Exploring the concept of digital twins of wetlands for supporting ecosystem monitoring and management
Bibliographic record
Abstract
Wetlands provide numerous ecosystems services and benefits that are essential for human society and the environment. However, wetlands have suffered significant loss and degradation globally over the past few centuries due to human disturbances and climate change. It is thus critical to monitor wetlands comprehensively and manage effectively. Meanwhile, comprehensive monitoring is challenging due to difficulties in collecting various wetland data (e.g. in situ hydrological and ecological data, remote sensing images), data analysis using diverse models (e.g. physically based and data-driven), and data visualization. Digital twins, which integrate data collection, analysis, visualization, and sharing into a comprehensive platform, are promising for addressing these challenges. While the concepts and technologies of digital twins have been frequently explored for cities and farms, they have been discussed far less for wetlands. This study attempts to explore the concept of wetland digital twins, identify technologies needed, and discuss associated challenges and opportunities. Though technologies from digital twins of cities and farms are transferable, it is essential to recognize the unique challenges of wetlands, such as their remote locations, limited accessibility, and the need to minimize human interventions. This study aims to bring insights to wetland policymakers and practitioners, promoting digital twins for more effective managements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".