Coastal and marine blue-green infrastructure
Bibliographic record
Abstract
Estuarine and coastal ecosystems support high biological productivity and provide important societal benefits including food production, carbon sequestration and alleviating erosion and flood risks for coastal communities. Coastal systems respond dynamically to changing conditions where they are unconstrained by artificial assets and infrastructure, with waves, tides and sea level fluctuations interacting with geomorphology and ecology to reshape habitats. However, human activities tend to ‘fix’ the coast in static positions in urban and peri-urban areas, limiting space for natural coastal landforms and their associated habitats, and for them to respond dynamically to climate-change impacts. Additionally, the coast faces intense pressure from the socio-ecological impacts associated with growing coastal populations alongside accelerating coastal climate-change risks. Increasing the amount of urban coastal and marine blue-green infrastructure (BGI) is key to reversing some of the deterioration of urban coastal habitats. This chapter introduces the concept of coastal BGI, signposting existing guidance, resources and case studies to support engineering applications. This chapter recommends combined urban and coastal BGI approaches to improve future resilience to coastal climate change and highlights the urgent need to (1) consider habitat connectivity as part of coastal BGI strategies and (2) rethink the land–sea boundary to help coastal ecosystems and landforms continue to provide essential ecosystem services as climate-change impacts accelerate. Emerging examples of projects and policies are presented to aid planners, developers, engineers and BGI practitioners in designing future-smart, climate-resilient flexible urban areas that include provision for current and future coastal BGI.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".