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- Chen, M.; Tieszen, L.L.; Hollinger, D.Y.
- Ecological modelling 2008 v.219 no.3-4 pp. 317
- dynamic models, etc ; forest ecosystems; carbon dioxide; biogeochemical cycles; mathematical models; algorithms; data analysis; prediction; temporal variation; variance; Maine; Show all 11 Subjects
- ... Much of the effort spent in developing data assimilation methods for carbon dynamics analysis has focused on estimating optimal values for either model parameters or state variables. The main weakness of estimating parameter values alone (i.e., without considering state variables) is that all errors from input, output, and model structure are attributed to model parameter uncertainties. On the oth ...
- Perissi, Ilaria; Bardi, Ugo; El Asmar, Toufic; Lavacchi, Alessandro
- Ecological modelling 2017 v.359 pp. 285-292
- dynamic models, etc ; ecosystems; fish; oceans; overfishing; pollution; Show all 6 Subjects
- ... Understanding overfishing and regulating fishing quotas is a major global challenge for the 21st Century both in terms of providing food for humankind and to preserve the oceans’ ecosystems. However, fishing is a complex economic activity, affected not just by overfishing but also by such factors as pollution, technology, financial factors and more. For this reason, it is often difficult to state ...
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