Identification of Coastal Defence Measures Best Adapted to Mitigate Hazards in Specific Coastal Systems: Development of a Dynamic Literature Meta-Analysis Methodology
Bibliographic record
Abstract
The decision-making process of the coastal defence measures (CDMs) is complex and filled with uncertainties due to site-specific interactions between hydrodynamic and geomorphological conditions, which have repercussions on the ecological and social aspects of coastal communities. Scientific knowledge of the effects of CDMs contributes to the reduction in inherent uncertainties and facilitates the decision-making and design processes. The goal of this article is to present an algorithm designed to evaluate and hierarchize CDMs in relation to different coastal environments. Drawn from 411 published scientific case studies, a total of 1709 authors’ observation statements regarding the effects of CDMs on the study sites’ environmental features (type of coast, type of substrate, tidal range, and wave climate) were entered in a database, categorized, and weighted according to a qualitative scale. The algorithm processes the information by establishing a correspondence between user-selected environment features and those stocked in the database, and it evaluates user-selected CDMs in relation to the specified coastal characteristics by identifying, collating, and rating the effects as observed in similar contexts. The result is a tool able to process, structure, and concretize scientific knowledge regarding CDMs and their effects on coastal systems. It is complementary to existing tools currently used in the decision-making and design processes of the CDMs. The results present the hierarchization of CDMs according to a multilevel aggregated structure, which can be used in different ways by coastal managers, decision-makers, and engineers. The algorithm, based on standardized coastal characteristics, can be applied to any shoreline worldwide.
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 | 0.045 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.074 | 0.039 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".