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 [1].
Efficiently Approximating the Pareto Frontier: Hydropower Dam Placement in the Amazon Basin
Authors: Xiaojian Wu, Jonathan Gomes-Selman, Qinru Shi, Yexiang Xue, Roosevelt Garcia-Villacorta, Elizabeth Anderson, Suresh Sethi, Scott Steinschneider, Alexander Flecker, Carla Gomes
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical results showing that our methods outperform other approaches in efficiency and accuracy. Our work is motivated by a problem in computational sustainability concerning the proliferation of hydropower dams throughout the Amazon basin.Table 1: The equivalent DP binary tree (Western Amazon) leads to considerable speed-ups:Table 2: Examples of runtimes and number of solutions:Figure 5: Approximation quality:Figure 6: Compared to the MIP approximations: |
| Researcher Affiliation | Academia | Xiaojian Wu Dept of Computer Science Cornell University EMAIL Jonathan Gomes-Selman Dept of Computer Science Stanford University EMAIL Qinru Shi Dept of Computer Science Cornell University EMAIL Yexiang Xue Dept of Computer Science Cornell University EMAIL Roosevelt Garc ıa-Villacorta Dept of Ecology and Evolutionary Biology Cornell University EMAIL Elizabeth Anderson Dept of Earth & Environment Florida International University epanders@fiu.edu Suresh Sethi U.S. Geological Survey New York Cooperative Fish and Wildlife Unit Cornell University EMAIL Scott Steinschneider Dept of Biological & Environ. Engr. Cornell University EMAIL Alexander Flecker Dept of Ecology & Evolutionary Biology Cornell University EMAIL Carla P. Gomes Dept of Computer Science Cornell University EMAIL |
| Pseudocode | No | The paper describes algorithms (DP, MIP) and mathematical formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that the source code for the methodology described is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Data: In order to test our methodological approach at different spatial scales, we developed three sub-sets of data that correspond to different geographical areas within the Amazon region: the western Amazon basin, the Mara non basin, and the entire Amazon basin (Finer and Jenkins 2012; Shedlock et al. 2000; Venticinque et al. 2016; Winemiller et al. 2016; Zarflet al. 2015). We use the Amazon river network for the tree-structured graph and corresponding values for connectivity, sediment, and seismic risk, as described in section 2. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It evaluates its methods on different geographical basins as separate instances rather than splits of a single dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions 'MIP solvers like CPLEX' and 'Non-dominated Sorting Genetic Algorithm (NSGA-II)' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The paper specifies values for the approximation parameter ϵ, such as 'ϵ = 0.1', 'ϵ = 0.2', and 'ϵ = 0.25' when describing the performance of the DP and MIP approaches. These are concrete parameters for their algorithms. |