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These databases are based on administrative coding, and consistency in results obtained from >1 data source potentially indicates a higher chance of accuracy.
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This accuracy rate, however, must be corrected for the chance level of accuracy reached by participants' guessing the answers.
In terms of the immediate and delayed perceptual tasks, target size was accurately reported during non-masked trials; however, for masked trials only a chance level of accuracy was observed.
Moreover, we excluded participants performing at a chance level of accuracy, i.e., making 50% or more errors on any of the relevant task conditions.
In this manner, it is hypothesised that the maximum chance for accuracy in the collection of data can be achieved.
By eliminating the outliers before this process, the chance of decreasing the accuracy of the good anchors is minimized.
The baseline of chance accuracy for DFAs (i.e., the percentage of correct predictions that can be expected when habitat assignments are randomized) served as the cut-off point between good and bad habitat predictors.
This statistic ranges from 0.5 to 1 and indicates how much better our method performs as compared to a by chance baseline accuracy of 0.5.
Using the DAN values in searching for the best cut-off, we obtained a near chance result with only 50% of accuracy.
The ESS is the mean sensitivity obtained by the model across class categories which standardizes, using a 0 100% scale, where 0% is the level of accuracy expected by chance and 100% is the perfect accuracy; negative values indicate lower accuracy than expected by chance.
For each bootstrap sample, we calculated the classification accuracy and determined the onset of above chance classification accuracy for between- and within-category classification (defined as the first time after 50 ms that was significantly above chance).
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