From grey to green: Efficacy of eco‐engineering solutions for nature‐based coastal defence
Bibliographic record
Abstract
Climate change is increasing the threat of erosion and flooding along coastlines globally. Engineering solutions (e.g. seawalls and breakwaters) in response to protecting coastal communities and associated infrastructure are increasingly becoming economically and ecologically unsustainable. This has led to recommendations to create or restore natural habitats, such as sand dunes, saltmarsh, mangroves, seagrass and kelp beds, and coral and shellfish reefs, to provide coastal protection in place of (or to complement) artificial structures. Coastal managers are frequently faced with the problem of an eroding coastline, which requires a decision on what mitigation options are most appropriate to implement. A barrier to uptake of nature-based coastal defence is stringent evaluation of the effectiveness in comparison to artificial protection structures. Here, we assess the current evidence for the efficacy of nature-based vs. artificial coastal protection and discuss future research needs. Future projects should evaluate habitats created or restored for coastal defence for cost-effectiveness in comparison to an artificial structure under the same environmental conditions. Cost-benefit analyses should take into consideration all ecosystem services provided by nature-based or artificial structures in addition to coastal protection. Interdisciplinary research among scientists, coastal managers and engineers is required to facilitate the experimental trials needed to test the value of these shoreline protection schemes, in order to support their use as alternatives to artificial structures. This research needs to happen now as our rapidly changing climate requires new and innovative solutions to reduce the vulnerability of coastal communities to an increasingly uncertain future.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".