Introduces the Matter to Mechanism benchmark of 2,645 structured instances and a composite metric suite for evaluating AI co-scientists on problem-to-hypothesis reasoning in battery materials research.
On the Kendall Correlation Coefficient
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abstract
In the present paper, we first discuss the Kendall rank correlation coefficient. In continuous case, we define the Kendall rank correlation coefficient in terms of the concomitants of order statistics, find the expected value of the Kendall rank correlation coefficient and show that the later is free of n. We also prove that in continuous case the Kendall correlation coefficient converges in probability to its expected value. We then propose to consider the expected value of the Kendall rank correlation coefficient as a new theoretical correlation coefficient which can be an alternative to the classical Pearson product-moment correlation coefficient. At the end of this work we analyze illustrative examples.
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cs.CE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Matter to Mechanism: A Benchmark for AI Co-Scientists in Materials and Battery Research
Introduces the Matter to Mechanism benchmark of 2,645 structured instances and a composite metric suite for evaluating AI co-scientists on problem-to-hypothesis reasoning in battery materials research.