This scoring system allows us to quantify a model's tendency to assert knowledge about non-existent subjects. The visualizations above highlight the top and bottom performers in this test, revealing which models are more likely to falsely claim familiarity with artificial species. These results help identify which models are better calibrated to admit the limits of their knowledge, a crucial characteristic for trustworthy scientific AI.