Summary WATER AND WASTEWATER EFFICIENCY: OPTIMIZING THE LAND PLANNING, ENERGY, ECOLOGICAL AND AGRICULTURAL DIMENSIONS
Bibliographic record
Abstract
The traditional focus on maximizing supply in water infrastructure is contrasted with the “soft path for water, ” a multi-faceted approach which looks at demand side management, design and technology substitutions to use less or no water, along with basic infrastructure. Water efficiency approaches such as rainwater harvesting, wastewater effluent reuse and dry toilet technologies are described, citing Canadian and international case studies. The success of current infrastructure in reducing waterborne disease and limiting the impact of nutrients to natural waters is used as a starting point from which to describe future scenarios where new goals of energy efficiency, ecological needs and nutrient recycling for food production are addressed. Near-term technologies are capable of reducing water demand and effluent and nutrient loadings by up to an order of magnitude. While water supply, effluent disposal, and nitrate loadings to groundwater have been limiting factors in planning, the reduction in water impacts allows planners to shift weight to green issues such as the protection of prime agricultural land and habitat and the provision of green space. The potential effects on infrastructure, infrastructure renewal, sustainable community design and on the size of on-site water and wastewater treatment are quantified.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".