FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2560214594> ?p ?o ?g. }

• W2560214594 abstract "Eelgrass is a relevant sea grass species that provides important ecological services in near shore environments. The overall contribution of this species to human welfare is so important that upon threats to its permanence that associate to deleterious anthropogenic influences, a vigorous conservation effort has been recently enforced worldwide. Among restoration strategies transplanting plays a key role and the monitoring of the development of related plots is crucial to assess the restoration of the ecological features observed in donor populations. Since traditional eelgrass assessment methods are destructive their use in transplants could lead to undesirable effects such as alterations of shoot density and recruitment. Allometric methods can provide accurate proxies that sustain nondestructive estimations of variables required in the pertinent assessments. These constructs rely on extensive data sets for precise estimations of the involved parameters and also depend on precise estimations of the incumbent leaf area. The use of electronic scanning technologies for eelgrass leaf area estimation can enhance the nondestructive nature of associated allometric methods, because the necessary leaf area assessments could be obtained from digital images. But when a costly automatic leaf area meter is not available, we must rely on direct image processing, usually achieved through computationally costly Monte Carlo procedures. Previous results show that the amendment of simple genetic algorithms could drastically reduce the time required by regular Monte Carlo methods to achieve the estimation of the areas of individual eelgrass leaves. But even though this amendment, the completion of the task of measuring the areas of the leaves of a data set with an extension, as required for precise parameter estimation, still leads to a burdensome computational time. In this paper, we have explored the benefits that the addition of a master-slave parallel genetic algorithm to a Monte Carlo based estimation routine conveys in the aforementioned estimation task. We conclude that unless a suitable number of processors are involved, and also the proper mutation and crossover rates are contemplated the efficiency of the overall procedure will not be noticeably improved." @default.
• W2560214594 created "2016-12-16" @default.
• W2560214594 creator A5048223855 @default.
• W2560214594 creator A5054888301 @default.
• W2560214594 creator A5062821339 @default.
• W2560214594 creator A5090593799 @default.
• W2560214594 date "2016-12-10" @default.
• W2560214594 modified "2023-10-17" @default.
• W2560214594 title "On the Use of Parallel Genetic Algorithms for Improving the Efficiency of a Monte Carlo-Digital Image Based Approximation of Eelgrass Leaf Area I: Comparing the Performances of Simple and Master-Slaves Structures" @default.
• W2560214594 cites W1529353130 @default.
• W2560214594 cites W1571031121 @default.
• W2560214594 cites W1580237111 @default.
• W2560214594 cites W1963608599 @default.
• W2560214594 cites W1982119899 @default.
• W2560214594 cites W2000968137 @default.
• W2560214594 cites W2013351468 @default.
• W2560214594 cites W2016975070 @default.
• W2560214594 cites W2026985006 @default.
• W2560214594 cites W2031247637 @default.
• W2560214594 cites W2034232300 @default.
• W2560214594 cites W2055441489 @default.
• W2560214594 cites W2059529279 @default.
• W2560214594 cites W2075566713 @default.
• W2560214594 cites W2090181094 @default.
• W2560214594 cites W2105678117 @default.
• W2560214594 cites W2108996718 @default.
• W2560214594 cites W2110496077 @default.
• W2560214594 cites W2148641575 @default.
• W2560214594 cites W2155052599 @default.
• W2560214594 cites W2184916523 @default.
• W2560214594 cites W4302550575 @default.
• W2560214594 cites W907805460 @default.
• W2560214594 cites W952805319 @default.
• W2560214594 doi "https://doi.org/10.1007/978-3-319-47054-2_29" @default.
• W2560214594 hasPublicationYear "2016" @default.
• W2560214594 type Work @default.
• W2560214594 sameAs 2560214594 @default.
• W2560214594 citedByCount "1" @default.
• W2560214594 countsByYear W25602145942018 @default.
• W2560214594 crossrefType "book-chapter" @default.
• W2560214594 hasAuthorship W2560214594A5048223855 @default.
• W2560214594 hasAuthorship W2560214594A5054888301 @default.
• W2560214594 hasAuthorship W2560214594A5062821339 @default.
• W2560214594 hasAuthorship W2560214594A5090593799 @default.
• W2560214594 hasConcept C105795698 @default.
• W2560214594 hasConcept C11413529 @default.
• W2560214594 hasConcept C119857082 @default.
• W2560214594 hasConcept C18903297 @default.
• W2560214594 hasConcept C19499675 @default.
• W2560214594 hasConcept C33923547 @default.
• W2560214594 hasConcept C41008148 @default.
• W2560214594 hasConcept C86803240 @default.
• W2560214594 hasConcept C8880873 @default.
• W2560214594 hasConceptScore W2560214594C105795698 @default.
• W2560214594 hasConceptScore W2560214594C11413529 @default.
• W2560214594 hasConceptScore W2560214594C119857082 @default.
• W2560214594 hasConceptScore W2560214594C18903297 @default.
• W2560214594 hasConceptScore W2560214594C19499675 @default.
• W2560214594 hasConceptScore W2560214594C33923547 @default.
• W2560214594 hasConceptScore W2560214594C41008148 @default.
• W2560214594 hasConceptScore W2560214594C86803240 @default.
• W2560214594 hasConceptScore W2560214594C8880873 @default.
• W2560214594 hasLocation W25602145941 @default.
• W2560214594 hasOpenAccess W2560214594 @default.
• W2560214594 hasPrimaryLocation W25602145941 @default.
• W2560214594 hasRelatedWork W1972619326 @default.
• W2560214594 hasRelatedWork W1981618598 @default.
• W2560214594 hasRelatedWork W1985660135 @default.
• W2560214594 hasRelatedWork W2009943569 @default.
• W2560214594 hasRelatedWork W2019564919 @default.
• W2560214594 hasRelatedWork W2072018944 @default.
• W2560214594 hasRelatedWork W2142104377 @default.
• W2560214594 hasRelatedWork W2186064868 @default.
• W2560214594 hasRelatedWork W2233333459 @default.
• W2560214594 hasRelatedWork W2291146032 @default.
• W2560214594 hasRelatedWork W2808350141 @default.
• W2560214594 hasRelatedWork W2953748929 @default.
• W2560214594 hasRelatedWork W2968576668 @default.
• W2560214594 hasRelatedWork W3001606546 @default.
• W2560214594 hasRelatedWork W3013738829 @default.
• W2560214594 hasRelatedWork W3098480364 @default.
• W2560214594 hasRelatedWork W3114705373 @default.
• W2560214594 hasRelatedWork W39608697 @default.
• W2560214594 hasRelatedWork W6961585 @default.
• W2560214594 hasRelatedWork W73622280 @default.
• W2560214594 isParatext "false" @default.
• W2560214594 isRetracted "false" @default.
• W2560214594 magId "2560214594" @default.
• W2560214594 workType "book-chapter" @default.