Living Shorelines: The Science and Management of Nature-Based Coastal Protection
Bibliographic record
Abstract
Living Shorelines: The Science and Management of Nature-based Coastal Protection compiles, synthesizes and interprets the current state of the knowledge on the science and practice of nature-based shoreline protection. This book will serve as a valuable reference to guide scientists, students, managers, planners, regulators, environmental and engineering consultants, and others engaged in the design and implementation of living shorelines. This volume provides a background and history of living shorelines, understandings on management, policy, and project designs, technical synthesis of the science related to living shorelines including insights from new studies, and the identification of research needs, lessons learned, and perspectives on future guidance. \n\n \n\n International perspectives are presented from leading researchers and managers in the East, West and Gulf coasts of the United States, Europe, Canada, and Australia that are working on natural approaches to shoreline management. The broad geographic scope and interdisciplinary nature of contributing authors will help to facilitate dialogue and transfer knowledge among different disciplines and across different regions. This book will provide coastal communities with the scientific foundation and practical guidance necessary to implement effective shoreline management that enhances ecosystem services and coastal resilience now and into the 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".