Introduction of a nature-based sustainable technology to mitigate climate change-driven water pollution in rivers and lakes
Bibliographic record
Abstract
Climate change is intensifying water stress around the world by disrupting the water quantity and quality of surrounding rivers, lakes, and streams. Sustainable water management to adopt climate change and improve global water security needs to focus on technology and innovation. A decentralized, low-energy and sustainable approach to meet both the water quality and quantity demands requires for combating global water scarcity under climate change conditions. The proposed technology is based on the principle that incorporation of nature-based solutions in technological process development can lead to a powerful tool for tackling the climate change-driven water pollution. This technology is an extension version of the patented technology on oil sands tailings water treatment (Canadian Patent 2,952,680). The nature-based entrapped cells submerged reactor is proposed as a sustainable on-site treatment option to manage surface water quality. The process consists of selection and entrapment of suitable bacterial communities found in the natural environment. The submerged reactor containing entrapped naturally occurring bacterial communities is used for improving on-site water quality under aerobic conditions. This nature-based and decentralized microbial technology provides practical solutions like on-site wastewater treatment for achieving the United Nations Sustainable Development Goal.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".