Bibliographic record
Abstract
Spinosa (2004) provided an overview of more sustainable sludge management through the recovery and reuse of valuable products including both materials and energy.Because the selection of an appropriate sludge management system is influenced by many other factors, such as local economy and geography, climate, land use, regulatory constraints and public acceptance of the various practices, only general indications were given.Rulkens (2004) examined the challenges facing sustainable sludge management, providing a survey of the most relevant sludge treatment options and separate treatment steps.Rulkens (2004) paid special attention to those processes that are concurrently focused on the elimination of the risks for environment and human health and on the recovery or beneficial use of valuable sludge components such as organic carbon compounds, etc. Jiminez et al. (2004) discussed sustainable sludge management in developing countries where unsanitary conditions are responsible for more than three million deaths annually.They indicated that sludge management plays an important role in sanitation programs by helping reduce health problems and associated risks particularly because of the high microbial concentrations in sludge.Policies on sludge (or biosolids) management vary widely, particularly when decisions must be made on what to do with the final product.Dentel (2004) 15547531,
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".