Reproduced from Journal of Environmental Quality. Published by ASA, CSSA, and SSSA. All copyrights reserved. Interpreting Science in the Real World for Sustainable Land Application
Bibliographic record
Abstract
ABSTRACT fluents was considered to be safe and cost effective. Today’s land application practices are designed to effectively treat Many land-farming systems installed in the mid-1800s wastes, and have evolved from earlier practices that centered on cheap were used for 30 to 50 yr without size adjustment for disposal with less regard for environmental protection. The major growing populations, and resulted in unsightly overobjectives of this paper are to (i) review how current land application loaded conditions (Jewell and Seabrook, 1979). practices, and our understanding of them, have evolved over time and (ii) explore how science is used (and sometimes misused or ignored) in the development of design, regulation, and management of sustainable land application. Land treatment technologies have been used effec-tively for the treatment and recycling of many types of wastewaters and organic residuals for many years. Extensive research and demon-stration efforts, as well as experience with pilot- and field-scale projects, have provided the information about soil reactions with contaminants Land treatment was considered to be the most effective alternative in the United States from 1980 to 1905, and was used by many communities with sewage treatment. Most of the 143 sewage treatment facilities in the United States and Canada as of 1899 were land treatment systems (Rafter, 1899). In some cases (e.g., Calumet, MI;
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.403 | 0.189 |
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".