Bibliographic record
Abstract
There are three or four general categories of "waste." There is "municipal solid waste" (the waste that comes out of households, hotels, restaurants, businesses, etc.), "hazardous waste," and "nuclear waste," the latter of which is sometimes divided into low-level and high-level nuclear waste. (My remarks today will focus almost exclusively on municipal solid waste and hazardous waste). All of us are most familiar with municipal solid waste. Where does municipal solid waste end up? The vast majority of U.S. municipal solid waste - 57 percent - goes to landfills. Twenty-two percent of U.S. municipal solid waste is recycled, fifteen percent is incinerated, and six percent is composted. Over thirty million tons of that waste - that is, over 11 percent of the total waste stream - crosses state lines each year. Eighty percent of that waste goes to a neighboring state; going through one state to take the trash to another is much less common. Why does waste move across boundaries both between states and between Canada and the United States? People are trying to dispose of their waste at the lowest cost, and there are several reasons why we might move trash across the border. It might be more convenient; the closest landfill may not be within your state or province, but just across the border. It may also be that you have a shortage of landfills in your home state or province. Another reason that waste can move is because of differences in regulatory regimes between jurisdictions. In fact, a study commissioned by the North American Commission for Environmental Cooperation found that the increased level of hazardous waste exports from the United States to Canada between 1993 and 1998 was attributable to the lower standards for the disposal of hazardous waste in Ontario and Quebec.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".