Bibliographic record
Abstract
Ecology is often discussed as a matter of balance, in which environmental protection must be affordable and not interfere with jobs or the economy. At the same time, the economy is based on wastefulness. It has been estimated that the embodied energy in wasted food in the United States is greater than the energy available from the production of ethanol and from the annual yield from petroleum drilling in the outer continental shelf (Cuéllar & Webber, 2010). In addition, rising demand for fossil fuels is being met by sources that bring increasing environmental risk. This paper summarizes the industrial ecology aspects of a 2010 study completed by a cross-functional team of specialists in ecology, engineering, economics, and governance in Vancouver, Canada. The Integrated Resource Recovery Study, Metro Vancouver North Shore Communities (the North Shore Study) modeled the value of producing reclaimed water, electricity, and heat from wastewater, clean organic wood waste, and waste heat from industry simultaneously. The results suggest that this integrated approach could yield significant ecological benefits, and reduce the community’s greenhouse gas emissions by 25%. In addition, revenues from sales of recovered heat, water, greenhouse gas credits, and fertilizer could significantly reduce the cost of municipal waste management to taxpayers.
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 | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".