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Table 9 A list of terms and their meaning

Variable Meaning
NB Naïve Bayes Classifier
$$RSS$$ Received signal strength or detection power, model parameter
$$HR$$ Hit ratio, model parameter
$$CRL$$ Consecutive record length, model parameter
$$NR$$ Noise ratio, model parameter
$${\delta }^{2}L$$ Difference in time-lab between detections, model parameter
$$PDH$$ Proximate detection history
$$P\left({C}_{i}|{F}_{1},\dots ,{F}_{n}\right)$$ Posterior probability of a detection belonging to detection class $${C}_{i}$$ given the observed predictor variables $${F}_{1},\dots ,{F}_{n}.$$
$$P({C}_{i})$$ Prior probability of the ith detection class occurring where ($$C\in \left\{\mathrm{Valid},\mathrm{False Positive}\right\}$$)
$$P({F}_{j}|{C}_{i})$$ Likelihood (conditional probability) of the jth observed predictor ($${F}_{j}$$) value given the ith detection class ($${C}_{i}$$)
$${t}_{p}$$ True positive
$${f}_{p}$$ False positive
$${f}_{n}$$ False negative
$${t}_{n}$$ True negative
$$sen$$ Sensitivity, model metric, $$sen={t}_{p}/({t}_{p}+{f}_{n})$$
$$spc$$ Specificity, model metric,$$spc={t}_{n}/({f}_{p}+{t}_{n})$$
$$npv$$ Negative predictive value, model metric,$$npv={t}_{n}/({f}_{n}+{t}_{n})$$
$$ppv$$ Positive predictive value, model metric,$$ppv={t}_{p}/({t}_{p}+{f}_{p})$$
$$fpr$$ False positive rate, model metric,$$fpr={f}_{p}/({f}_{p}+{t}_{n})$$
PRC Precision-recall curve
AUC Area under the curve statistic, integral of the PRC
$$\kappa$$ Cohen’s Kappa measure of concordance