Ecological enhancement of coastal engineering structures: Passive enhancement techniques
Bibliographic record
Abstract
The rock type used in coastal engineering structures impacts biodiversity, but its effect has been understudied to date. We report here on whether different combinations of rock material and rock mass properties can improve habitat suitability and early phase ecological outcomes on coastal engineering structures. We examine two coastal engineering schemes that used different granites during construction. At site one, Shap granite boulders with a high number of cm-dm2 surface features (e.g. ledges) were deliberately positioned during construction (called passive enhancement), to a) maximise the provision of cm-dm scale intertidal habitat and b) determine which scale of habitat is best for ecological enhancement. At site two, Norwegian granite boulders were installed without passive enhancement, allowing for a direct comparison. Passive positioning of Shap granite boulders led to an increase in limpet (Patella vulgata, Linnaeus, 1758) abundance within two years but few limpets were recorded on the non-enhanced Norwegian granite. Positioning of boulder thus exerts a strong control on the mm and mm-dm scale geomorphic features present, with clear ecological benefits when suitable features are selected for and optimally positioned (i.e. passive enhancement) to maximise habitat features. An EcoRock scoring matrix was developed to aid in the selection of the most ecologically suitable rock materials for coastal engineering worldwide; this can help improve habitat provision on engineered structures in a rapidly warming world.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".