Reevaluating onsite wastewater systems: expert recommendations and municipal decision-making
Bibliographic record
Abstract
Onsite wastewater treatment systems (OWTS) serve 20–25% of the households in the USA, and large parts of rural Canada, Australia, and Europe. Urbanization and newer environmental standards are leading many communities that currently rely on OWTS to think of alternatives. We study this decision-making in 19 municipalities across the USA through the unique lens of feasibility reports commissioned by the respective municipalities and authored by engineering/design consulting firms. The reports omitted certain essential information relevant to the decision-making process, and were not of high quality due to a lack of specificity on various parameters. However, the reports evaluated a balanced mix of decentralized and centralized treatment options, and the final recommendations were not biased in any particular direction. Most municipalities failed to take any follow-up action on the report recommendations, calling into question the motive behind commissioning these reports. Although not representative of the entire USA, the small sample of feasibility reports evaluated here is indicative in nature and provided significant insights about the inputs that help municipalities make decisions on complex issues.
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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 | 0.070 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".