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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Quantification of Resource Production Incompleteness
Authors: Yakoub Salhi6480-6487
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we introduce a logic-based framework for measuring resource production incompleteness: the greater the value returned by a measure, the greater is the intensity of incompleteness. |
| Researcher Affiliation | Academia | Yakoub Salhi CRIL, U. Artois & CNRS, Lens, France EMAIL |
| Pseudocode | No | The paper describes logical rules and definitions (e.g., Sequent Calculus CIMAAL in Figure 1), but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and focuses on a logic-based framework; there is no mention of releasing open-source code for any implementation. |
| Open Datasets | No | The paper is theoretical and introduces a logic-based framework; it does not use datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and introduces a logic-based framework; it does not report on computational experiments, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve software implementation or computational experiments, therefore no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical, focusing on a logic-based framework; it does not detail any experimental setup, hyperparameters, or training configurations. |