The unique value proposition for using drones to map coastal ecosystems
Bibliographic record
Abstract
Abstract Coasts are undeniably regions of critical importance for a range of environmental, sociocultural, and economic reasons. Yet they are also areas of intense anthropogenic impact and are particularly susceptible to climate change-related concerns. As such, it is imperative that we have the means to monitor and manage them in a sustainable manner. Drone technology has emerged as providing a unique value proposition in coastal environments to support data-driven monitoring and management decisions. With their highly detailed data capture capability, drones are particularly well suited to map the spatial heterogeneity, structural complexity, and temporally dynamic nature of coasts. Further, they are readily accessible to coastal populations and can promote grassroots action by the very people whose lives and livelihoods the coasts support. Herein, we cover several of the leading innovations in using aerial drones to map coastal ecosystems. We then consider how general trends and technology projections including artificial intelligence, as well as cloud and edge computing offer opportunities for the future of drone mapping and monitoring in a coastal context. While the challenge of change is inevitable, embracing the opportunities it provides will allow us to better understand and live sustainably with and within our coastal ecosystems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".