## 3 Results and Discussion
Moreover, the average position was employed to determine the algorithm's performance in terms of detection, calculated as follows:  
\[AP=\int_{R=0}^{1}P(R).dR \tag{10}\]  
Finally, mean average recision explains the average precision (\(AP\)) calculated not for every class (size of the mollusk), but for every class under study:  
\[mAP=\frac{1}{g}\sum_{i}^{g}AP_{i} \tag{11}\]  
Figure 10 provides a detailed visualization of the Recall-Confidence Curve for the various classes, serving as a critical indicator of the algorithm's performance. The curve illustrates how the algorithm's Recall changes as the Confidence threshold is adjusted. Recall, defined as the ratio of true positive instances to the sum of true positives and false negatives, measures the ability of the algorithm to identify all relevant instances. The curve's shape and the specific data points offer insights into the balance between correctly identified instances and the Confidence level at which these identifications occur. This shows the algorithm's efficacy in recognizing different classes under varying degrees of Confidence. The results highlighted in Figure 10 demonstrate that the algorithm achieves a high level of accuracy. When applying a reasonable Confidence threshold, it can be observed that more than 90% of the classes were correctly identified. This high Recall rate indicates that the algorithm is performing as expected in detecting the majority of relevant instances within each class, thereby ensuring robust performance across different categories. The implication is that the algorithm maintains a strong ability to discern relevant data points even when adjusting for confidence levels, reflecting its overall reliability and precision in practical applications. To further enhance the reliability of the algorithm and mitigate the risk of false positives, predictions with confidence levels below 50% were systematically discarded. This threshold was established to ensure that only those predictions with a higher likelihood of accuracy were considered. By setting this cutoff, the number of incorrect classifications, or false positives, was significantly reduced. This filtering mechanism ensures that the algorithm's outputs are not only accurate but also trustworthy, thereby increasing the overall integrity of the classification results. The balance between Recall and Confidence underscores the algorithm's capability to deliver high-quality predictions while minimizing errors, making it a valuable tool for classifying the individuals in a water sample. Figure 10: Recall–Confidence Curve. Figure 11 illustrates the Precision-Recall curve, a tool for evaluating the performance of the model in terms of its ability to correctly identify positive instances. Precision, defined as the number of true positives divided by the sum of true positives and false positives, measures the proportion of positive identifications that were accurately detected by the algorithm. This metric helps us to understand how well the model is distinguishing between true positive cases and false alarms. The Precision-Recall Curve in Figure 11 provides a visual representation of this relationship across different thresholds, highlighting the model's efficacy in maintaining high precision while adjusting recall levels. The curve indicates that the model demonstrates an average Precision value of 97.6%, showing its capability to correctly identify positive instances with minimal errors. This high Precision rate suggests that the model is avoiding incorrect labeling of negative cases, thus reducing the occurrence of false positives. The ability to achieve this Precision is indicative of the model's robustness, particularly in scenarios where accurate detection of positive cases is critical. Overall, the Precision-Recall Curve not only reflects the model's strong performance but also its capacity to deliver precise classifications, making it a valuable asset in the laboratory.