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trap success

Catalogue: GRDC Final Reports
ABSTRACT A preliminary, mouse abundance model was constructed using artificial neural networks and data on trap success rates spanning a ten-year period in neighbouring catchments in Central Queensland... To predict trap success (percentage of traps that catch mice) in June, the model uses trap success rates in December and February, as well as rainfall in January/February and March/April... The preliminary results shown suggest that artificial neural networks have potential to be used to predict the likelihood of high mouse abundance in winter in Central Queensland...
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