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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Finding Galaxies in the Shadows of Quasars with Gaussian Processes
Authors: Roman Garnett, Shirley Ho, Jeff Schneider
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 14,899 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,299 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our method s effectiveness with a large-scale validation experiment on over 100 000 spectra, with excellent performance. |
| Researcher Affiliation | Academia | Roman Garnett EMAIL Washington University in St. Louis, St. Louis, MO 63130, United States Shirley Ho EMAIL Jeff Schneider EMAIL Carnegie Mellon University, Pittsburgh, PA 15213, United States |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | We plan to make the parameters of our learned GP model, using the full DR10 release, available for future research efforts. |
| Open Datasets | Yes | We used the QSO spectra from the BOSS DR9 Lymanforest sample (Lee et al., 2013) to train our GP model. Later, we will use the spectra from the corresponding DR10 sample (Lee et al., 2014) to evaluate our proposed DLA finding approach. |
| Dataset Splits | No | The paper mentions using DR9 for training and DR10 for evaluation, but does not specify a distinct validation set with explicit percentages or counts for model selection or hyperparameter tuning. |
| Hardware Specification | No | The paper only mentions that processing takes 'under a second on a standard desktop machine,' without providing specific hardware details like GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | No specific software dependencies with version numbers were listed in the paper. |
| Experiment Setup | Yes | We took k D 10, noting that the first 10 principal components of the flux matrix Y explain approximately 99.9% of the total variance. The chosen grid was this set of wavelengths with a linearly equal spacing of D 0:25 Å. The constant 0 serves to ensure that QSOs with very small redshift have sufficient data for estimating the prior; here we took 0 D 0:1. |