Model Evaluation by Comparison of Model-Based Predictions and Measured Values
By Hugh G. Jr Gauch, J. Hwang, G. Fick. 2003
305 citations recorded by the source corpus.
Subfields: Agronomy and Crop Science, Plant Science
View Paper: https://www.semanticscholar.org/paper/aafd99ae896834848bdbebace3c371f8fcc3ad59
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Agronomy and Crop Science
- Pierre Martre
- P. Jamieson
- Mikhail A. Semenov
- E. Triboi
- John R. Porter
- Rob F. Zyskowski
- L. Tedeschi
- M. J. Foulkes
In Plant Science
- Pierre Martre
- P. Jamieson
- Mikhail A. Semenov
- E. Triboi
- John R. Porter
- Rob F. Zyskowski
- L. Tedeschi
- M. J. Foulkes
Citations in the corpus
- Assessment of the adequacy of mathematical models (2006, methodology): “The lack of accuracy shown in case 2 (Figure 2) is also known as translation discrepancy (Gauch et al., 2003); despite the slope being equal to unity, the intercept is different from zero.”
- Optimal foraging theory predicts diving and feeding strategies of the largest marine predator (2011, background): “MSD is more conservative than MSE and can be decomposed into 3 additive components with distinct and clear interpretations (Gauch et al. 2003): squared bias (SB) resulting from unequal means, non-unity slope (NU) which arises when the slope of the least-square regression differs from 1, and lack of ”
- Modelling protein content and composition in relation to crop nitrogen dynamics for wheat (2006, methodology): “MSD s the mean of the squared deviations around the 1:1 line in a lot of model simulation against measured values, and RMSD s the standard deviation of these deviations around the 1:1 line Gauch et al., 2003).”