A digital twin for monitoring land use/cover and coastal change in Tobago
Bibliographic record
Abstract
Abstract. Digital twin technology presents a transformative opportunity for monitoring land use and coastal changes on small islands. These comprehensive virtual models help to mirror physical environments, thereby offering real-time data integration, simulation, and predictive analytics. The island of Tobago, in the southeastern Caribbean Sea, has diverse landscapes and coastal regions ranging from tropical forests and mangrove ecosystems to thriving coral reefs and seagrass beds. In this study, high resolution digital elevation data was used to model the topography of the island. A land use and land cover information layer, generated from high resolution satellite imagery was then draped over the elevation model to illustrate the variation across the small, mountainous tropical island. An updated high-resolution satellite image base layer of the nearshore marine environment was also integrated into the model along with polygons demarcating the distribution and extent of key coastal ecosystems such as coral reefs. The key to digital twins lies in their ability to provide accurate and up-to-date spatial data and environmental insights. This virtual replica allows for detailed analysis of changing land use and habitat loss, helping stakeholders to make informed decisions. By integrating data from various sources such as satellite imagery, geographic information systems (GIS), and in-situ measurements the digital twin facilitates comprehensive monitoring and forecasting of land and coastal changes. For Tobago, this digital model can enhance understanding and management of its dynamic coastal and land use environments, which are increasingly threatened by climate change, urbanization, and natural disasters.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".