Sensing and Measurement Techniques for Evaluation of Nature-Based Solutions: A State-of-the-Art Review
Bibliographic record
Abstract
Sensing and measurement techniques are necessary to study, evaluate, and understand the complex physical and chemical interactions that must occur for the successful deployment of nature-based solutions (NbS). How NbS are measured can determine which solutions best address local environmental and societal challenges, and how these solutions are prioritized and adopted by decision makers. Sensing and measurement techniques can provide useful meteorological and physiological data on nature-based interventions between different spatial, spectral, temporal, and thematic scales. Because NbS encompass research from across different fields, it is essential to reduce barriers to knowledge dissemination, and enable the circulation of information across different jurisdictions. In this study, a bibliometric and systematic analysis of the literature was undertaken to systemize and categorize sensing and measurement techniques for NbS. Opportunities and challenges associated with studying the effects of NbS have also been identified. Sensing and measurement techniques can provide evidence-based information on the efficacy of NbS, in addition to guiding policy formulation for the achievement of sustainable development across communities.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".