Table 2.
Error | Overall | Non-expert | Expert | P(Non> Expert) |
---|---|---|---|---|
Split | 0.091 ± 0.016 | 0.098 ± 0.025 | 0.078 ± 0.021 | 0.709 |
Combine | 0.016 ± 0.007 | 0.007 ± 0.007 | 0.019 ± 0.011 | 0.138 |
Shift | 0.023 ± 0.008 | 0.042 ± 0.016 | 0.002 ± 0.001 | 1 |
Total split | 0.111 ± 0.011 | 0.139 ± 0.028 | 0.078 ± 0.022 | 0.957 |
Total combine | 0.037 ± 0.011 | 0.049 ± 0.017 | 0.019 ± 0.011 | 0.918 |
Exclude | 0.087 ± 0.016 | 0.119 ± 0.027 | 0.053 ± 0.018 | 0.981 |
Exclude* | 0.049 ± 0.013 | 0.044 ± 0.019 | 0.053 ± 0.018 | 0.344 |
CE error | 0.125 ± 0.019 | 0.146 ± 0.029 | 0.099 ± 0.024 | 0.895 |
CE error + | 0.208 ± 0.023 | 0.264 ± 0.036 | 0.151 ± 0.028 | 0.994 |
CE error* + | 0.174 ± 0.023 | 0.191 ± 0.034 | 0.151 ± 0.028 | 0.782 |
Estimates are presented for the 8 observers (overall) and also divided according to their previous experience in snow leopard photo classification (non-expert vs. expert) with the probability that non-experts have greater errors than experts [P(non > expert) derived from the posterior distribution of the difference between observers]. Total split and total combine add the shift estimate to the split and combine estimates, respectively, since shifts involve a split and combination error. Capture event error (CE) relates to the total probability of a capture event being misclassified: this is presented for classification errors only (CE error) and when exclusions are considered as a classification error (CE error+). Because observer 2 excluded 30% of all capture events, some estimates are also presented where observer 2 has been removed from the analysis (*).