COLLABORATIVE LIVING LABORATORIES TO INFORM CANADIAN DESIGN GUIDANCE FOR COASTAL NATURE-BASED SOLUTIONS
Bibliographic record
Abstract
Nature-based solutions (NbS) have been widely applied for managing coastal flood and erosion risk (Bridges et al. 2021). However, they are underutilized in Canada, owing to a variety of factors including, e.g.: (i) uncertainty surrounding the performance of different nature-based solutions across Canada’s diverse coastal climates, geographies, and land uses; and (ii) the lack of authoritative, regionally appropriate design guidance (Vouk et al. 2021). The Nature-based Infrastructure for Coastal Resilience and Risk Reduction project is bringing together a multi-disciplinary team of Canadian researchers, practitioners, and community leaders to develop an improved understanding of the performance of nature-based shore protection systems in diverse Canadian coastal environments. The project involves conducting synchronized and coordinated parallel research activities – laboratory experiments, field monitoring, and numerical modelling – centered on multiple pilot sites along Canada’s Pacific and Atlantic coasts.
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.010 |
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".