23 lines
2.8 KiB
Markdown
23 lines
2.8 KiB
Markdown
collapsed:: true
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type:: [[REVIEWS]]
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tags::
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year:: 2025
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venue:: [[MODELS-WS]]
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full-title:: Modeling AI-Driven Workflows for Ecosystem Resilience Prediction
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date-start:: [[29-07-2025]] - 15:17
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date-submitted::
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external-links::
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status:: [[DONE]]
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deadline-submission::
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file:: [[@Modeling AI-Driven Workflows for Ecosystem Resilience Prediction]]
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parent::
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todoist:: https://app.todoist.com/app/task/3-tiago-sousa-nicolas-guelfi-and-benoit-ries-modeling-ai-driven-workflows-for-ec-6cVfQ9fHM7mgvQ2c
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- ### [[Highlights]]
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- ### [[Comments]]
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- **SUMMARY: **The paper presents an approach for supporting the development of Ecosystem Resilience Prediction Systems (ERPS). The approach consists of two main metamodels, i.e., EcoSys and PredSys, to precisely define the ecosystems of interest and design the corresponding AI-based prediction systems. The approach's primary goal is to ensure the consistency and traceability of the prediction workflow involving ecosystem properties, data constraints, and AI capabilities. Different scenarios are given to illustrate the adoption of the presented approach.
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- *COMMENTS:* The paper is interesting and about a relevant topic. I have only a few concerns related to the work presentation. In particular,
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- The concept of *ecosystem resilience* is central to the work, but it is never explicitly defined initially. It remains unclear whether the paper refers exclusively to natural ecosystems and what specific resilience properties are considered. Similarly, the application context emerges only later, which can confuse readers. Early inclusion of concrete examples, such as the types of ecosystems or the resilience indicators targeted, would improve the work's readability.
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- The methodology introduces important concepts, yet some remain abstract. For example, while the authors mention *resilience patterns*, they do not provide concrete examples of what these patterns are or how they are used to drive prediction. It is also unclear where features of interest and prediction goals are specified in the proposed models.
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- The description of how data is validated against the requirements defined in EcoSys is insufficient and should be expanded. The same applies to the roles of users: the paper should clarify who is expected to use the approach (ecologists, AI engineers, both?) and how they interact during the process.
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- A significant weakness is the limited connection with practical execution environments. The workflow is well described at a conceptual level, but how the presented models are operationalized remains unclear. Are there tools or environments capable of interpreting these specifications? Are there code generation steps involved? Without this information, the approach risks remaining purely theoretical. Similarly, while the scenarios are helpful, they do not bridge the gap between abstract modeling and real-world deployment. |