False metrics

Where A tp is the number of true positives, A/pw is the number of false negatives, A/jn is the number of true negatives, A pp is the number of false positives, and N = A/pp + AlpN + A/tn + A fp- Sensitivity and specificity are often expressed as either fractional quantities ranging from 0 to 1, or percentage quantities ranging up to 100%. In either case, 1.0 or 100% for both metrics indicates perfect method performance. 

This is a nonsymmetric construction, which could be derived from a traditional Hermitean version via a nonpositive definite metric A An = — A22 = 1, A12 = A21 = 0. As we will see analogous constructions also follow from our complex symmetric realizations, cf. previous developments above. We emphasize that these realizations are required with the intention to mimic our dissipative system, the environment" here being made up of the other wffs of the formal system. The probability operator p is represented in an abstract space spanned by the vectors true and false). Diagonalising T p > i), i.e.,... 

Diagnostic indicators such as indicator species and community metric approaches are useful in extrapolation between smaller test units to landscapes and between landscapes themselves. The use of these indicators in extrapolation can be improved by constructing databases with information on the life-cycle characteristics of species, their occurrence and mobility in the landscape, and their sensitivity to the chemicals of concern. In the extrapolation of site-specific ecological impacts of chemical stressors, it is important to use more than one indicator to increase the discriminatory power of identifying impaired sites and to reduce the possibility of false negatives (type 2 errors, in which responses are present but not observed). 

Pearlman and Smith have reported the receptor-relevant subspace concept [18], using ACE inhibitors as an example. They found that only 3 out of the 6 universal BCUT metrics were receptor-relevant , in that the actives clustered only in these 3 dimensions. These 3 metrics are then considered receptor-relevant and worthy of being constrained, while the other can be varied and not falsely constrained. 

A performance improvement effort will require time and resources for data collection and analysis. If the metrics data collection effort is too large for available resources, it is likely that specific data will not be captured and that could impact the validity of the system performance evaluations. Any data collection effort needs appropriate resources to execute the designed plan effectively. Sometimes insufficient or incomplete data can be worse than having no data if the reported data leads to false conclusions. 

The metrics system will require informed practitioners to reliably complete the system tasks. There are many examples where poorly trained personnel collected data that was misunderstood or just wrong. This wastes resources by collecting improper and potentially useless data and may lead to erroneous conclusions about performance. A false sense of confidence could result fl-om improper data that indicates the process safety system is operating more reliably than is true. On the other hand, invalid data may lead to a conclusion that action is needed when, in fact, the process safety system is meeting expectations. 

Table 1 demonstrates the experimental matrix which was followed, and the plot below (Figure 4) shows the ROC curves that were generated from these experiments. The carbonyl peak centered at 1720 cm is used as the metric for phosmet detection. For clarity, 20,000 ppb is omitted since it overlays 2,000 ppb 2 ppb is not shown since this data was not statistically different than the blank. The separation factors, or ROC K-factors, for the curves shown were 6.75, 3.75, and 1.63 for 2,000 ppb, 200 ppb, and 20 ppb respectively. The minimum value of K that meets the Joint Service Agent Water Monitor (JSAWM) requirement of 95% detection at 5% false positives is 3.29. Therefore, these test results clearly demonstrate the detection of a VX surrogate at 200 ppb using these modified mesoporous adsorbents in a simple batch sampling mode. 

A matcher may produce not only false positives but also false negatives, which the matching designer will have to add manually to the result of the matcher, or will have to tune the tool to generate them. Two metrics have been proposed in the literature for quantifying this effort. One is the overall, which is also found under... 

The operational aspects of building protection are important considerations, even though they are less quantifiable than the protection metrics. The need for continuous operation has an important influence on protection system design. If part of the protective response is building evacuation or movement of personnel to an interior shelter, essential operations could be disrupted. Key activities that cannot be disrupted must be accounted for by the protective architecture. Similarly, the tolerance for false alarms could vary from building to building depending on the need for continuous operation. 

In comparison to intensity-based methods, techniques that rely on FRET provide large changes in emission profiles and open the opportunity for ratio-metric fluorescence measurements [59]. Assays of this type are less prone to false positives from nonspecific binding events. 

The performance of a classifier may be measured by a number of metrics. We have four basic counts to tabulate a binary prediction TP (True Positive), FP (False Positive), TN (True Negative), and FN (False Negative). Most metrics are calculated from these four numbers. A standard classifier minimizes the error estimated by the number of mistakes over the number of predictions this is often measured by the accuracy (TP + TN) /(TP + TN + FP + FN). Nevertheless, a binary classifier can make two types of errors, one for each class. For the positive class it is called sensitivity TP/(TP+FP). Similarly, the for the negative class it is called specificity TN/ TN + FN). Note that each of these metrics depends on the threshold used for a real-valued classifier, e.g. a higher threshold will lower the sensitivity and increase the specificity. 

Schurmann A, Dvorak V, Crtlzer C, Butcher P, Kaufmann A, False-positive liquid chromatography/tandem mass spectro-metric confirmation of sebuthylazine residues using the identification points system according to EU Directive 2002/657/EC due to a biogenic insecticide in tarragon, Rapid Commun. Mass Spectrom. 2009 23 1196-1200. 

An optimized development therefore takes these metrics as criteria for optimization and considers both expected safety benefit as well as possible negative consequences. In order to test false-positive rates or calculate NNT, adequate testing methods with respect to real traffic and its variability are needed [9, 10]. 

In order to better evaluate the accuracy of the proposed approach in both cases, we computed the metrics reported in Tab. 1 and Tab. 2, where Ntp (number of true positives) indicates the number of alerts correctly produced, i.e., during a deviation from the correct system behavior Nm (number of true negatives) is the nrnnber of samples of percent error that correctly are under the alert threshold, i.e., during correct system behavior Nfp (number of false positive) is the number of alerts incorrectly produced, i.e., during correct system behavior and finally AT/ (munber of false negatives) is the number samples that incorrectly are imder the alert threshold, during a deviation from the correct system behavior. 

---