4. Greywater management in the northeastern Badia of Jordan
Bibliographic record
Abstract
This chapter looks at the feasibility of adopting non-conventional GW management policies for small, rural communities in the north-eastern Badia of Jordan. \nThe north-eastern Badia comprises 33 small clusters (communities), all of which lack public sewerage networks. The most common wastewater collection method is the use of pit latrines and unlined cesspools. About two-thirds of the population separate GW from blackwater, but use the GW for irrigation in an uncontrolled manner and without any treatment. A local stakeholder committee (LSC) formed \nof and including community members and officials was engaged in all project activities, including field visits to wastewater/GW treatment-and-use projects as well as a training workshop on public participation concepts and participatory rapid (or rural) appraisal (PRA) tools and methodologies. Relevant social, economic, and environmental data and information were collected utilizing PRA tools as well as formal surveys. One of the clusters – Rawdat Al-Amir Ali – was appointed as a research site based on specific criteria set by the research team and the LSC. Greywater quality and quantities generated from different fixtures of six households at the research site were investigated during the period March–August 2005. Different cost-effective and technologically-sound alternative treatment options were assessed, taking into consideration potential reuse opportunities. Two different treatment options were considered: 1) septic tank followed by intermittent sand filter; 2) up-flow anaerobic sludge blanket (UASB). Two pilot plants were designed, installed and operated in two households at the research site.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".