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]
Credibility-limited Base Revision: New Classes and Their Characterizations
Authors: Marco Garapa, Eduardo Fermé, Maurício Reis
JAIR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we study a kind of operator known as credibility-limited base revisions... We propose twenty different classes of credibility-limited base revision operators and obtain axiomatic characterizations for each of them. Additionally we thoroughly investigate the interrelations (in the sense of inclusion) among all those classes. |
| Researcher Affiliation | Academia | Marco Garapa EMAIL Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105 Funchal, Portugal Eduardo Ferme EMAIL Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105 Funchal, Portugal Maurício D. L. Reis EMAIL Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105 Funchal, Portugal |
| Pseudocode | No | The paper does not contain any sections explicitly labeled "Pseudocode" or "Algorithm", nor does it present any structured code-like blocks for procedures. The methodology is described using logical definitions and theorems. |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code or provide links to a code repository. Given the theoretical nature of the paper, it is not expected to include implementation code. |
| Open Datasets | No | The paper is theoretical and focuses on logical operators and their axiomatic characterizations. It does not use any real-world datasets, benchmarks, or empirical data, hence no public datasets are mentioned or provided. |
| Dataset Splits | No | Since the paper does not utilize any datasets for experimental evaluation, there are no descriptions of training, testing, or validation dataset splits. |
| Hardware Specification | No | The paper does not describe any experiments that would require specific hardware. Therefore, no hardware specifications (such as CPU, GPU models, or memory details) are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational implementations or experiments. Consequently, there are no mentions of specific software, libraries, or their version numbers that would constitute software dependencies. |
| Experiment Setup | No | The paper does not involve empirical experiments or model training. Therefore, there are no details provided regarding experimental setup, hyperparameters, or system-level training settings. |