Bibliographic record
Abstract
Fire has always held a fascination for humans, and it has been one of our most useful tools. Fire has provided warmth, cooked food, cleared forest lands, offered protection against marauding animals, and much more. Although garbage has probably been burned ever since humans discovered fire, it has been incinerated in a systematic manner for only about a century. Perhaps surprisingly, given its long history and obvious benefits, waste incineration is a topic that is both controversial and emotional. In this chapter we will discuss the advantages and disadvantages of incineration and how it can contribute to an integrated waste management program. Under proper conditions, incineration provides a number of benefits: • It greatly reduces the volume of waste that must go to disposal in landfills—a vitally important objective. In conventional municipal incinerators, the volume reduction ranges from 80% to 95%, with a mean of about 90%. • It can be used in conjunction with landfill mining (see chapter 8) to reclaim closed landfills and greatly extend the operating lifetimes of existing landfills. • The ash produced is relatively homogeneous and thus more suitable than raw waste for treatment such as solidification in concrete. • A relatively large proportion of the organic compounds, including putrescible and hazardous wastes, is destroyed; thus, there is a net reduction in the quantity of toxics. • Energy can be generated as a useful byproduct, which preserves nonrenewable fuels like natural gas, oil, and coal. Fewer air pollutants are produced by burning waste than by burning coal or oil. The use of incineration has been increasing in the United States since about the mid-1980s, and currently the country burns about 16% of its municipal wastes (EPA, 1994). This figure is significantly lower in Canada—about 4%—but it can be much higher overseas. For example, Japan, which faced its waste disposal crisis in the 1950s, 20 years before the crisis reached North America, incinerates approximately 34% of its municipal garbage (Hershkowitz & Salerni, 1987). Most Japanese incinerators generate electricity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".