Maximizing Benefits to Nature and Society in Techno-Ecological Innovation for Water
Bibliographic record
Abstract
Nature-based solutions (NbS) build upon the proven contribution of well-managed and diverse ecosystems to enhance resilience of human societies. They include alternatives to techno-industrial solutions that aim to enhance social-ecological integration by providing simultaneous benefits to nature (such as biodiversity protection and green/blue space) and society (such as ecosystem services and climate resiliency). Yet, many NbS exhibit aspects of a technological or engineered ecosystem integrated into nature; this techno-ecological coupling has not been widely considered. In this work, our aim is to investigate this coupling through a high-level and cross-disciplinary analysis of NbS for water security (quantity, quality, and/or water-related risk) across the spectrums of naturalness, biota scale, and benefits to nature and society. Within the limitations of our conceptual analysis, we highlight the clear gap between “nature” and “nature-based” for most NbS. We present a preliminary framework for advancing innovation efforts in NbS towards maximizing benefits to both nature and society, and offer examples in biophysical innovation and innovation to maximize techno-ecological synergies (TES).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".