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].
Qualitative Mechanism Independence
Authors: Oliver Richardson, Spencer J Peters, Joseph Halpern
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is theoretical in nature and does not include experimental results. |
| Researcher Affiliation | Academia | Oliver E. Richardson Dept of Computer Science Cornell University Ithaca NY 14853 EMAIL Spencer Peters Dept of Computer Science Cornell University Ithaca NY 14853 EMAIL Joseph Y. Halpern Dept of Computer Science Cornell University Ithaca NY 14853 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | This paper is theoretical in nature and there is no associated data or code. |
| Open Datasets | No | This paper is theoretical and does not include experiments. |
| Dataset Splits | No | This paper is theoretical and does not involve any training or testing of models. |
| Hardware Specification | No | This paper does not have experiments, computational or otherwise. |
| Software Dependencies | No | This paper is theoretical and does not include experiments. |
| Experiment Setup | No | This paper is theoretical and does not involve any training or testing of models. |