Advancing on the promises of techno-ecological nature-based solutions: A framework for green technology in water supply and treatment
Bibliographic record
Abstract
Abstract Nature-based solutions (NBS) are increasingly proposed for effectively and adaptively addressing societal challenges such as water security and natural disasters. However, NBS that are exclusively reliant on natural processes are not fit-for-purpose for the provision of safe drinking water – some range of built technology is required. There is a wide spectrum of techno-ecological NBS – ‘green technologies’ – that are fit-for-purpose in the treatment and distribution of safe drinking water. A framework was developed to enable an accurate and transparent description of the ‘green’ attributes of technology – including green infrastructure – in the water industry. The framework differentiates technology ‘greenness’ by relatively examining key attributes that may cause environmental impacts across the technology's life cycle through the lens of the environmental setting in which it is applied. In the water industry, green technology can be described by four main attributes: natural-resource basis, energy consumption, waste production, and footprint. These attributes are closely linked and must be considered relative to the biophysical and human environments in which they are applied and the other technologies to which they are being compared. The use of the framework can facilitate techno-ecological decision-making that strives to address diverse stakeholder priorities – including the influence of sociocultural factors on the green technology preferences of individuals, groups, or communities.
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 | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| 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".