Bibliographic record
Abstract
An ash monofill was studied from 1997 to 2001. Monitoring results of the lined landfill showed viability of liner since groundwater standards were not exceeded. Raw leachate of RCRA heavy metal leachate results show Chromium reaching groundwater standards while Lead, Cadmium and Zinc slightly exceed these standards. An upset incident of premature set-up of lime-laden ash caused a back-up and overflow condition in 1994. Adding water of solubilization and field compaction achieves optimal geo-technical properties and reduces heavy metal leachate. This water addition would have also reduced fugitive dust concerns. These principles of sound engineering management of MWC residues were well-known and widely publicized. If the landfill operator had applied these principles the upset incident could have been avoided. Long-term trends of RCRA heavy metal leachate results show compliance with groundwater standards, although Lead, Cadmium and Zinc exceed these standards. Application of sound engineering placement practice would have reduced these long-term trends. USA Regulatory officials should consider incorporating these principles into residue management recommendations, following Environment Canada’s example. Recognition and implementation of these principles would confirm that incinerator ash can be properly managed — to alleviate concerns — justifying their beneficial reuse.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".