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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Revision by Comparison for Ranking Functions
Authors: Meliha Sezgin, Gabriele Kern-Isberner
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The proofs in this paper are straightforward, but technical, and therefore omitted due to lack of space. |
| Researcher Affiliation | Academia | Meliha Sezgin and Gabriele Kern-Isberner TU Dortmund University, Germany EMAIL |
| Pseudocode | No | The paper contains formal definitions, theorems, and mathematical expressions but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or reference any publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits (training, validation, test) for experimental reproduction. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |