the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 May 2026
Brief Communication: Structured Virtual Expert Panels for Interdisciplinary Ideation in Natural Hazard Science
Abstract. We present a virtual expert panel workflow for early-stage interdisciplinary ideation in natural hazard research. Using the Six Thinking Hats framework, a moderator questions multiple virtual experts to elicit points of consensus, contested assumptions, knowledge gaps, and follow-up clarifications. We demonstrate this workflow using a debris-flow monitoring case at the Illgraben, Switzerland. The panel highlights concept drift: year-to-year environmental change shifts the link between seismic signals and event labels and reduces machine-learning generalization. The output motivates ideas on deployment-realistic evaluation, including time-ordered data splits for training and testing, and forward-chaining validation. The workflow provides a traceable record for hypothesis formulation.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-884', Anonymous Referee #1, 08 Jul 2026
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RC2: 'Comment on egusphere-2026-884', Anonymous Referee #2, 14 Jul 2026
This manuscript presents an LLM-based virtual expert panel for interdisciplinary ideation in natural-hazard research. Although the topic is timely, the study remains a preliminary demonstration of a prompting workflow rather than a scientific investigation. It provides no new observations, quantitative experiments, independent validation, or evidence that the proposed method improves scientific reasoning compared with simpler alternatives. In my view, the manuscript does not yet meet the scientific or methodological standards required for publication in Natural Hazards and Earth System Sciences.
- The manuscript mainly demonstrates that an LLM system can generate summaries, disagreements, gaps, and research ideas. Producing these outputs does not establish their scientific value, accuracy, or originality.
- The workflow is tested only once on a single case. There is no comparison with human experts, individual LLM prompting, conventional brainstorming, or alternative multi-agent designs.
- The main conclusions—concept drift, chronological data splitting, and forward-chaining validation—are already well-established principles in time-series machine learning. The manuscript does not show that the virtual panel generated genuinely new insights.
- Assigning disciplinary roles to LLM agents does not demonstrate real expertise or independent judgment. Agreement among agents using similar models and shared inputs should not be interpreted as expert consensus.
- The adaptation of the Six Thinking Hats framework, including replacement of the Black Hat with a Purple Hat, is not justified or tested. No ablation study shows that this structure improves the outputs.
- LLM outputs depend on model versions, prompts, and sampling settings, yet no repeated-run analysis or robustness assessment is provided. Public code alone does not resolve this concern.
Citation: https://doi.org/10.5194/egusphere-2026-884-RC2
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The manuscript presents an interesting idea of using LLM-based virtual expert panels for early-stage interdisciplinary ideation in natural hazard science, demonstrated with a debris-flow monitoring case at Illgraben. However, the current manuscript is not sufficiently developed as a scientific contribution. The work mainly describes a workflow and reports AI-generated discussion outputs, but it does not provide an independent methodological advance, rigorous validation, or convincing evidence that the proposed panel improves hypothesis generation compared with conventional literature review, expert discussion, or standard AI-assisted brainstorming. The Illgraben case is used only as an illustrative example, and no new observational analysis, model testing, or empirical comparison is conducted. The conclusions about concept drift, forward-chaining validation, uncertainty calibration, and self-supervised learning are reasonable but largely known methodological considerations rather than findings generated or verified by this study.
A major concern is that the virtual experts are predefined personas with strong and somewhat artificial disciplinary positions, and the supplementary material shows that the outputs are essentially prompt-dependent AI responses rather than independently verifiable expert judgments. The reproducibility, robustness, and scientific reliability of the multi-agent discussion are not sufficiently assessed. The manuscript also lacks quantitative evaluation criteria, comparison with human expert panels, sensitivity analysis to prompts/models/panel composition, and a clear demonstration of added value for natural hazard science. Therefore, although the topic is timely and potentially useful as a perspective or methodological note, the current manuscript remains too preliminary and descriptive for publication. I recommend rejection.