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..
Adaptable Regression Method for Ensemble Consensus Forecasting
Authors: John Williams, Peter Neilley, Joseph Koval, Jeff McDonald
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The algorithm is illustrated for 0-72 hour temperature forecasts at over 1200 sites in the contiguous U.S. based on a 22-member forecast ensemble, and its performance over multiple seasons is compared to a state-of-the-art ensemble-based forecasting system. |
| Researcher Affiliation | Industry | The Weather Company, Andover, MA EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical equations and steps, but does not include a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing code or a link to a code repository. |
| Open Datasets | No | The paper mentions using "Surface temperature measurements from over 1200 ground weather station ( METAR ) locations" and "hourly temperature forecasts from an ensemble of 22 input forecasts", but does not provide concrete access information (link, DOI, formal citation for public dataset) for this data. |
| Dataset Splits | No | The paper mentions "Cross-validation was not appropriate for this evaluation" and "experiments for determining good parameters were performed using a small number of odd-hour forecast lead times", but does not provide specific dataset split information (e.g., exact percentages, sample counts, or citations to predefined splits) for training, validation, or testing subsets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "MATLAB s quadprog function" and "MATLAB s linsolve", but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For these AR results, the bias modulation = 1 or 0.8, regularization parameter = 0 or 0.1, and error covariance aggregation proportion = 0 or 0.7; the bias aggregation proportion = 0.0 is fixed for all four. In this and all other AR runs shown in this paper, the bias and error covariance learning rates were fixed at and ; the goal weight ; and the weight limits were and . |