Two to seven taxonomic specialists reviewed each model output to determine thresholds to convert continuous predictions into presence-absence maps based on known areas of absence, and to remove areas of known over-prediction (i.e., where the species was known not to occur).
We present the case of variables, where in current practices, an uncertainty study could be understood as a quantity of interest study of the model output: mean, quantile, threshold probability, etc…This situation is very common in engineering, where complex models exist and where the experimental data are difficult to obtain.
We thresholded raw model output to binary using the least training presence threshold approach that emphasizes full prediction of the ecological niche of the species in question [50].
In the present study, we designed a methodology to quantify the risk of eutrophication from model output in terms of percentage exceedance of thresholds.
Owing to the high temporal resolution of the data, we were able to build models for a large range of urine output thresholds and durations to determine that the existing AKIN definitions employ clinically meaningful criteria [ 18].
Local SA led to unexpected and counter-intuitive results, indicating that the model output (population size) was much more sensitive to the fecundity threshold (length at first parturition) than to the fecundity parameter (brood size).
A threshold of 0.5 was used to separate responders (>0.5) from nonresponders (≤0.5) for each prediction model output and prediction sensitivity, specificity, PPV and NPV were calculated accordingly.
Therefore, we evaluated the quality of our predictive model using four metrics which ignore the success on the TN rate and summarize prediction performance over a range of output thresholds.
To estimate the effect of radiologist variability including the callback threshold (sensitivity/specificity pair) as well as skill level, we calculated a range of values for the model output.
Table 2 Attribute of the team agent Variable Data type Value Population (population) Integer [10, 100] Threshold to dismiss a team (dismiss-rate) Floating-point [0.4, 0.8] Number of shirked households (shirk-num) Floating-point Model output Number of exited households (exit-num) Floating-point Model output Average sustained discount rate (ave-discount) Floating-point Model output.
