that avoids these monsters. This model consists of software tools, guidelines and actions for the application of software design metrics.
    Type of Medium: Electronic Resource
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