Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Change Detection Using Directional Statistics
Authors: Tsuyoshi Idé, Dzung T. Phan, Jayant Kalagnanam
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The method is validated with real-world data from an ore mining system. |
| Researcher Affiliation | Industry | IBM Research, T. J. Watson Research Center 1101 Kitchawan Rd., Yorktown Heights, NY 10598, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 RED algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | The paper uses 'real-world data from an ore mining system' which was 'generated by a testbed system' and 'synthetic three-dimensional time-series data', but does not provide any link, DOI, or formal citation for public access to these datasets. |
| Dataset Splits | No | The paper defines a 'training window' and 'test window' (Figure 1) and mentions determining (λ, ε) values using F-score on the test data, but does not explicitly provide a separate 'validation' dataset split with specific percentages or counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For RED, we used (λ, ) = (1, 4), while for SSA we used D = 25. |