Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
Authors: Rico Angell, Daniel R. Sheldon
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,809 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,833 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 | In this section, we present the results from experiments to evaluate the effectiveness of the method we presented in the previous section. |
| Researcher Affiliation | Academia | Rico Angell University of Massachusetts Amherst EMAIL Daniel Sheldon University of Massachusetts Amherst EMAIL |
| Pseudocode | Yes | Algorithm 1 Efficient Inference using Laplace s Method |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper uses 'Archived data from the US network of weather radars' and refers to NEXRAD radars, but does not provide a direct link, DOI, or formal citation for accessing this specific dataset. |
| Dataset Splits | No | The paper does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions that 'hyperparameters are fixed at values chosen through preliminary experiments to match the expected smoothness of the data, so that the RMSE between inferred radial velocities and raw measurements match values from velocity models used in prior research [7, 9]', but does not provide specific hyperparameter values or detailed training configurations. |