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].
On a Scientific Discipline (Once) Named AI
Authors: Wolfgang Bibel
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The present article is therefore meant to provide just an analysis of the present state of affairs rather than pointing out a concrete plan for changing directions except for some indications in view of the given possibilities. |
| Researcher Affiliation | Academia | Wolfgang Bibel Darmstadt University of Technology EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | This is a theoretical paper and does not describe any new software or code implementation, therefore no open-source code is provided. |
| Open Datasets | No | This paper is theoretical and does not involve experimental training on datasets, thus no dataset access information is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments or their computational requirements, thus no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper and does not describe any software implementation or dependencies. |
| Experiment Setup | No | This paper is theoretical and does not describe any experimental setup or hyperparameters. |