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- W2051249112 abstract "The question of whether growth in bivalves is predictable in terms of environmental condition is addressed directly by trying to infer juvenile scallop growth from environment data within and between two locations in the Baie des Chaleurs, Québec. Using models based on either self-organizing models – the group method of data handling (GMDH) algorithm - or multilinear regressions, scallop growth was found to be predictable. GMDH models lead consistently to better predictions than multilinear regressions and could thus be a useful alternative tool in managing scallop fisheries aquaculture. Temperature and food availability were the most prominent variables included in the GMDHD models, emphisizing their importance as physical determinants of scallop growth." @default.
- W2051249112 created "2016-06-24" @default.
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- W2051249112 date "1994-11-01" @default.
- W2051249112 modified "2023-09-26" @default.
- W2051249112 title "GMDH algorithm as a tool for bivalve growth analysis and prediction" @default.
- W2051249112 doi "https://doi.org/10.1006/jmsc.1994.1045" @default.
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