A micropaleontologist uses machine learning to classify microfossil images. Her algorithm correctly identifies radiolarians 94% of the time and diatoms 88% of the time. If a sample contains 250 radiolarians and 150 diatoms, how many total microfossils are correctly classified?

Sun, 19 Apr 2026 15:04:53 +0000

**A micropaleontologist uses machine learning to classify microfossil images. Her algorithm correctly identifies radiolarians 94% of the time and diatoms 88% of the time. If a sample contains 250 radiolarians and 150 diatoms, how many total microfossils are correctly classified?** In a growing intersection of paleontology and artificial intelligence, scientists are increasingly turning to machine learning to analyze delicate microfossil images—tiny remnants of ancient marine life that hold clues to Earth’s past. As data sets expand, researchers are finding that algorithms enhance speed and accuracy beyond manual classification, especially when identifying complex organisms like radiolarians and diatoms. With 250 radiolarians and 150 diatoms in a single sample, AI-driven analysis now enables researchers to process vast collections efficiently—reshaping how microfossil science advances. Why is this headline-click-worthy in the US scientific community? Agile machine learning models are unlocking new discovery potential in paleontology, offering precise identifications where human analysis alone faces limits. Radiolarians, with their intricate silica skeletons, and diatoms, with their fragile glass-like cell walls, represent key indicators of past climate and ocean conditions. When algorithms achieve 94% accuracy on radiolarians and 88% on diatoms, scientists gain powerful tools to interpret ancient ecosystems—driving insights relevant to climate research, education, and paleontological exploration. How does this real-world application work? The algorithm processes digitized microfossil images, learning from labeled training data to recognize unique morphological patterns. In practice, users input a sample, and the model scans thousands of images in seconds, assigning confidence scores to each classification. For a sample with 250 radiolarians and 150 diatoms, calibrated performance metrics translate directly to categorized outputs. With known accuracy rates, a straightforward calculation reveals the total number of correctly identified microfossils.