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 [1].
The State of the AIIDE Conference in 2017
Authors: Nathan Sturtevant, Brian Magerko
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is a descriptive overview of the AIIDE conference's state and trends, rather than an experimental study with data analysis or a theoretical paper presenting new algorithms or proofs. |
| Researcher Affiliation | Academia | Nathan R. Sturtevant Computer Science Department University of Denver EMAIL; Brian Magerko School of Literature, Communication and Culture Georgia Institute of Technology EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a descriptive overview and does not present any software or code for release. |
| Open Datasets | No | The paper is a descriptive overview and does not use datasets for training or present information about public datasets. |
| Dataset Splits | No | The paper is a descriptive overview and does not involve data splits for validation or training. |
| Hardware Specification | No | The paper is a descriptive overview and does not mention any hardware used for experiments. |
| Software Dependencies | No | The paper is a descriptive overview and does not mention any software dependencies for experiments. |
| Experiment Setup | No | The paper is a descriptive overview and does not describe any experimental setup or hyperparameters. |