Bibliographic record
Abstract
In this study, performance of non-woven synthetic fabric (NWF) aided SSF was evaluated in a laboratory scale setup. NWF was selected based on the specifications suggested in literature. Three filters with different thicknesses of fabric on sand beds and one filter without fabric were studied with simulated raw water prepared in laboratory. The results revealed that there was no significant increase in filter run time for the filters with fabric as compared to the one without fabric. However, NWF captured most of the particles, and significantly protected the sand beds from particles deposition. The sand bed protection time was increased linearly with fabric depths. 22.3 mm thickness of selected NWF protected the sand bed for a longer period as compared to 8.9 mm thickness of fabric. Even though NWF showed no significant increase in filter run time, it allowed non sand-bed disturbing filter cleaning operation by protecting the sand bed. The fabric also supported the biogrowth and schmutzdecke development, which contributed to a significant portion (>60%) of total organic carbon (TOC), total coliform and turbidity removal. Removing top one or more fabric layers, after previous filter runs, reduced the time required for filter ripening. Cleaning of fabric by pressurized tap water was convenient and restored the clean bed head loss. (Abstract shortened by UMI.)Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .M66. Source: Masters Abstracts International, Volume: 43-03, page: 0955. Advisers: Nihar Biswas; Rajesh Seth. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".