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..
The Ostomachion Process
Authors: Xuhui Fan, Bin Li, Yi Wang, Yang Wang, Fang Chen
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on relational modeling and decision tree classification have validated the merit of the OP. |
| Researcher Affiliation | Academia | Xuhui Fan, Bin Li, Yi Wang, Yang Wang, Fang Chen Machine Learning Research Group, National ICT Australia, Eveleigh, NSW 2015, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1 RJMCMC for the Ostomachion process |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We test our ORM on seven benchmark data sets: Foodweb, Dolphin, Lazega, Polbooks, Train, Reality, Wikitalk. ... Lichman, M. 2013. UCI machine learning repository. |
| Dataset Splits | Yes | The performance of the two applications is evaluated by averaging the prediction on 10 randomly selected (in a ratio of 1/10) hold-out test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper refers to algorithms and models (e.g., 'reversible-jump MCMC algorithm', 'decision tree CART') and their implementations ('RJMCMC-2', 'RJMCMC-3'), but does not specify any software libraries or dependencies with version numbers. |
| Experiment Setup | Yes | In our experiments, we set the time limit τ = 5 (meaning that the expected number of cuts is 5) and the concentration parameter α = 10. We set T = 200 (200 iterations) for both RJMCMC-2 and RJMCMC-3. |